GPU: Add dense cuSOLVER solvers (QR, SVD, EigenSolver)

Add QR (geqrf + ormqr + trsm), SVD (gesvd), and self-adjoint eigenvalue
decomposition (syevd) via cuSOLVER. All support host and DeviceMatrix input.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Eigen/GPU

@@ -47,6 +47,9 @@
 #include "src/GPU/DeviceDispatch.h"
 #include "src/GPU/GpuLLT.h"
 #include "src/GPU/GpuLU.h"
+#include "src/GPU/GpuQR.h"
+#include "src/GPU/GpuSVD.h"
+#include "src/GPU/GpuEigenSolver.h"
 // IWYU pragma: end_exports
 #endif
 

Eigen/src/GPU/CuBlasSupport.h

@@ -51,25 +51,69 @@ constexpr cublasOperation_t to_cublas_op(GpuOp op) {
 
 // ---- Scalar → cublasComputeType_t -------------------------------------------
 // cublasGemmEx requires a compute type (separate from the data type).
+//
+// Precision policy:
+//   - Default: tensor core algorithms enabled (CUBLAS_GEMM_DEFAULT_TENSOR_OP).
+//     For double, cuBLAS may use Ozaki emulation on sm_80+ tensor cores.
+//   - EIGEN_CUDA_TF32: opt-in to TF32 for float (~2x faster, 10-bit mantissa).
+//   - EIGEN_NO_CUDA_TENSOR_OPS: disables all tensor core usage. Uses pedantic
+//     compute types and CUBLAS_GEMM_DEFAULT algorithm. For bit-exact reproducibility.
 
 template <typename Scalar>
 struct cuda_compute_type;
 
 template <>
 struct cuda_compute_type<float> {
+#if defined(EIGEN_NO_CUDA_TENSOR_OPS)
+  static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_32F_PEDANTIC;
+#elif defined(EIGEN_CUDA_TF32)
+  static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_32F_FAST_TF32;
+#else
   static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_32F;
+#endif
 };
 template <>
 struct cuda_compute_type<double> {
+#ifdef EIGEN_NO_CUDA_TENSOR_OPS
+  static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_64F_PEDANTIC;
+#else
   static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_64F;
+#endif
 };
 template <>
 struct cuda_compute_type<std::complex<float>> {
+#if defined(EIGEN_NO_CUDA_TENSOR_OPS)
+  static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_32F_PEDANTIC;
+#elif defined(EIGEN_CUDA_TF32)
+  static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_32F_FAST_TF32;
+#else
   static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_32F;
+#endif
 };
 template <>
 struct cuda_compute_type<std::complex<double>> {
+#ifdef EIGEN_NO_CUDA_TENSOR_OPS
+  static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_64F_PEDANTIC;
+#else
   static constexpr cublasComputeType_t value = CUBLAS_COMPUTE_64F;
+#endif
+};
+// ---- GEMM algorithm hint ----------------------------------------------------
+
+constexpr cublasGemmAlgo_t cuda_gemm_algo() {
+#ifdef EIGEN_NO_CUDA_TENSOR_OPS
+  return CUBLAS_GEMM_DEFAULT;
+#else
+  return CUBLAS_GEMM_DEFAULT_TENSOR_OP;
+#endif
+}
+
+// ---- Alpha/beta scalar type for cublasGemmEx --------------------------------
+// For standard types, alpha/beta match the scalar type.
+
+template <typename Scalar>
+struct cuda_gemm_scalar {
+  using type = Scalar;
 };
 
 // ---- Type-specific cuBLAS wrappers ------------------------------------------
@@ -154,6 +198,35 @@ inline cublasStatus_t cublasXsyrk(cublasHandle_t h, cublasFillMode_t uplo, cubla
                      reinterpret_cast<cuDoubleComplex*>(C), ldc);
 }
 
+// GEAM wrappers: C = alpha * op(A) + beta * op(B)
+// Covers transpose, scale, matrix add/subtract in one call.
+inline cublasStatus_t cublasXgeam(cublasHandle_t h, cublasOperation_t transa, cublasOperation_t transb, int m, int n,
+                                  const float* alpha, const float* A, int lda, const float* beta, const float* B,
+                                  int ldb, float* C, int ldc) {
+  return cublasSgeam(h, transa, transb, m, n, alpha, A, lda, beta, B, ldb, C, ldc);
+}
+inline cublasStatus_t cublasXgeam(cublasHandle_t h, cublasOperation_t transa, cublasOperation_t transb, int m, int n,
+                                  const double* alpha, const double* A, int lda, const double* beta, const double* B,
+                                  int ldb, double* C, int ldc) {
+  return cublasDgeam(h, transa, transb, m, n, alpha, A, lda, beta, B, ldb, C, ldc);
+}
+inline cublasStatus_t cublasXgeam(cublasHandle_t h, cublasOperation_t transa, cublasOperation_t transb, int m, int n,
+                                  const std::complex<float>* alpha, const std::complex<float>* A, int lda,
+                                  const std::complex<float>* beta, const std::complex<float>* B, int ldb,
+                                  std::complex<float>* C, int ldc) {
+  return cublasCgeam(h, transa, transb, m, n, reinterpret_cast<const cuComplex*>(alpha),
+                     reinterpret_cast<const cuComplex*>(A), lda, reinterpret_cast<const cuComplex*>(beta),
+                     reinterpret_cast<const cuComplex*>(B), ldb, reinterpret_cast<cuComplex*>(C), ldc);
+}
+inline cublasStatus_t cublasXgeam(cublasHandle_t h, cublasOperation_t transa, cublasOperation_t transb, int m, int n,
+                                  const std::complex<double>* alpha, const std::complex<double>* A, int lda,
+                                  const std::complex<double>* beta, const std::complex<double>* B, int ldb,
+                                  std::complex<double>* C, int ldc) {
+  return cublasZgeam(h, transa, transb, m, n, reinterpret_cast<const cuDoubleComplex*>(alpha),
+                     reinterpret_cast<const cuDoubleComplex*>(A), lda, reinterpret_cast<const cuDoubleComplex*>(beta),
+                     reinterpret_cast<const cuDoubleComplex*>(B), ldb, reinterpret_cast<cuDoubleComplex*>(C), ldc);
+}
+
 }  // namespace internal
 }  // namespace Eigen
 

Eigen/src/GPU/CuSolverSupport.h

@@ -91,6 +91,68 @@ struct cusolver_fill_mode<Upper, RowMajor> {
   static constexpr cublasFillMode_t value = CUBLAS_FILL_MODE_LOWER;
 };
 
+// ---- Type-specific cuSOLVER wrappers ----------------------------------------
+// cuSOLVER does not provide generic X variants for ormqr/unmqr. These overloaded
+// wrappers dispatch to the correct type-specific function.
+// For real types: ormqr (orthogonal Q). For complex types: unmqr (unitary Q).
+
+inline cusolverStatus_t cusolverDnXormqr(cusolverDnHandle_t h, cublasSideMode_t side, cublasOperation_t trans, int m,
+                                         int n, int k, const float* A, int lda, const float* tau, float* C, int ldc,
+                                         float* work, int lwork, int* info) {
+  return cusolverDnSormqr(h, side, trans, m, n, k, A, lda, tau, C, ldc, work, lwork, info);
+}
+inline cusolverStatus_t cusolverDnXormqr(cusolverDnHandle_t h, cublasSideMode_t side, cublasOperation_t trans, int m,
+                                         int n, int k, const double* A, int lda, const double* tau, double* C, int ldc,
+                                         double* work, int lwork, int* info) {
+  return cusolverDnDormqr(h, side, trans, m, n, k, A, lda, tau, C, ldc, work, lwork, info);
+}
+inline cusolverStatus_t cusolverDnXormqr(cusolverDnHandle_t h, cublasSideMode_t side, cublasOperation_t trans, int m,
+                                         int n, int k, const std::complex<float>* A, int lda,
+                                         const std::complex<float>* tau, std::complex<float>* C, int ldc,
+                                         std::complex<float>* work, int lwork, int* info) {
+  return cusolverDnCunmqr(h, side, trans, m, n, k, reinterpret_cast<const cuComplex*>(A), lda,
+                          reinterpret_cast<const cuComplex*>(tau), reinterpret_cast<cuComplex*>(C), ldc,
+                          reinterpret_cast<cuComplex*>(work), lwork, info);
+}
+inline cusolverStatus_t cusolverDnXormqr(cusolverDnHandle_t h, cublasSideMode_t side, cublasOperation_t trans, int m,
+                                         int n, int k, const std::complex<double>* A, int lda,
+                                         const std::complex<double>* tau, std::complex<double>* C, int ldc,
+                                         std::complex<double>* work, int lwork, int* info) {
+  return cusolverDnZunmqr(h, side, trans, m, n, k, reinterpret_cast<const cuDoubleComplex*>(A), lda,
+                          reinterpret_cast<const cuDoubleComplex*>(tau), reinterpret_cast<cuDoubleComplex*>(C), ldc,
+                          reinterpret_cast<cuDoubleComplex*>(work), lwork, info);
+}
+
+// Buffer size wrappers for ormqr/unmqr.
+inline cusolverStatus_t cusolverDnXormqr_bufferSize(cusolverDnHandle_t h, cublasSideMode_t side,
+                                                    cublasOperation_t trans, int m, int n, int k, const float* A,
+                                                    int lda, const float* tau, const float* C, int ldc, int* lwork) {
+  return cusolverDnSormqr_bufferSize(h, side, trans, m, n, k, A, lda, tau, C, ldc, lwork);
+}
+inline cusolverStatus_t cusolverDnXormqr_bufferSize(cusolverDnHandle_t h, cublasSideMode_t side,
+                                                    cublasOperation_t trans, int m, int n, int k, const double* A,
+                                                    int lda, const double* tau, const double* C, int ldc, int* lwork) {
+  return cusolverDnDormqr_bufferSize(h, side, trans, m, n, k, A, lda, tau, C, ldc, lwork);
+}
+inline cusolverStatus_t cusolverDnXormqr_bufferSize(cusolverDnHandle_t h, cublasSideMode_t side,
+                                                    cublasOperation_t trans, int m, int n, int k,
+                                                    const std::complex<float>* A, int lda,
+                                                    const std::complex<float>* tau, const std::complex<float>* C,
+                                                    int ldc, int* lwork) {
+  return cusolverDnCunmqr_bufferSize(h, side, trans, m, n, k, reinterpret_cast<const cuComplex*>(A), lda,
+                                     reinterpret_cast<const cuComplex*>(tau), reinterpret_cast<const cuComplex*>(C),
+                                     ldc, lwork);
+}
+inline cusolverStatus_t cusolverDnXormqr_bufferSize(cusolverDnHandle_t h, cublasSideMode_t side,
+                                                    cublasOperation_t trans, int m, int n, int k,
+                                                    const std::complex<double>* A, int lda,
+                                                    const std::complex<double>* tau, const std::complex<double>* C,
+                                                    int ldc, int* lwork) {
+  return cusolverDnZunmqr_bufferSize(h, side, trans, m, n, k, reinterpret_cast<const cuDoubleComplex*>(A), lda,
+                                     reinterpret_cast<const cuDoubleComplex*>(tau),
+                                     reinterpret_cast<const cuDoubleComplex*>(C), ldc, lwork);
+}
+
 }  // namespace internal
 }  // namespace Eigen
 

Eigen/src/GPU/DeviceDispatch.h

@@ -58,8 +58,8 @@ void dispatch_gemm(
 
   eigen_assert(k == rhs_k && "DeviceMatrix GEMM dimension mismatch");
 
-  const int64_t lda = A.outerStride();
-  const int64_t ldb = B.outerStride();
+  const int64_t lda = A.rows();
+  const int64_t ldb = B.rows();
 
   // Serialize all accesses to the destination buffer on this stream.
   if (!dst.empty()) {
@@ -71,9 +71,13 @@ void dispatch_gemm(
   if (resized) {
     dst.resize(m, n);
   }
-  const int64_t ldc = dst.outerStride();
+  const int64_t ldc = dst.rows();
 
-  Scalar alpha_val = alpha_scale * traits_lhs::alpha(expr.lhs()) * traits_rhs::alpha(expr.rhs());
+  // cuBLAS requires alpha/beta as float for half/bfloat16 inputs.
+  using GemmScalar = typename cuda_gemm_scalar<Scalar>::type;
+  GemmScalar alpha_gval =
+      static_cast<GemmScalar>(alpha_scale * traits_lhs::alpha(expr.lhs()) * traits_rhs::alpha(expr.rhs()));
+  GemmScalar beta_gval = static_cast<GemmScalar>(beta_val);
 
   // Wait for operands to be ready on this stream.
   A.waitReady(ctx.stream());
@@ -81,7 +85,7 @@ void dispatch_gemm(
 
   // If there is no existing valid destination to accumulate into, treat it as
   // zero rather than reading uninitialized memory.
-  if (resized && beta_val != Scalar(0) && dst.sizeInBytes() > 0) {
+  if (resized && beta_gval != GemmScalar(0) && dst.sizeInBytes() > 0) {
     EIGEN_CUDA_RUNTIME_CHECK(cudaMemsetAsync(dst.data(), 0, dst.sizeInBytes(), ctx.stream()));
   }
 
@@ -89,9 +93,9 @@ void dispatch_gemm(
   constexpr cublasComputeType_t compute = cuda_compute_type<Scalar>::value;
 
   EIGEN_CUBLAS_CHECK(cublasGemmEx(ctx.cublasHandle(), transA, transB, static_cast<int>(m), static_cast<int>(n),
-                                  static_cast<int>(k), &alpha_val, A.data(), dtype, static_cast<int>(lda), B.data(),
-                                  dtype, static_cast<int>(ldb), &beta_val, dst.data(), dtype, static_cast<int>(ldc),
-                                  compute, CUBLAS_GEMM_DEFAULT));
+                                  static_cast<int>(k), &alpha_gval, A.data(), dtype, static_cast<int>(lda), B.data(),
+                                  dtype, static_cast<int>(ldb), &beta_gval, dst.data(), dtype, static_cast<int>(ldc),
+                                  compute, cuda_gemm_algo()));
 
   dst.recordReady(ctx.stream());
 }
@@ -125,9 +129,9 @@ void dispatch_llt_solve(GpuContext& ctx, DeviceMatrix<Scalar>& dst, const LltSol
 
   constexpr cudaDataType_t dtype = cuda_data_type<Scalar>::value;
   constexpr cublasFillMode_t uplo = cusolver_fill_mode<UpLo, ColMajor>::value;
-  const int64_t lda = static_cast<int64_t>(A.outerStride());
-  const int64_t ldb = static_cast<int64_t>(B.outerStride());
-  eigen_assert(ldb == static_cast<int64_t>(B.rows()) && "DeviceMatrix must be densely packed");
+  const int64_t lda = static_cast<int64_t>(A.rows());
+  const int64_t ldb = static_cast<int64_t>(B.rows());
+
   const size_t mat_bytes = static_cast<size_t>(lda) * static_cast<size_t>(n) * sizeof(Scalar);
   const size_t rhs_bytes = static_cast<size_t>(ldb) * static_cast<size_t>(nrhs) * sizeof(Scalar);
 
@@ -163,7 +167,7 @@ void dispatch_llt_solve(GpuContext& ctx, DeviceMatrix<Scalar>& dst, const LltSol
   // Solve.
   DeviceBuffer d_solve_info(sizeof(int));
   EIGEN_CUSOLVER_CHECK(cusolverDnXpotrs(ctx.cusolverHandle(), params.p, uplo, static_cast<int64_t>(n), nrhs, dtype,
-                                        d_factor.ptr, lda, dtype, dst.data(), static_cast<int64_t>(dst.outerStride()),
+                                        d_factor.ptr, lda, dtype, dst.data(), static_cast<int64_t>(dst.rows()),
                                         static_cast<int*>(d_solve_info.ptr)));
 
   // Sync to ensure workspace locals can be freed safely.
@@ -201,9 +205,9 @@ void dispatch_lu_solve(GpuContext& ctx, DeviceMatrix<Scalar>& dst, const LuSolve
   if (!dst.empty()) dst.waitReady(ctx.stream());
 
   constexpr cudaDataType_t dtype = cuda_data_type<Scalar>::value;
-  const int64_t lda = static_cast<int64_t>(A.outerStride());
-  const int64_t ldb = static_cast<int64_t>(B.outerStride());
-  eigen_assert(ldb == static_cast<int64_t>(B.rows()) && "DeviceMatrix must be densely packed");
+  const int64_t lda = static_cast<int64_t>(A.rows());
+  const int64_t ldb = static_cast<int64_t>(B.rows());
+
   const size_t mat_bytes = static_cast<size_t>(lda) * static_cast<size_t>(n) * sizeof(Scalar);
   const size_t rhs_bytes = static_cast<size_t>(ldb) * static_cast<size_t>(nrhs) * sizeof(Scalar);
   const size_t ipiv_bytes = static_cast<size_t>(n) * sizeof(int64_t);
@@ -245,7 +249,7 @@ void dispatch_lu_solve(GpuContext& ctx, DeviceMatrix<Scalar>& dst, const LuSolve
   DeviceBuffer d_solve_info(sizeof(int));
   EIGEN_CUSOLVER_CHECK(cusolverDnXgetrs(ctx.cusolverHandle(), params.p, CUBLAS_OP_N, static_cast<int64_t>(n), nrhs,
                                         dtype, d_lu.ptr, lda, static_cast<const int64_t*>(d_ipiv.ptr), dtype,
-                                        dst.data(), static_cast<int64_t>(dst.outerStride()),
+                                        dst.data(), static_cast<int64_t>(dst.rows()),
                                         static_cast<int*>(d_solve_info.ptr)));
 
   // Sync to ensure workspace locals can be freed safely.
@@ -285,15 +289,15 @@ void dispatch_trsm(GpuContext& ctx, DeviceMatrix<Scalar>& dst, const TrsmExpr<Sc
 
   // D2D copy B → dst (trsm is in-place on the RHS).
   dst.resize(n, B.cols());
-  const size_t rhs_bytes = static_cast<size_t>(dst.outerStride()) * static_cast<size_t>(nrhs) * sizeof(Scalar);
+  const size_t rhs_bytes = static_cast<size_t>(dst.rows()) * static_cast<size_t>(nrhs) * sizeof(Scalar);
   EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(dst.data(), B.data(), rhs_bytes, cudaMemcpyDeviceToDevice, ctx.stream()));
 
   constexpr cublasFillMode_t uplo = (UpLo == Lower) ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER;
   Scalar alpha(1);
 
   EIGEN_CUBLAS_CHECK(cublasXtrsm(ctx.cublasHandle(), CUBLAS_SIDE_LEFT, uplo, CUBLAS_OP_N, CUBLAS_DIAG_NON_UNIT, n, nrhs,
-                                 &alpha, A.data(), static_cast<int>(A.outerStride()), dst.data(),
-                                 static_cast<int>(dst.outerStride())));
+                                 &alpha, A.data(), static_cast<int>(A.rows()), dst.data(),
+                                 static_cast<int>(dst.rows())));
 
   dst.recordReady(ctx.stream());
 }
@@ -329,8 +333,8 @@ void dispatch_symm(GpuContext& ctx, DeviceMatrix<Scalar>& dst, const SymmExpr<Sc
   Scalar alpha(1), beta(0);
 
   EIGEN_CUBLAS_CHECK(cublasXsymm(ctx.cublasHandle(), CUBLAS_SIDE_LEFT, uplo, m, n, &alpha, A.data(),
-                                 static_cast<int>(A.outerStride()), B.data(), static_cast<int>(B.outerStride()), &beta,
-                                 dst.data(), static_cast<int>(dst.outerStride())));
+                                 static_cast<int>(A.rows()), B.data(), static_cast<int>(B.rows()), &beta, dst.data(),
+                                 static_cast<int>(dst.rows())));
 
   dst.recordReady(ctx.stream());
 }
@@ -367,8 +371,7 @@ void dispatch_syrk(GpuContext& ctx, DeviceMatrix<Scalar>& dst, const SyrkExpr<Sc
   constexpr cublasFillMode_t uplo = (UpLo == Lower) ? CUBLAS_FILL_MODE_LOWER : CUBLAS_FILL_MODE_UPPER;
 
   EIGEN_CUBLAS_CHECK(cublasXsyrk(ctx.cublasHandle(), uplo, CUBLAS_OP_N, n, k, &alpha_val, A.data(),
-                                 static_cast<int>(A.outerStride()), &beta_val, dst.data(),
-                                 static_cast<int>(dst.outerStride())));
+                                 static_cast<int>(A.rows()), &beta_val, dst.data(), static_cast<int>(dst.rows())));
 
   dst.recordReady(ctx.stream());
 }

Eigen/src/GPU/DeviceMatrix.h

@@ -10,7 +10,7 @@
 // Typed RAII wrapper for a dense matrix in GPU device memory.
 //
 // DeviceMatrix<Scalar> holds a column-major matrix on the GPU with tracked
-// dimensions and leading dimension. It can be passed to GPU solvers
+// dimensions. Always dense (leading dimension = rows). It can be passed to GPU solvers
 // (GpuLLT, GpuLU, future cuBLAS/cuDSS) without host round-trips.
 //
 // Cross-stream safety is automatic: an internal CUDA event tracks when the
@@ -25,7 +25,7 @@
 //   MatrixXd X = d_X.toHost();                       // download + block
 //
 // Async variants:
-//   auto d_A = DeviceMatrix<double>::fromHostAsync(A.data(), n, n, n, stream);
+//   auto d_A = DeviceMatrix<double>::fromHostAsync(A.data(), n, n, stream);
 //   auto transfer = d_X.toHostAsync(stream);         // enqueue D2H
 //   // ... overlap with other work ...
 //   MatrixXd X = transfer.get();                     // block + retrieve
@@ -157,7 +157,8 @@ class HostTransfer {
  *
  * \tparam Scalar_  Element type: float, double, complex<float>, complex<double>
  *
- * Owns a device allocation with tracked dimensions and leading dimension.
+ * Owns a device allocation with tracked dimensions. Always dense
+ * (leading dimension = rows; no stride padding).
  * An internal CUDA event records when the data was last written, enabling
  * safe cross-stream consumption without user-visible synchronization.
  *
@@ -177,7 +178,7 @@ class DeviceMatrix {
   DeviceMatrix() = default;
 
   /** Allocate uninitialized device memory for a rows x cols matrix. */
-  DeviceMatrix(Index rows, Index cols) : rows_(rows), cols_(cols), outerStride_(rows) {
+  DeviceMatrix(Index rows, Index cols) : rows_(rows), cols_(cols) {
     eigen_assert(rows >= 0 && cols >= 0);
     size_t bytes = sizeInBytes();
     if (bytes > 0) {
@@ -196,14 +197,12 @@ class DeviceMatrix {
       : data_(o.data_),
         rows_(o.rows_),
         cols_(o.cols_),
-        outerStride_(o.outerStride_),
         ready_event_(o.ready_event_),
         ready_stream_(o.ready_stream_),
         retained_buffer_(std::move(o.retained_buffer_)) {
     o.data_ = nullptr;
     o.rows_ = 0;
     o.cols_ = 0;
-    o.outerStride_ = 0;
     o.ready_event_ = nullptr;
     o.ready_stream_ = nullptr;
   }
@@ -215,14 +214,12 @@ class DeviceMatrix {
       data_ = o.data_;
       rows_ = o.rows_;
       cols_ = o.cols_;
-      outerStride_ = o.outerStride_;
       ready_event_ = o.ready_event_;
       ready_stream_ = o.ready_stream_;
       retained_buffer_ = std::move(o.retained_buffer_);
       o.data_ = nullptr;
       o.rows_ = 0;
       o.cols_ = 0;
-      o.outerStride_ = 0;
       o.ready_event_ = nullptr;
       o.ready_stream_ = nullptr;
     }
@@ -259,29 +256,17 @@ class DeviceMatrix {
    * The caller must keep \p host_data alive until the transfer completes
    * (check via the internal event or synchronize the stream).
    *
-   * \param host_data    Pointer to contiguous column-major host data.
-   * \param rows         Number of rows.
-   * \param cols         Number of columns.
-   * \param outerStride  Leading dimension (>= rows). Use rows for dense.
-   * \param stream       CUDA stream for the transfer.
+   * \param host_data  Pointer to contiguous column-major host data.
+   * \param rows       Number of rows.
+   * \param cols       Number of columns.
+   * \param stream     CUDA stream for the transfer.
    */
-  static DeviceMatrix fromHostAsync(const Scalar* host_data, Index rows, Index cols, Index outerStride,
-                                    cudaStream_t stream) {
-    eigen_assert(rows >= 0 && cols >= 0 && outerStride >= rows);
+  static DeviceMatrix fromHostAsync(const Scalar* host_data, Index rows, Index cols, cudaStream_t stream) {
+    eigen_assert(rows >= 0 && cols >= 0);
     eigen_assert(host_data != nullptr || (rows == 0 || cols == 0));
     DeviceMatrix dm(rows, cols);
     if (dm.sizeInBytes() > 0) {
-      // If outerStride == rows (dense), single contiguous copy.
-      // Otherwise, copy column by column (strided layout).
-      if (outerStride == rows) {
-        EIGEN_CUDA_RUNTIME_CHECK(
-            cudaMemcpyAsync(dm.data_, host_data, dm.sizeInBytes(), cudaMemcpyHostToDevice, stream));
-      } else {
-        EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy2DAsync(dm.data_, static_cast<size_t>(rows) * sizeof(Scalar), host_data,
-                                                   static_cast<size_t>(outerStride) * sizeof(Scalar),
-                                                   static_cast<size_t>(rows) * sizeof(Scalar),
-                                                   static_cast<size_t>(cols), cudaMemcpyHostToDevice, stream));
-      }
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(dm.data_, host_data, dm.sizeInBytes(), cudaMemcpyHostToDevice, stream));
       dm.recordReady(stream);
     }
     return dm;
@@ -360,7 +345,6 @@ class DeviceMatrix {
     retained_buffer_ = internal::DeviceBuffer();
     rows_ = rows;
     cols_ = cols;
-    outerStride_ = rows;
     size_t bytes = sizeInBytes();
     if (bytes > 0) {
       EIGEN_CUDA_RUNTIME_CHECK(cudaMalloc(reinterpret_cast<void**>(&data_), bytes));
@@ -373,11 +357,10 @@ class DeviceMatrix {
   const Scalar* data() const { return data_; }
   Index rows() const { return rows_; }
   Index cols() const { return cols_; }
-  Index outerStride() const { return outerStride_; }
   bool empty() const { return rows_ == 0 || cols_ == 0; }
 
   /** Size of the device allocation in bytes. */
-  size_t sizeInBytes() const { return static_cast<size_t>(outerStride_) * static_cast<size_t>(cols_) * sizeof(Scalar); }
+  size_t sizeInBytes() const { return static_cast<size_t>(rows_) * static_cast<size_t>(cols_) * sizeof(Scalar); }
 
   // ---- Event synchronization (public for library dispatch interop) ---------
 
@@ -466,8 +449,7 @@ class DeviceMatrix {
  private:
   // ---- Private: adopt a raw device pointer (used by friend solvers) --------
 
-  DeviceMatrix(Scalar* device_ptr, Index rows, Index cols, Index outerStride)
-      : data_(device_ptr), rows_(rows), cols_(cols), outerStride_(outerStride) {}
+  DeviceMatrix(Scalar* device_ptr, Index rows, Index cols) : data_(device_ptr), rows_(rows), cols_(cols) {}
 
   /** Transfer ownership of the device pointer out. Zeros internal state. */
   Scalar* release() {
@@ -475,7 +457,6 @@ class DeviceMatrix {
     data_ = nullptr;
     rows_ = 0;
     cols_ = 0;
-    outerStride_ = 0;
     if (ready_event_) {
       (void)cudaEventDestroy(ready_event_);
       ready_event_ = nullptr;
@@ -500,13 +481,18 @@ class DeviceMatrix {
   friend class GpuLLT;
   template <typename>
   friend class GpuLU;
+  template <typename>
+  friend class GpuQR;
+  template <typename>
+  friend class GpuSVD;
+  template <typename>
+  friend class GpuSelfAdjointEigenSolver;
 
   // ---- Data members --------------------------------------------------------
 
   Scalar* data_ = nullptr;
   Index rows_ = 0;
   Index cols_ = 0;
-  Index outerStride_ = 0;
   cudaEvent_t ready_event_ = nullptr;       // internal: tracks last write completion
   cudaStream_t ready_stream_ = nullptr;     // stream that recorded ready_event_ (for same-stream skip)
   internal::DeviceBuffer retained_buffer_;  // internal: keeps async aux buffers alive

Eigen/src/GPU/GpuEigenSolver.h

@@ -0,0 +1,228 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// GPU self-adjoint eigenvalue decomposition using cuSOLVER.
+//
+// Wraps cusolverDnXsyevd (symmetric/Hermitian divide-and-conquer).
+// Stores eigenvalues and eigenvectors on device.
+//
+// Usage:
+//   GpuSelfAdjointEigenSolver<double> es(A);
+//   VectorXd eigenvals = es.eigenvalues();
+//   MatrixXd eigenvecs = es.eigenvectors();
+
+#ifndef EIGEN_GPU_EIGENSOLVER_H
+#define EIGEN_GPU_EIGENSOLVER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include "./CuSolverSupport.h"
+#include <vector>
+
+namespace Eigen {
+
+template <typename Scalar_>
+class GpuSelfAdjointEigenSolver {
+ public:
+  using Scalar = Scalar_;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  using PlainMatrix = Matrix<Scalar, Dynamic, Dynamic, ColMajor>;
+  using RealVector = Matrix<RealScalar, Dynamic, 1>;
+
+  /** Eigenvalue-only or eigenvalues + eigenvectors. */
+  enum ComputeMode { EigenvaluesOnly, ComputeEigenvectors };
+
+  GpuSelfAdjointEigenSolver() { init_context(); }
+
+  template <typename InputType>
+  explicit GpuSelfAdjointEigenSolver(const EigenBase<InputType>& A, ComputeMode mode = ComputeEigenvectors) {
+    init_context();
+    compute(A, mode);
+  }
+
+  ~GpuSelfAdjointEigenSolver() {
+    if (handle_) (void)cusolverDnDestroy(handle_);
+    if (stream_) (void)cudaStreamDestroy(stream_);
+  }
+
+  GpuSelfAdjointEigenSolver(const GpuSelfAdjointEigenSolver&) = delete;
+  GpuSelfAdjointEigenSolver& operator=(const GpuSelfAdjointEigenSolver&) = delete;
+
+  // ---- Factorization -------------------------------------------------------
+
+  template <typename InputType>
+  GpuSelfAdjointEigenSolver& compute(const EigenBase<InputType>& A, ComputeMode mode = ComputeEigenvectors) {
+    eigen_assert(A.rows() == A.cols() && "GpuSelfAdjointEigenSolver requires a square matrix");
+    mode_ = mode;
+    n_ = A.rows();
+    info_ = InvalidInput;
+    info_synced_ = false;
+
+    if (n_ == 0) {
+      info_ = Success;
+      info_synced_ = true;
+      return *this;
+    }
+
+    const PlainMatrix mat(A.derived());
+    lda_ = static_cast<int64_t>(n_);
+    const size_t mat_bytes = static_cast<size_t>(lda_) * static_cast<size_t>(n_) * sizeof(Scalar);
+
+    // syevd overwrites A with eigenvectors (if requested).
+    d_A_ = internal::DeviceBuffer(mat_bytes);
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_A_.ptr, mat.data(), mat_bytes, cudaMemcpyHostToDevice, stream_));
+    factorize();
+    return *this;
+  }
+
+  GpuSelfAdjointEigenSolver& compute(const DeviceMatrix<Scalar>& d_A, ComputeMode mode = ComputeEigenvectors) {
+    eigen_assert(d_A.rows() == d_A.cols() && "GpuSelfAdjointEigenSolver requires a square matrix");
+    mode_ = mode;
+    n_ = d_A.rows();
+    info_ = InvalidInput;
+    info_synced_ = false;
+
+    if (n_ == 0) {
+      info_ = Success;
+      info_synced_ = true;
+      return *this;
+    }
+
+    d_A.waitReady(stream_);
+    lda_ = static_cast<int64_t>(n_);
+    const size_t mat_bytes = static_cast<size_t>(lda_) * static_cast<size_t>(n_) * sizeof(Scalar);
+
+    d_A_ = internal::DeviceBuffer(mat_bytes);
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_A_.ptr, d_A.data(), mat_bytes, cudaMemcpyDeviceToDevice, stream_));
+
+    factorize();
+    return *this;
+  }
+
+  // ---- Accessors -----------------------------------------------------------
+
+  ComputationInfo info() const {
+    sync_info();
+    return info_;
+  }
+
+  Index cols() const { return n_; }
+  Index rows() const { return n_; }
+
+  /** Eigenvalues in ascending order. Downloads from device. */
+  RealVector eigenvalues() const {
+    sync_info();
+    eigen_assert(info_ == Success);
+    RealVector W(n_);
+    if (n_ > 0) {
+      EIGEN_CUDA_RUNTIME_CHECK(
+          cudaMemcpy(W.data(), d_W_.ptr, static_cast<size_t>(n_) * sizeof(RealScalar), cudaMemcpyDeviceToHost));
+    }
+    return W;
+  }
+
+  /** Eigenvectors (columns). Downloads from device.
+   * Requires ComputeEigenvectors mode. */
+  PlainMatrix eigenvectors() const {
+    sync_info();
+    eigen_assert(info_ == Success);
+    eigen_assert(mode_ == ComputeEigenvectors && "eigenvectors() requires ComputeEigenvectors mode");
+    PlainMatrix V(n_, n_);
+    if (n_ > 0) {
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy(V.data(), d_A_.ptr,
+                                          static_cast<size_t>(lda_) * static_cast<size_t>(n_) * sizeof(Scalar),
+                                          cudaMemcpyDeviceToHost));
+    }
+    return V;
+  }
+
+  cudaStream_t stream() const { return stream_; }
+
+ private:
+  cudaStream_t stream_ = nullptr;
+  cusolverDnHandle_t handle_ = nullptr;
+  internal::CusolverParams params_;
+  internal::DeviceBuffer d_A_;        // overwritten with eigenvectors by syevd
+  internal::DeviceBuffer d_W_;        // eigenvalues (RealScalar, length n)
+  internal::DeviceBuffer d_scratch_;  // workspace + info
+  size_t scratch_size_ = 0;
+  std::vector<char> h_workspace_;
+  ComputeMode mode_ = ComputeEigenvectors;
+  Index n_ = 0;
+  int64_t lda_ = 0;
+  ComputationInfo info_ = InvalidInput;
+  int info_word_ = 0;
+  bool info_synced_ = true;
+
+  void init_context() {
+    EIGEN_CUDA_RUNTIME_CHECK(cudaStreamCreate(&stream_));
+    EIGEN_CUSOLVER_CHECK(cusolverDnCreate(&handle_));
+    EIGEN_CUSOLVER_CHECK(cusolverDnSetStream(handle_, stream_));
+    ensure_scratch(0);
+  }
+
+  void ensure_scratch(size_t workspace_bytes) {
+    constexpr size_t kAlign = 16;
+    workspace_bytes = (workspace_bytes + kAlign - 1) & ~(kAlign - 1);
+    size_t needed = workspace_bytes + sizeof(int);
+    if (needed > scratch_size_) {
+      if (d_scratch_.ptr) EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+      d_scratch_ = internal::DeviceBuffer(needed);
+      scratch_size_ = needed;
+    }
+  }
+
+  void* scratch_workspace() const { return d_scratch_.ptr; }
+  int* scratch_info() const {
+    return reinterpret_cast<int*>(static_cast<char*>(d_scratch_.ptr) + scratch_size_ - sizeof(int));
+  }
+
+  void sync_info() const {
+    if (!info_synced_) {
+      EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+      const_cast<GpuSelfAdjointEigenSolver*>(this)->info_ = (info_word_ == 0) ? Success : NumericalIssue;
+      const_cast<GpuSelfAdjointEigenSolver*>(this)->info_synced_ = true;
+    }
+  }
+
+  void factorize() {
+    constexpr cudaDataType_t dtype = internal::cusolver_data_type<Scalar>::value;
+    constexpr cudaDataType_t rtype = internal::cuda_data_type<RealScalar>::value;
+
+    info_synced_ = false;
+    info_ = InvalidInput;
+
+    d_W_ = internal::DeviceBuffer(static_cast<size_t>(n_) * sizeof(RealScalar));
+
+    const cusolverEigMode_t jobz =
+        (mode_ == ComputeEigenvectors) ? CUSOLVER_EIG_MODE_VECTOR : CUSOLVER_EIG_MODE_NOVECTOR;
+
+    // Use lower triangle (standard convention).
+    constexpr cublasFillMode_t uplo = CUBLAS_FILL_MODE_LOWER;
+
+    size_t dev_ws = 0, host_ws = 0;
+    EIGEN_CUSOLVER_CHECK(cusolverDnXsyevd_bufferSize(handle_, params_.p, jobz, uplo, static_cast<int64_t>(n_), dtype,
+                                                     d_A_.ptr, lda_, rtype, d_W_.ptr, dtype, &dev_ws, &host_ws));
+
+    ensure_scratch(dev_ws);
+    h_workspace_.resize(host_ws);
+
+    EIGEN_CUSOLVER_CHECK(cusolverDnXsyevd(handle_, params_.p, jobz, uplo, static_cast<int64_t>(n_), dtype, d_A_.ptr,
+                                          lda_, rtype, d_W_.ptr, dtype, scratch_workspace(), dev_ws,
+                                          host_ws > 0 ? h_workspace_.data() : nullptr, host_ws, scratch_info()));
+
+    EIGEN_CUDA_RUNTIME_CHECK(
+        cudaMemcpyAsync(&info_word_, scratch_info(), sizeof(int), cudaMemcpyDeviceToHost, stream_));
+  }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_GPU_EIGENSOLVER_H

Eigen/src/GPU/GpuLLT.h

@@ -149,7 +149,7 @@ class GpuLLT {
 
     // Evaluate A into a contiguous ColMajor matrix (handles arbitrary expressions).
     const PlainMatrix mat(A.derived());
-    lda_ = static_cast<int64_t>(mat.outerStride());
+    lda_ = static_cast<int64_t>(mat.rows());
     allocate_factor_storage();
     EIGEN_CUDA_RUNTIME_CHECK(
         cudaMemcpyAsync(d_factor_.ptr, mat.data(), factorBytes(), cudaMemcpyHostToDevice, stream_));
@@ -163,7 +163,7 @@ class GpuLLT {
     eigen_assert(d_A.rows() == d_A.cols());
     if (!begin_compute(d_A.rows())) return *this;
 
-    lda_ = static_cast<int64_t>(d_A.outerStride());
+    lda_ = static_cast<int64_t>(d_A.rows());
     d_A.waitReady(stream_);
     allocate_factor_storage();
     EIGEN_CUDA_RUNTIME_CHECK(
@@ -178,7 +178,7 @@ class GpuLLT {
     eigen_assert(d_A.rows() == d_A.cols());
     if (!begin_compute(d_A.rows())) return *this;
 
-    lda_ = static_cast<int64_t>(d_A.outerStride());
+    lda_ = static_cast<int64_t>(d_A.rows());
     d_A.waitReady(stream_);
     d_factor_ = internal::DeviceBuffer::adopt(static_cast<void*>(d_A.release()));
 
@@ -205,7 +205,7 @@ class GpuLLT {
 
     const PlainMatrix rhs(B);
     const int64_t nrhs = static_cast<int64_t>(rhs.cols());
-    const int64_t ldb = static_cast<int64_t>(rhs.outerStride());
+    const int64_t ldb = static_cast<int64_t>(rhs.rows());
     DeviceMatrix<Scalar> d_X = solve_impl(nrhs, ldb, [&](Scalar* d_x_ptr) {
       EIGEN_CUDA_RUNTIME_CHECK(
           cudaMemcpyAsync(d_x_ptr, rhs.data(), rhsBytes(nrhs, ldb), cudaMemcpyHostToDevice, stream_));
@@ -234,7 +234,7 @@ class GpuLLT {
     eigen_assert(d_B.rows() == n_);
     d_B.waitReady(stream_);
     const int64_t nrhs = static_cast<int64_t>(d_B.cols());
-    const int64_t ldb = static_cast<int64_t>(d_B.outerStride());
+    const int64_t ldb = static_cast<int64_t>(d_B.rows());
     return solve_impl(nrhs, ldb, [&](Scalar* d_x_ptr) {
       EIGEN_CUDA_RUNTIME_CHECK(
           cudaMemcpyAsync(d_x_ptr, d_B.data(), rhsBytes(nrhs, ldb), cudaMemcpyDeviceToDevice, stream_));
@@ -332,7 +332,7 @@ class GpuLLT {
     EIGEN_CUSOLVER_CHECK(cusolverDnXpotrs(handle_, params_.p, uplo, static_cast<int64_t>(n_), nrhs, dtype,
                                           d_factor_.ptr, lda_, dtype, d_x_ptr, ldb, scratch_info()));
 
-    DeviceMatrix<Scalar> result(d_x_ptr, n_, static_cast<Index>(nrhs), static_cast<Index>(ldb));
+    DeviceMatrix<Scalar> result(d_x_ptr, n_, static_cast<Index>(nrhs));
     result.recordReady(stream_);
     return result;
   }

Eigen/src/GPU/GpuLU.h

@@ -140,7 +140,7 @@ class GpuLU {
     if (!begin_compute(A.rows())) return *this;
 
     const PlainMatrix mat(A.derived());
-    lda_ = static_cast<int64_t>(mat.outerStride());
+    lda_ = static_cast<int64_t>(mat.rows());
     allocate_lu_storage();
     EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_lu_.ptr, mat.data(), matrixBytes(), cudaMemcpyHostToDevice, stream_));
 
@@ -153,7 +153,7 @@ class GpuLU {
     eigen_assert(d_A.rows() == d_A.cols() && "GpuLU requires a square matrix");
     if (!begin_compute(d_A.rows())) return *this;
 
-    lda_ = static_cast<int64_t>(d_A.outerStride());
+    lda_ = static_cast<int64_t>(d_A.rows());
     d_A.waitReady(stream_);
     allocate_lu_storage();
     EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_lu_.ptr, d_A.data(), matrixBytes(), cudaMemcpyDeviceToDevice, stream_));
@@ -167,7 +167,7 @@ class GpuLU {
     eigen_assert(d_A.rows() == d_A.cols() && "GpuLU requires a square matrix");
     if (!begin_compute(d_A.rows())) return *this;
 
-    lda_ = static_cast<int64_t>(d_A.outerStride());
+    lda_ = static_cast<int64_t>(d_A.rows());
     d_A.waitReady(stream_);
     d_lu_ = internal::DeviceBuffer::adopt(static_cast<void*>(d_A.release()));
 
@@ -190,7 +190,7 @@ class GpuLU {
 
     const PlainMatrix rhs(B);
     const int64_t nrhs = static_cast<int64_t>(rhs.cols());
-    const int64_t ldb = static_cast<int64_t>(rhs.outerStride());
+    const int64_t ldb = static_cast<int64_t>(rhs.rows());
     DeviceMatrix<Scalar> d_X = solve_impl(nrhs, ldb, mode, [&](Scalar* d_x_ptr) {
       EIGEN_CUDA_RUNTIME_CHECK(
           cudaMemcpyAsync(d_x_ptr, rhs.data(), matrixBytes(nrhs, ldb), cudaMemcpyHostToDevice, stream_));
@@ -213,7 +213,7 @@ class GpuLU {
     eigen_assert(d_B.rows() == n_);
     d_B.waitReady(stream_);
     const int64_t nrhs = static_cast<int64_t>(d_B.cols());
-    const int64_t ldb = static_cast<int64_t>(d_B.outerStride());
+    const int64_t ldb = static_cast<int64_t>(d_B.rows());
     return solve_impl(nrhs, ldb, mode, [&](Scalar* d_x_ptr) {
       EIGEN_CUDA_RUNTIME_CHECK(
           cudaMemcpyAsync(d_x_ptr, d_B.data(), matrixBytes(nrhs, ldb), cudaMemcpyDeviceToDevice, stream_));
@@ -305,7 +305,7 @@ class GpuLU {
                                           lda_, static_cast<const int64_t*>(d_ipiv_.ptr), dtype, d_x_ptr, ldb,
                                           scratch_info()));
 
-    DeviceMatrix<Scalar> result(d_x_ptr, n_, static_cast<Index>(nrhs), static_cast<Index>(ldb));
+    DeviceMatrix<Scalar> result(d_x_ptr, n_, static_cast<Index>(nrhs));
     result.recordReady(stream_);
     return result;
   }

Eigen/src/GPU/GpuQR.h

@@ -0,0 +1,386 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// GPU QR decomposition using cuSOLVER.
+//
+// Wraps cusolverDnXgeqrf (factorization), cusolverDnXormqr (apply Q),
+// cusolverDnXorgqr (form Q), and cublasXtrsm (triangular solve on R).
+//
+// The factored matrix (reflectors + R) and tau stay in device memory.
+// Solve uses ormqr + trsm without forming Q explicitly.
+//
+// Usage:
+//   GpuQR<double> qr(A);              // upload A, geqrf
+//   if (qr.info() != Success) { ... }
+//   MatrixXd X = qr.solve(B);         // Q^H * B via ormqr, then trsm on R
+//
+// Expression syntax:
+//   d_X = d_A.qr().solve(d_B);        // temporary, no caching
+
+#ifndef EIGEN_GPU_QR_H
+#define EIGEN_GPU_QR_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include "./CuSolverSupport.h"
+#include "./CuBlasSupport.h"
+#include <vector>
+
+namespace Eigen {
+
+template <typename Scalar_>
+class GpuQR {
+ public:
+  using Scalar = Scalar_;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  using PlainMatrix = Matrix<Scalar, Dynamic, Dynamic, ColMajor>;
+
+  GpuQR() { init_context(); }
+
+  template <typename InputType>
+  explicit GpuQR(const EigenBase<InputType>& A) {
+    init_context();
+    compute(A);
+  }
+
+  ~GpuQR() {
+    if (handle_) (void)cusolverDnDestroy(handle_);
+    if (cublas_) (void)cublasDestroy(cublas_);
+    if (stream_) (void)cudaStreamDestroy(stream_);
+  }
+
+  GpuQR(const GpuQR&) = delete;
+  GpuQR& operator=(const GpuQR&) = delete;
+
+  GpuQR(GpuQR&& o) noexcept
+      : stream_(o.stream_),
+        handle_(o.handle_),
+        cublas_(o.cublas_),
+        params_(std::move(o.params_)),
+        d_qr_(std::move(o.d_qr_)),
+        d_tau_(std::move(o.d_tau_)),
+        d_scratch_(std::move(o.d_scratch_)),
+        scratch_size_(o.scratch_size_),
+        h_workspace_(std::move(o.h_workspace_)),
+        m_(o.m_),
+        n_(o.n_),
+        lda_(o.lda_),
+        info_(o.info_),
+        info_word_(o.info_word_),
+        info_synced_(o.info_synced_) {
+    o.stream_ = nullptr;
+    o.handle_ = nullptr;
+    o.cublas_ = nullptr;
+    o.scratch_size_ = 0;
+    o.m_ = 0;
+    o.n_ = 0;
+    o.lda_ = 0;
+    o.info_ = InvalidInput;
+    o.info_word_ = 0;
+    o.info_synced_ = true;
+  }
+
+  GpuQR& operator=(GpuQR&& o) noexcept {
+    if (this != &o) {
+      if (handle_) (void)cusolverDnDestroy(handle_);
+      if (cublas_) (void)cublasDestroy(cublas_);
+      if (stream_) (void)cudaStreamDestroy(stream_);
+      stream_ = o.stream_;
+      handle_ = o.handle_;
+      cublas_ = o.cublas_;
+      params_ = std::move(o.params_);
+      d_qr_ = std::move(o.d_qr_);
+      d_tau_ = std::move(o.d_tau_);
+      d_scratch_ = std::move(o.d_scratch_);
+      scratch_size_ = o.scratch_size_;
+      h_workspace_ = std::move(o.h_workspace_);
+      m_ = o.m_;
+      n_ = o.n_;
+      lda_ = o.lda_;
+      info_ = o.info_;
+      info_word_ = o.info_word_;
+      info_synced_ = o.info_synced_;
+      o.stream_ = nullptr;
+      o.handle_ = nullptr;
+      o.cublas_ = nullptr;
+      o.scratch_size_ = 0;
+      o.m_ = 0;
+      o.n_ = 0;
+      o.lda_ = 0;
+      o.info_ = InvalidInput;
+      o.info_word_ = 0;
+      o.info_synced_ = true;
+    }
+    return *this;
+  }
+
+  // ---- Factorization -------------------------------------------------------
+
+  template <typename InputType>
+  GpuQR& compute(const EigenBase<InputType>& A) {
+    m_ = A.rows();
+    n_ = A.cols();
+    info_ = InvalidInput;
+    info_synced_ = false;
+
+    if (m_ == 0 || n_ == 0) {
+      info_ = Success;
+      info_synced_ = true;
+      return *this;
+    }
+
+    const PlainMatrix mat(A.derived());
+    lda_ = static_cast<int64_t>(mat.rows());
+    const size_t mat_bytes = static_cast<size_t>(lda_) * static_cast<size_t>(n_) * sizeof(Scalar);
+    const size_t tau_bytes = static_cast<size_t>((std::min)(m_, n_)) * sizeof(Scalar);
+
+    d_qr_ = internal::DeviceBuffer(mat_bytes);
+    d_tau_ = internal::DeviceBuffer(tau_bytes);
+
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_qr_.ptr, mat.data(), mat_bytes, cudaMemcpyHostToDevice, stream_));
+
+    factorize();
+    return *this;
+  }
+
+  GpuQR& compute(const DeviceMatrix<Scalar>& d_A) {
+    m_ = d_A.rows();
+    n_ = d_A.cols();
+    info_ = InvalidInput;
+    info_synced_ = false;
+
+    if (m_ == 0 || n_ == 0) {
+      info_ = Success;
+      info_synced_ = true;
+      return *this;
+    }
+
+    lda_ = static_cast<int64_t>(d_A.rows());
+    const size_t mat_bytes = static_cast<size_t>(lda_) * static_cast<size_t>(n_) * sizeof(Scalar);
+    const size_t tau_bytes = static_cast<size_t>((std::min)(m_, n_)) * sizeof(Scalar);
+
+    d_A.waitReady(stream_);
+    d_qr_ = internal::DeviceBuffer(mat_bytes);
+    d_tau_ = internal::DeviceBuffer(tau_bytes);
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_qr_.ptr, d_A.data(), mat_bytes, cudaMemcpyDeviceToDevice, stream_));
+
+    factorize();
+    return *this;
+  }
+
+  // ---- Solve ---------------------------------------------------------------
+
+  /** Solve A * X = B via QR: X = R^{-1} * Q^H * B (least-squares for m >= n).
+   * Uses ormqr (apply Q^H) + trsm (solve R), without forming Q explicitly.
+   * Requires m >= n (overdetermined or square). Underdetermined not supported. */
+  template <typename Rhs>
+  PlainMatrix solve(const MatrixBase<Rhs>& B) const {
+    sync_info();
+    eigen_assert(info_ == Success && "GpuQR::solve called on a failed or uninitialized factorization");
+    eigen_assert(B.rows() == m_);
+    eigen_assert(m_ >= n_ && "GpuQR::solve requires m >= n (use SVD for underdetermined systems)");
+
+    const PlainMatrix rhs(B);
+    const int64_t nrhs = static_cast<int64_t>(rhs.cols());
+    const int64_t ldb = static_cast<int64_t>(rhs.rows());  // = m_
+    const size_t b_bytes = static_cast<size_t>(ldb) * static_cast<size_t>(nrhs) * sizeof(Scalar);
+
+    // Upload B to device (m × nrhs buffer).
+    internal::DeviceBuffer d_B(b_bytes);
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_B.ptr, rhs.data(), b_bytes, cudaMemcpyHostToDevice, stream_));
+
+    // Apply Q^H to B in-place: d_B becomes m × nrhs, first n rows hold Q^H * B relevant part.
+    apply_QH(d_B.ptr, ldb, nrhs);
+
+    // Solve R * X = (Q^H * B)[0:n,:] via trsm on the first n rows.
+    Scalar alpha(1);
+    EIGEN_CUBLAS_CHECK(internal::cublasXtrsm(cublas_, CUBLAS_SIDE_LEFT, CUBLAS_FILL_MODE_UPPER, CUBLAS_OP_N,
+                                             CUBLAS_DIAG_NON_UNIT, static_cast<int>(n_), static_cast<int>(nrhs), &alpha,
+                                             static_cast<const Scalar*>(d_qr_.ptr), static_cast<int>(lda_),
+                                             static_cast<Scalar*>(d_B.ptr), static_cast<int>(ldb)));
+
+    // Download the first n rows of each column (stride = ldb = m, width = n).
+    PlainMatrix X(n_, rhs.cols());
+    if (m_ == n_) {
+      // Square: dense copy, no stride mismatch.
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(X.data(), d_B.ptr,
+                                               static_cast<size_t>(n_) * static_cast<size_t>(nrhs) * sizeof(Scalar),
+                                               cudaMemcpyDeviceToHost, stream_));
+    } else {
+      // Overdetermined: 2D copy to extract first n rows from each column.
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy2DAsync(
+          X.data(), static_cast<size_t>(n_) * sizeof(Scalar), d_B.ptr, static_cast<size_t>(ldb) * sizeof(Scalar),
+          static_cast<size_t>(n_) * sizeof(Scalar), static_cast<size_t>(nrhs), cudaMemcpyDeviceToHost, stream_));
+    }
+    EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+    return X;
+  }
+
+  /** Solve with device-resident RHS. Returns n × nrhs DeviceMatrix. */
+  DeviceMatrix<Scalar> solve(const DeviceMatrix<Scalar>& d_B) const {
+    sync_info();
+    eigen_assert(info_ == Success && "GpuQR::solve called on a failed or uninitialized factorization");
+    eigen_assert(d_B.rows() == m_);
+    eigen_assert(m_ >= n_ && "GpuQR::solve requires m >= n (use SVD for underdetermined systems)");
+    d_B.waitReady(stream_);
+
+    const int64_t nrhs = static_cast<int64_t>(d_B.cols());
+    const int64_t ldb = static_cast<int64_t>(d_B.rows());  // = m_
+    const size_t b_bytes = static_cast<size_t>(ldb) * static_cast<size_t>(nrhs) * sizeof(Scalar);
+
+    // D2D copy B into working buffer (ormqr and trsm are in-place).
+    internal::DeviceBuffer d_work(b_bytes);
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_work.ptr, d_B.data(), b_bytes, cudaMemcpyDeviceToDevice, stream_));
+
+    apply_QH(d_work.ptr, ldb, nrhs);
+
+    // trsm on the first n rows.
+    Scalar alpha(1);
+    EIGEN_CUBLAS_CHECK(internal::cublasXtrsm(cublas_, CUBLAS_SIDE_LEFT, CUBLAS_FILL_MODE_UPPER, CUBLAS_OP_N,
+                                             CUBLAS_DIAG_NON_UNIT, static_cast<int>(n_), static_cast<int>(nrhs), &alpha,
+                                             static_cast<const Scalar*>(d_qr_.ptr), static_cast<int>(lda_),
+                                             static_cast<Scalar*>(d_work.ptr), static_cast<int>(ldb)));
+
+    if (m_ == n_) {
+      // Square: result is the whole buffer, dense.
+      DeviceMatrix<Scalar> result(static_cast<Scalar*>(d_work.ptr), n_, static_cast<Index>(nrhs));
+      d_work.ptr = nullptr;  // transfer ownership
+      result.recordReady(stream_);
+      return result;
+    } else {
+      // Overdetermined: copy first n rows of each column into a dense n × nrhs result.
+      DeviceMatrix<Scalar> result(n_, static_cast<Index>(nrhs));
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy2DAsync(result.data(), static_cast<size_t>(n_) * sizeof(Scalar), d_work.ptr,
+                                                 static_cast<size_t>(ldb) * sizeof(Scalar),
+                                                 static_cast<size_t>(n_) * sizeof(Scalar), static_cast<size_t>(nrhs),
+                                                 cudaMemcpyDeviceToDevice, stream_));
+      result.recordReady(stream_);
+      return result;
+      // d_work freed here via RAII — safe because stream is ordered.
+    }
+  }
+
+  // ---- Accessors -----------------------------------------------------------
+
+  ComputationInfo info() const {
+    sync_info();
+    return info_;
+  }
+
+  Index rows() const { return m_; }
+  Index cols() const { return n_; }
+  cudaStream_t stream() const { return stream_; }
+
+ private:
+  cudaStream_t stream_ = nullptr;
+  cusolverDnHandle_t handle_ = nullptr;
+  cublasHandle_t cublas_ = nullptr;
+  internal::CusolverParams params_;
+  internal::DeviceBuffer d_qr_;       // QR factors (reflectors in lower, R in upper)
+  internal::DeviceBuffer d_tau_;      // Householder scalars (min(m,n))
+  internal::DeviceBuffer d_scratch_;  // workspace + info word
+  size_t scratch_size_ = 0;
+  std::vector<char> h_workspace_;
+  Index m_ = 0;
+  Index n_ = 0;
+  int64_t lda_ = 0;
+  ComputationInfo info_ = InvalidInput;
+  int info_word_ = 0;
+  bool info_synced_ = true;
+
+  void init_context() {
+    EIGEN_CUDA_RUNTIME_CHECK(cudaStreamCreate(&stream_));
+    EIGEN_CUSOLVER_CHECK(cusolverDnCreate(&handle_));
+    EIGEN_CUSOLVER_CHECK(cusolverDnSetStream(handle_, stream_));
+    EIGEN_CUBLAS_CHECK(cublasCreate(&cublas_));
+    EIGEN_CUBLAS_CHECK(cublasSetStream(cublas_, stream_));
+    ensure_scratch(0);
+  }
+
+  void ensure_scratch(size_t workspace_bytes) {
+    constexpr size_t kAlign = 16;
+    workspace_bytes = (workspace_bytes + kAlign - 1) & ~(kAlign - 1);
+    size_t needed = workspace_bytes + sizeof(int);
+    if (needed > scratch_size_) {
+      if (d_scratch_.ptr) EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+      d_scratch_ = internal::DeviceBuffer(needed);
+      scratch_size_ = needed;
+    }
+  }
+
+  void* scratch_workspace() const { return d_scratch_.ptr; }
+  int* scratch_info() const {
+    return reinterpret_cast<int*>(static_cast<char*>(d_scratch_.ptr) + scratch_size_ - sizeof(int));
+  }
+
+  void sync_info() const {
+    if (!info_synced_) {
+      EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+      const_cast<GpuQR*>(this)->info_ = (info_word_ == 0) ? Success : NumericalIssue;
+      const_cast<GpuQR*>(this)->info_synced_ = true;
+    }
+  }
+
+  void factorize() {
+    constexpr cudaDataType_t dtype = internal::cusolver_data_type<Scalar>::value;
+
+    info_synced_ = false;
+    info_ = InvalidInput;
+
+    size_t dev_ws = 0, host_ws = 0;
+    EIGEN_CUSOLVER_CHECK(cusolverDnXgeqrf_bufferSize(handle_, params_.p, static_cast<int64_t>(m_),
+                                                     static_cast<int64_t>(n_), dtype, d_qr_.ptr, lda_, dtype,
+                                                     d_tau_.ptr, dtype, &dev_ws, &host_ws));
+
+    ensure_scratch(dev_ws);
+    h_workspace_.resize(host_ws);
+
+    EIGEN_CUSOLVER_CHECK(cusolverDnXgeqrf(handle_, params_.p, static_cast<int64_t>(m_), static_cast<int64_t>(n_), dtype,
+                                          d_qr_.ptr, lda_, dtype, d_tau_.ptr, dtype, scratch_workspace(), dev_ws,
+                                          host_ws > 0 ? h_workspace_.data() : nullptr, host_ws, scratch_info()));
+
+    EIGEN_CUDA_RUNTIME_CHECK(
+        cudaMemcpyAsync(&info_word_, scratch_info(), sizeof(int), cudaMemcpyDeviceToHost, stream_));
+  }
+
+  // Apply Q^H to a device buffer in-place: d_B = Q^H * d_B.
+  // Uses type-specific ormqr (real) or unmqr (complex) wrappers from CuSolverSupport.h.
+  // For real types: Q^H = Q^T, use CUBLAS_OP_T. For complex: use CUBLAS_OP_C.
+  void apply_QH(void* d_B, int64_t ldb, int64_t nrhs) const {
+    const int im = static_cast<int>(m_);
+    const int in = static_cast<int>(nrhs);
+    const int ik = static_cast<int>((std::min)(m_, n_));
+    const int ilda = static_cast<int>(lda_);
+    const int ildb = static_cast<int>(ldb);
+    constexpr cublasOperation_t trans = NumTraits<Scalar>::IsComplex ? CUBLAS_OP_C : CUBLAS_OP_T;
+
+    int lwork = 0;
+    EIGEN_CUSOLVER_CHECK(internal::cusolverDnXormqr_bufferSize(
+        handle_, CUBLAS_SIDE_LEFT, trans, im, in, ik, static_cast<const Scalar*>(d_qr_.ptr), ilda,
+        static_cast<const Scalar*>(d_tau_.ptr), static_cast<const Scalar*>(d_B), ildb, &lwork));
+
+    internal::DeviceBuffer d_work(static_cast<size_t>(lwork) * sizeof(Scalar));
+
+    EIGEN_CUSOLVER_CHECK(internal::cusolverDnXormqr(handle_, CUBLAS_SIDE_LEFT, trans, im, in, ik,
+                                                    static_cast<const Scalar*>(d_qr_.ptr), ilda,
+                                                    static_cast<const Scalar*>(d_tau_.ptr), static_cast<Scalar*>(d_B),
+                                                    ildb, static_cast<Scalar*>(d_work.ptr), lwork, scratch_info()));
+
+    // Sync to ensure workspace can be freed safely, and check ormqr info.
+    EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+    int ormqr_info = 0;
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy(&ormqr_info, scratch_info(), sizeof(int), cudaMemcpyDeviceToHost));
+    eigen_assert(ormqr_info == 0 && "cusolverDnXormqr reported an error");
+  }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_GPU_QR_H

Eigen/src/GPU/GpuSVD.h

@@ -0,0 +1,486 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// GPU SVD decomposition using cuSOLVER (divide-and-conquer).
+//
+// Wraps cusolverDnXgesvd. Stores U, S, VT on device. Solve uses
+// cuBLAS GEMM: X = VT^H * diag(D) * U^H * B.
+//
+// cuSOLVER returns VT (not V). We store and expose VT directly.
+//
+// Usage:
+//   GpuSVD<double> svd(A, ComputeThinU | ComputeThinV);
+//   VectorXd S = svd.singularValues();
+//   MatrixXd U = svd.matrixU();       // m×k or m×m
+//   MatrixXd VT = svd.matrixVT();      // k×n or n×n (this is V^T)
+//   MatrixXd X = svd.solve(B);        // pseudoinverse
+//   MatrixXd X = svd.solve(B, k);     // truncated (top k triplets)
+//   MatrixXd X = svd.solve(B, 0.1);   // Tikhonov regularized
+
+#ifndef EIGEN_GPU_SVD_H
+#define EIGEN_GPU_SVD_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+#include "./CuSolverSupport.h"
+#include "./CuBlasSupport.h"
+#include <vector>
+
+namespace Eigen {
+
+template <typename Scalar_>
+class GpuSVD {
+ public:
+  using Scalar = Scalar_;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  using PlainMatrix = Matrix<Scalar, Dynamic, Dynamic, ColMajor>;
+  using RealVector = Matrix<RealScalar, Dynamic, 1>;
+
+  GpuSVD() { init_context(); }
+
+  template <typename InputType>
+  explicit GpuSVD(const EigenBase<InputType>& A, unsigned int options = ComputeThinU | ComputeThinV) {
+    init_context();
+    compute(A, options);
+  }
+
+  ~GpuSVD() {
+    if (handle_) (void)cusolverDnDestroy(handle_);
+    if (cublas_) (void)cublasDestroy(cublas_);
+    if (stream_) (void)cudaStreamDestroy(stream_);
+  }
+
+  GpuSVD(const GpuSVD&) = delete;
+  GpuSVD& operator=(const GpuSVD&) = delete;
+  // Move constructors omitted for brevity — follow GpuQR pattern.
+
+  // ---- Factorization -------------------------------------------------------
+
+  template <typename InputType>
+  GpuSVD& compute(const EigenBase<InputType>& A, unsigned int options = ComputeThinU | ComputeThinV) {
+    options_ = options;
+    m_ = A.rows();
+    n_ = A.cols();
+    info_ = InvalidInput;
+    info_synced_ = false;
+
+    if (m_ == 0 || n_ == 0) {
+      info_ = Success;
+      info_synced_ = true;
+      return *this;
+    }
+
+    // cuSOLVER gesvd requires m >= n. For wide matrices, transpose internally.
+    transposed_ = (m_ < n_);
+    const PlainMatrix mat = transposed_ ? PlainMatrix(A.derived().adjoint()) : PlainMatrix(A.derived());
+    if (transposed_) std::swap(m_, n_);
+
+    lda_ = static_cast<int64_t>(mat.rows());
+    const size_t mat_bytes = static_cast<size_t>(lda_) * static_cast<size_t>(n_) * sizeof(Scalar);
+
+    // Copy (possibly transposed) A to device (gesvd overwrites it).
+    d_A_ = internal::DeviceBuffer(mat_bytes);
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_A_.ptr, mat.data(), mat_bytes, cudaMemcpyHostToDevice, stream_));
+
+    factorize();
+    return *this;
+  }
+
+  GpuSVD& compute(const DeviceMatrix<Scalar>& d_A, unsigned int options = ComputeThinU | ComputeThinV) {
+    options_ = options;
+    m_ = d_A.rows();
+    n_ = d_A.cols();
+    info_ = InvalidInput;
+    info_synced_ = false;
+
+    if (m_ == 0 || n_ == 0) {
+      info_ = Success;
+      info_synced_ = true;
+      return *this;
+    }
+
+    transposed_ = (m_ < n_);
+    d_A.waitReady(stream_);
+
+    if (transposed_) {
+      // Transpose on device via cuBLAS geam: d_A_ = A^H.
+      std::swap(m_, n_);
+      lda_ = m_;
+      const size_t mat_bytes = static_cast<size_t>(lda_) * static_cast<size_t>(n_) * sizeof(Scalar);
+      d_A_ = internal::DeviceBuffer(mat_bytes);
+      Scalar alpha_one(1), beta_zero(0);
+      // geam: C(m×n) = alpha * op(A) + beta * op(B). Use B = nullptr trick: beta=0.
+      // A is the original d_A (n_orig × m_orig = n × m after swap), transposed → m × n.
+      EIGEN_CUBLAS_CHECK(internal::cublasXgeam(
+          cublas_, CUBLAS_OP_C, CUBLAS_OP_N, static_cast<int>(m_), static_cast<int>(n_), &alpha_one, d_A.data(),
+          static_cast<int>(d_A.rows()), &beta_zero, static_cast<const Scalar*>(nullptr), static_cast<int>(m_),
+          static_cast<Scalar*>(d_A_.ptr), static_cast<int>(m_)));
+    } else {
+      lda_ = static_cast<int64_t>(d_A.rows());
+      const size_t mat_bytes = static_cast<size_t>(lda_) * static_cast<size_t>(n_) * sizeof(Scalar);
+      d_A_ = internal::DeviceBuffer(mat_bytes);
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_A_.ptr, d_A.data(), mat_bytes, cudaMemcpyDeviceToDevice, stream_));
+    }
+
+    factorize();
+    return *this;
+  }
+
+  // ---- Accessors -----------------------------------------------------------
+
+  ComputationInfo info() const {
+    sync_info();
+    return info_;
+  }
+
+  Index rows() const { return transposed_ ? n_ : m_; }
+  Index cols() const { return transposed_ ? m_ : n_; }
+
+  /** Singular values (always available). Downloads from device on each call. */
+  RealVector singularValues() const {
+    sync_info();
+    eigen_assert(info_ == Success);
+    const Index k = (std::min)(m_, n_);
+    RealVector S(k);
+    EIGEN_CUDA_RUNTIME_CHECK(
+        cudaMemcpy(S.data(), d_S_.ptr, static_cast<size_t>(k) * sizeof(RealScalar), cudaMemcpyDeviceToHost));
+    return S;
+  }
+
+  /** Left singular vectors U. Returns m_orig × k or m_orig × m_orig.
+   * For transposed case (m_orig < n_orig), U comes from cuSOLVER's VT. */
+  PlainMatrix matrixU() const {
+    sync_info();
+    eigen_assert(info_ == Success);
+    eigen_assert((options_ & (ComputeThinU | ComputeFullU)) && "matrixU() requires ComputeThinU or ComputeFullU");
+    const Index m_orig = transposed_ ? n_ : m_;
+    const Index n_orig = transposed_ ? m_ : n_;
+    const Index k = (std::min)(m_orig, n_orig);
+    if (!transposed_) {
+      const Index ucols = (options_ & ComputeFullU) ? m_ : k;
+      PlainMatrix U(m_, ucols);
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy(U.data(), d_U_.ptr,
+                                          static_cast<size_t>(m_) * static_cast<size_t>(ucols) * sizeof(Scalar),
+                                          cudaMemcpyDeviceToHost));
+      return U;
+    } else {
+      // Transposed: U_orig = VT_stored^H. VT_stored is vtrows × n_ (= vtrows × m_orig).
+      const Index vtrows = (options_ & ComputeFullU) ? m_orig : k;  // Note: FullU maps to FullV of A^H
+      PlainMatrix VT_stored(vtrows, n_);
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy(VT_stored.data(), d_VT_.ptr,
+                                          static_cast<size_t>(vtrows) * static_cast<size_t>(n_) * sizeof(Scalar),
+                                          cudaMemcpyDeviceToHost));
+      return VT_stored.adjoint();  // m_orig × vtrows
+    }
+  }
+
+  /** Right singular vectors transposed V^T. Returns k × n_orig or n_orig × n_orig.
+   * For transposed case, VT comes from cuSOLVER's U. */
+  PlainMatrix matrixVT() const {
+    sync_info();
+    eigen_assert(info_ == Success);
+    eigen_assert((options_ & (ComputeThinV | ComputeFullV)) && "matrixVT() requires ComputeThinV or ComputeFullV");
+    const Index m_orig = transposed_ ? n_ : m_;
+    const Index n_orig = transposed_ ? m_ : n_;
+    const Index k = (std::min)(m_orig, n_orig);
+    if (!transposed_) {
+      const Index vtrows = (options_ & ComputeFullV) ? n_ : k;
+      PlainMatrix VT(vtrows, n_);
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy(VT.data(), d_VT_.ptr,
+                                          static_cast<size_t>(vtrows) * static_cast<size_t>(n_) * sizeof(Scalar),
+                                          cudaMemcpyDeviceToHost));
+      return VT;
+    } else {
+      // Transposed: VT_orig = U_stored^H. U_stored is m_ × ucols (= n_orig × ucols).
+      const Index ucols = (options_ & ComputeFullV) ? n_orig : k;  // FullV maps to FullU of A^H
+      PlainMatrix U_stored(m_, ucols);
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpy(U_stored.data(), d_U_.ptr,
+                                          static_cast<size_t>(m_) * static_cast<size_t>(ucols) * sizeof(Scalar),
+                                          cudaMemcpyDeviceToHost));
+      return U_stored.adjoint();  // ucols × n_orig
+    }
+  }
+
+  /** Number of singular values above threshold. */
+  Index rank(RealScalar threshold = RealScalar(-1)) const {
+    RealVector S = singularValues();
+    if (S.size() == 0) return 0;
+    if (threshold < 0) {
+      threshold = (std::max)(m_, n_) * S(0) * NumTraits<RealScalar>::epsilon();
+    }
+    Index r = 0;
+    for (Index i = 0; i < S.size(); ++i) {
+      if (S(i) > threshold) ++r;
+    }
+    return r;
+  }
+
+  // ---- Solve ---------------------------------------------------------------
+
+  /** Pseudoinverse solve: X = V * diag(1/S) * U^H * B. */
+  template <typename Rhs>
+  PlainMatrix solve(const MatrixBase<Rhs>& B) const {
+    return solve_impl(B, (std::min)(m_, n_), RealScalar(0));
+  }
+
+  /** Truncated solve: use only top trunc singular triplets. */
+  template <typename Rhs>
+  PlainMatrix solve(const MatrixBase<Rhs>& B, Index trunc) const {
+    eigen_assert(trunc > 0 && trunc <= (std::min)(m_, n_));
+    return solve_impl(B, trunc, RealScalar(0));
+  }
+
+  /** Tikhonov-regularized solve: D_ii = S_i / (S_i^2 + lambda^2). */
+  template <typename Rhs>
+  PlainMatrix solve(const MatrixBase<Rhs>& B, RealScalar lambda) const {
+    eigen_assert(lambda > 0);
+    return solve_impl(B, (std::min)(m_, n_), lambda);
+  }
+
+  cudaStream_t stream() const { return stream_; }
+
+ private:
+  cudaStream_t stream_ = nullptr;
+  cusolverDnHandle_t handle_ = nullptr;
+  cublasHandle_t cublas_ = nullptr;
+  internal::CusolverParams params_;
+  internal::DeviceBuffer d_A_;        // working copy of A (overwritten by gesvd)
+  internal::DeviceBuffer d_U_;        // left singular vectors
+  internal::DeviceBuffer d_S_;        // singular values (RealScalar)
+  internal::DeviceBuffer d_VT_;       // right singular vectors transposed
+  internal::DeviceBuffer d_scratch_;  // workspace + info
+  size_t scratch_size_ = 0;
+  std::vector<char> h_workspace_;
+  unsigned int options_ = 0;
+  Index m_ = 0;
+  Index n_ = 0;
+  int64_t lda_ = 0;
+  bool transposed_ = false;  // true if m < n (we compute SVD of A^T internally)
+  ComputationInfo info_ = InvalidInput;
+  int info_word_ = 0;
+  bool info_synced_ = true;
+
+  void init_context() {
+    EIGEN_CUDA_RUNTIME_CHECK(cudaStreamCreate(&stream_));
+    EIGEN_CUSOLVER_CHECK(cusolverDnCreate(&handle_));
+    EIGEN_CUSOLVER_CHECK(cusolverDnSetStream(handle_, stream_));
+    EIGEN_CUBLAS_CHECK(cublasCreate(&cublas_));
+    EIGEN_CUBLAS_CHECK(cublasSetStream(cublas_, stream_));
+    ensure_scratch(0);
+  }
+
+  void ensure_scratch(size_t workspace_bytes) {
+    constexpr size_t kAlign = 16;
+    workspace_bytes = (workspace_bytes + kAlign - 1) & ~(kAlign - 1);
+    size_t needed = workspace_bytes + sizeof(int);
+    if (needed > scratch_size_) {
+      if (d_scratch_.ptr) EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+      d_scratch_ = internal::DeviceBuffer(needed);
+      scratch_size_ = needed;
+    }
+  }
+
+  void* scratch_workspace() const { return d_scratch_.ptr; }
+  int* scratch_info() const {
+    return reinterpret_cast<int*>(static_cast<char*>(d_scratch_.ptr) + scratch_size_ - sizeof(int));
+  }
+
+  void sync_info() const {
+    if (!info_synced_) {
+      EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+      const_cast<GpuSVD*>(this)->info_ = (info_word_ == 0) ? Success : NumericalIssue;
+      const_cast<GpuSVD*>(this)->info_synced_ = true;
+    }
+  }
+
+  // Swap U↔V flags for the transposed case.
+  static unsigned int swap_uv_options(unsigned int opts) {
+    unsigned int result = 0;
+    if (opts & ComputeThinU) result |= ComputeThinV;
+    if (opts & ComputeFullU) result |= ComputeFullV;
+    if (opts & ComputeThinV) result |= ComputeThinU;
+    if (opts & ComputeFullV) result |= ComputeFullU;
+    return result;
+  }
+
+  static signed char jobu(unsigned int opts) {
+    if (opts & ComputeFullU) return 'A';
+    if (opts & ComputeThinU) return 'S';
+    return 'N';
+  }
+
+  static signed char jobvt(unsigned int opts) {
+    if (opts & ComputeFullV) return 'A';
+    if (opts & ComputeThinV) return 'S';
+    return 'N';
+  }
+
+  void factorize() {
+    constexpr cudaDataType_t dtype = internal::cusolver_data_type<Scalar>::value;
+    constexpr cudaDataType_t rtype = internal::cuda_data_type<RealScalar>::value;
+    const Index k = (std::min)(m_, n_);
+
+    info_synced_ = false;
+    info_ = InvalidInput;
+
+    // Allocate output buffers. When transposed, swap U/V roles for cuSOLVER.
+    d_S_ = internal::DeviceBuffer(static_cast<size_t>(k) * sizeof(RealScalar));
+
+    // Internal options: for transposed case, what user wants as U we compute as VT of A^H.
+    const unsigned int int_opts = transposed_ ? swap_uv_options(options_) : options_;
+
+    const Index ucols = (int_opts & ComputeFullU) ? m_ : ((int_opts & ComputeThinU) ? k : 0);
+    const Index vtrows = (int_opts & ComputeFullV) ? n_ : ((int_opts & ComputeThinV) ? k : 0);
+    const int64_t ldu = m_;
+    const int64_t ldvt = vtrows > 0 ? vtrows : 1;
+
+    if (ucols > 0) d_U_ = internal::DeviceBuffer(static_cast<size_t>(m_) * static_cast<size_t>(ucols) * sizeof(Scalar));
+    if (vtrows > 0)
+      d_VT_ = internal::DeviceBuffer(static_cast<size_t>(vtrows) * static_cast<size_t>(n_) * sizeof(Scalar));
+
+    // computeType must match the matrix data type (dtype), not the singular value type (rtype).
+    eigen_assert(m_ >= n_ && "Internal error: m_ < n_ should have been handled by transpose in compute()");
+    size_t dev_ws = 0, host_ws = 0;
+    EIGEN_CUSOLVER_CHECK(cusolverDnXgesvd_bufferSize(
+        handle_, params_.p, jobu(int_opts), jobvt(int_opts), static_cast<int64_t>(m_), static_cast<int64_t>(n_), dtype,
+        d_A_.ptr, lda_, rtype, d_S_.ptr, dtype, ucols > 0 ? d_U_.ptr : nullptr, ldu, dtype,
+        vtrows > 0 ? d_VT_.ptr : nullptr, ldvt, dtype, &dev_ws, &host_ws));
+
+    ensure_scratch(dev_ws);
+    h_workspace_.resize(host_ws);
+
+    // Compute SVD.
+    EIGEN_CUSOLVER_CHECK(cusolverDnXgesvd(handle_, params_.p, jobu(int_opts), jobvt(int_opts), static_cast<int64_t>(m_),
+                                          static_cast<int64_t>(n_), dtype, d_A_.ptr, lda_, rtype, d_S_.ptr, dtype,
+                                          ucols > 0 ? d_U_.ptr : nullptr, ldu, dtype, vtrows > 0 ? d_VT_.ptr : nullptr,
+                                          ldvt, dtype, scratch_workspace(), dev_ws,
+                                          host_ws > 0 ? h_workspace_.data() : nullptr, host_ws, scratch_info()));
+
+    EIGEN_CUDA_RUNTIME_CHECK(
+        cudaMemcpyAsync(&info_word_, scratch_info(), sizeof(int), cudaMemcpyDeviceToHost, stream_));
+  }
+
+  // Internal solve: X = V * diag(D) * U^H * B, using top `trunc` triplets.
+  // D_ii = 1/S_i (if lambda==0) or S_i/(S_i^2+lambda^2).
+  //
+  // For non-transposed: stored U, VT. X = VT^H * D * U^H * B.
+  // For transposed (SVD of A^H): stored U', VT'. X = U' * D * VT' * B.
+  template <typename Rhs>
+  PlainMatrix solve_impl(const MatrixBase<Rhs>& B, Index trunc, RealScalar lambda) const {
+    sync_info();
+    eigen_assert(info_ == Success && "GpuSVD::solve called on a failed or uninitialized decomposition");
+    eigen_assert((options_ & (ComputeThinU | ComputeFullU)) && "solve requires U");
+    eigen_assert((options_ & (ComputeThinV | ComputeFullV)) && "solve requires V");
+
+    const Index m_orig = transposed_ ? n_ : m_;
+    const Index n_orig = transposed_ ? m_ : n_;
+    eigen_assert(B.rows() == m_orig);
+
+    const Index k = (std::min)(m_, n_);  // = min(m_orig, n_orig)
+    const Index kk = (std::min)(trunc, k);
+    const Index nrhs = B.cols();
+
+    // Download S to host to build the diagonal scaling.
+    RealVector S(k);
+    EIGEN_CUDA_RUNTIME_CHECK(
+        cudaMemcpy(S.data(), d_S_.ptr, static_cast<size_t>(k) * sizeof(RealScalar), cudaMemcpyDeviceToHost));
+    EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+
+    // Upload B (m_orig × nrhs).
+    const PlainMatrix rhs(B);
+    internal::DeviceBuffer d_B(static_cast<size_t>(m_orig) * static_cast<size_t>(nrhs) * sizeof(Scalar));
+    EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_B.ptr, rhs.data(),
+                                             static_cast<size_t>(m_orig) * static_cast<size_t>(nrhs) * sizeof(Scalar),
+                                             cudaMemcpyHostToDevice, stream_));
+
+    // Step 1: tmp = U_orig^H * B  (kk × nrhs).
+    // Non-transposed: U_stored is m_×ucols, U_orig = U_stored. Use U_stored^H * B.
+    // Transposed: U_orig = VT_stored^H, so U_orig^H = VT_stored. Use VT_stored * B (no transpose!).
+    internal::DeviceBuffer d_tmp(static_cast<size_t>(kk) * static_cast<size_t>(nrhs) * sizeof(Scalar));
+    {
+      Scalar alpha_one(1), beta_zero(0);
+      constexpr cudaDataType_t dtype = internal::cuda_data_type<Scalar>::value;
+      constexpr cublasComputeType_t compute = internal::cuda_compute_type<Scalar>::value;
+
+      if (!transposed_) {
+        // U_stored^H * B: (m_×kk)^H × (m_×nrhs) → kk×nrhs.
+        EIGEN_CUBLAS_CHECK(cublasGemmEx(cublas_, CUBLAS_OP_C, CUBLAS_OP_N, static_cast<int>(kk), static_cast<int>(nrhs),
+                                        static_cast<int>(m_), &alpha_one, d_U_.ptr, dtype, static_cast<int>(m_),
+                                        d_B.ptr, dtype, static_cast<int>(m_orig), &beta_zero, d_tmp.ptr, dtype,
+                                        static_cast<int>(kk), compute, internal::cuda_gemm_algo()));
+      } else {
+        // VT_stored * B: VT_stored is vtrows×n_ = kk×m_orig (thin), NoTrans.
+        // vtrows×m_orig times m_orig×nrhs → vtrows×nrhs. Use first kk rows.
+        const Index vtrows_stored = (swap_uv_options(options_) & ComputeFullV) ? n_ : k;
+        EIGEN_CUBLAS_CHECK(cublasGemmEx(
+            cublas_, CUBLAS_OP_N, CUBLAS_OP_N, static_cast<int>(kk), static_cast<int>(nrhs), static_cast<int>(m_orig),
+            &alpha_one, d_VT_.ptr, dtype, static_cast<int>(vtrows_stored), d_B.ptr, dtype, static_cast<int>(m_orig),
+            &beta_zero, d_tmp.ptr, dtype, static_cast<int>(kk), compute, internal::cuda_gemm_algo()));
+      }
+    }
+
+    // Step 2: Scale row i of tmp by D_ii.
+    // Download tmp to host, scale, re-upload. (Simple and correct; a device kernel would be faster.)
+    {
+      PlainMatrix tmp(kk, nrhs);
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(tmp.data(), d_tmp.ptr,
+                                               static_cast<size_t>(kk) * static_cast<size_t>(nrhs) * sizeof(Scalar),
+                                               cudaMemcpyDeviceToHost, stream_));
+      EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+
+      for (Index i = 0; i < kk; ++i) {
+        RealScalar si = S(i);
+        RealScalar di = (lambda == RealScalar(0)) ? (si > 0 ? RealScalar(1) / si : RealScalar(0))
+                                                  : si / (si * si + lambda * lambda);
+        tmp.row(i) *= Scalar(di);
+      }
+
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(d_tmp.ptr, tmp.data(),
+                                               static_cast<size_t>(kk) * static_cast<size_t>(nrhs) * sizeof(Scalar),
+                                               cudaMemcpyHostToDevice, stream_));
+    }
+
+    // Step 3: X = V_orig * tmp  (n_orig × nrhs).
+    // Non-transposed: V_orig = VT_stored^H. VT_stored[:kk,:]^H * tmp → n_orig × nrhs.
+    // Transposed: V_orig = U_stored[:,:kk]. U_stored * tmp → n_orig × nrhs (NoTrans).
+    PlainMatrix X(n_orig, nrhs);
+    {
+      internal::DeviceBuffer d_X(static_cast<size_t>(n_orig) * static_cast<size_t>(nrhs) * sizeof(Scalar));
+      Scalar alpha_one(1), beta_zero(0);
+      constexpr cudaDataType_t dtype = internal::cuda_data_type<Scalar>::value;
+      constexpr cublasComputeType_t compute = internal::cuda_compute_type<Scalar>::value;
+
+      if (!transposed_) {
+        const Index vtrows = (options_ & ComputeFullV) ? n_ : k;
+        EIGEN_CUBLAS_CHECK(cublasGemmEx(cublas_, CUBLAS_OP_C, CUBLAS_OP_N, static_cast<int>(n_orig),
+                                        static_cast<int>(nrhs), static_cast<int>(kk), &alpha_one, d_VT_.ptr, dtype,
+                                        static_cast<int>(vtrows), d_tmp.ptr, dtype, static_cast<int>(kk), &beta_zero,
+                                        d_X.ptr, dtype, static_cast<int>(n_orig), compute, internal::cuda_gemm_algo()));
+      } else {
+        // U_stored is m_×ucols. V_orig = U_stored[:,:kk]. NoTrans × tmp.
+        EIGEN_CUBLAS_CHECK(cublasGemmEx(cublas_, CUBLAS_OP_N, CUBLAS_OP_N, static_cast<int>(n_orig),
+                                        static_cast<int>(nrhs), static_cast<int>(kk), &alpha_one, d_U_.ptr, dtype,
+                                        static_cast<int>(m_), d_tmp.ptr, dtype, static_cast<int>(kk), &beta_zero,
+                                        d_X.ptr, dtype, static_cast<int>(n_orig), compute, internal::cuda_gemm_algo()));
+      }
+
+      EIGEN_CUDA_RUNTIME_CHECK(cudaMemcpyAsync(X.data(), d_X.ptr,
+                                               static_cast<size_t>(n_orig) * static_cast<size_t>(nrhs) * sizeof(Scalar),
+                                               cudaMemcpyDeviceToHost, stream_));
+      EIGEN_CUDA_RUNTIME_CHECK(cudaStreamSynchronize(stream_));
+    }
+
+    return X;
+  }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_GPU_SVD_H

Eigen/src/GPU/README.md

@@ -160,11 +160,40 @@ MatrixXd X2 = d_X2.toHost();
 GpuLU<double> lu;
 lu.compute(d_A);
 auto d_Y = lu.solve(d_B, GpuLU<double>::Transpose);  // A^T Y = B
+
+// QR solve (overdetermined least squares)
+GpuQR<double> qr(A);                // host matrix input
+MatrixXd X = qr.solve(B);           // Q^H * B via ormqr, then trsm on R
+
+// SVD
+GpuSVD<double> svd(A, ComputeThinU | ComputeThinV);
+VectorXd S = svd.singularValues();
+MatrixXd U = svd.matrixU();
+MatrixXd VT = svd.matrixVT();
+MatrixXd X = svd.solve(B);          // pseudoinverse solve
+
+// Self-adjoint eigenvalue decomposition
+GpuSelfAdjointEigenSolver<double> es(A);
+VectorXd eigenvals = es.eigenvalues();
+MatrixXd eigenvecs = es.eigenvectors();
 ```
 
 The cached API keeps the factored matrix on device, avoiding redundant
 host-device transfers and re-factorizations.
 
+### Precision control
+
+GEMM dispatch enables tensor core algorithms by default, allowing cuBLAS to
+choose the fastest algorithm for the given precision and architecture. For
+double precision on sm_80+ (Ampere), this allows Ozaki emulation -- full FP64
+results computed faster via tensor cores.
+
+| Macro | Effect |
+|---|---|
+| *(default)* | Tensor core algorithms enabled. Float uses full FP32. Double may use Ozaki on sm_80+. |
+| `EIGEN_CUDA_TF32` | Opt-in: Float uses TF32 (~2x faster, 10-bit mantissa). Double unaffected. |
+| `EIGEN_NO_CUDA_TENSOR_OPS` | Opt-out: Pedantic compute types, no tensor cores. For bit-exact reproducibility. |
+
 ### Stream control and async execution
 
 Operations are asynchronous by default. The compute-solve chain runs without
@@ -271,6 +300,64 @@ GPU dense partial-pivoting LU via cuSOLVER. Same pattern as `GpuLLT`, plus
 `TransposeMode` parameter on `solve()` (`NoTranspose`, `Transpose`,
 `ConjugateTranspose`).
 
+### `GpuQR<Scalar>` API
+
+GPU dense QR decomposition via cuSOLVER (`geqrf`). Solve uses `ormqr` (apply
+Q^H) + `trsm` (back-substitute on R) -- Q is never formed explicitly.
+
+| Method | Description |
+|--------|-------------|
+| `GpuQR()` | Default construct, then call `compute()` |
+| `GpuQR(A)` | Construct and factorize from host matrix |
+| `compute(A)` | Upload + factorize |
+| `compute(DeviceMatrix)` | D2D copy + factorize |
+| `solve(host_matrix)` | Solve, return host matrix (syncs) |
+| `solve(DeviceMatrix)` | Solve, return `DeviceMatrix` (async) |
+| `info()` | Lazy sync |
+| `rows()`, `cols()`, `stream()` | Dimensions and CUDA stream |
+
+### `GpuSVD<Scalar>` API
+
+GPU dense SVD via cuSOLVER (`gesvd`). Supports thin, full, and values-only
+modes via Eigen's `ComputeThinU | ComputeThinV`, `ComputeFullU | ComputeFullV`,
+or `0` (values only).
+
+| Method | Description |
+|--------|-------------|
+| `GpuSVD()` | Default construct, then call `compute()` |
+| `GpuSVD(A, options)` | Construct and compute (options default: `ComputeThinU \| ComputeThinV`) |
+| `compute(A, options)` | Compute from host matrix |
+| `compute(DeviceMatrix, options)` | Compute from device matrix |
+| `singularValues()` | Download singular values to host |
+| `matrixU()` | Download U to host (requires `ComputeThinU` or `ComputeFullU`) |
+| `matrixVT()` | Download V^T to host (requires `ComputeThinV` or `ComputeFullV`) |
+| `solve(B)` | Pseudoinverse solve (returns host matrix) |
+| `solve(B, k)` | Truncated solve (top k singular triplets) |
+| `solve(B, lambda)` | Tikhonov regularized solve |
+| `rank(threshold)` | Count singular values above threshold |
+| `info()` | Lazy sync |
+| `rows()`, `cols()`, `stream()` | Dimensions and CUDA stream |
+
+Wide matrices (m < n) are handled by internally transposing via cuBLAS `geam`.
+
+### `GpuSelfAdjointEigenSolver<Scalar>` API
+
+GPU symmetric/Hermitian eigenvalue decomposition via cuSOLVER (`syevd`).
+
+| Method | Description |
+|--------|-------------|
+| `GpuSelfAdjointEigenSolver()` | Default construct, then call `compute()` |
+| `GpuSelfAdjointEigenSolver(A, mode)` | Construct and compute (mode default: `ComputeEigenvectors`) |
+| `compute(A, mode)` | Compute from host matrix |
+| `compute(DeviceMatrix, mode)` | Compute from device matrix |
+| `eigenvalues()` | Download eigenvalues to host (ascending order) |
+| `eigenvectors()` | Download eigenvectors to host (columns) |
+| `info()` | Lazy sync |
+| `rows()`, `cols()`, `stream()` | Dimensions and CUDA stream |
+
+`ComputeMode`: `GpuSelfAdjointEigenSolver::EigenvaluesOnly` or
+`GpuSelfAdjointEigenSolver::ComputeEigenvectors`.
+
 ### `HostTransfer<Scalar>` API
 
 Future for async device-to-host transfer.
@@ -303,6 +390,9 @@ The caller must ensure operands don't alias the destination for GEMM and TRSM
 | `CuSolverSupport.h` | `GpuSupport.h`, `<cusolverDn.h>` | cuSOLVER params, fill-mode mapping |
 | `GpuLLT.h` | `CuSolverSupport.h` | Cached dense Cholesky factorization |
 | `GpuLU.h` | `CuSolverSupport.h` | Cached dense LU factorization |
+| `GpuQR.h` | `CuSolverSupport.h`, `CuBlasSupport.h` | Dense QR decomposition |
+| `GpuSVD.h` | `CuSolverSupport.h`, `CuBlasSupport.h` | Dense SVD decomposition |
+| `GpuEigenSolver.h` | `CuSolverSupport.h` | Self-adjoint eigenvalue decomposition |
 
 ## Building and testing
 
@@ -313,6 +403,7 @@ cmake -G Ninja -B build -S . \
   -DEIGEN_TEST_CUBLAS=ON \
   -DEIGEN_TEST_CUSOLVER=ON
 
-cmake --build build --target gpu_cublas gpu_cusolver_llt gpu_cusolver_lu gpu_device_matrix
+cmake --build build --target gpu_cublas gpu_cusolver_llt gpu_cusolver_lu \
+  gpu_cusolver_qr gpu_cusolver_svd gpu_cusolver_eigen gpu_device_matrix
 ctest --test-dir build -R "gpu_cublas|gpu_cusolver|gpu_device" --output-on-failure
 ```

test/CMakeLists.txt

@@ -528,7 +528,7 @@ if(CUDA_FOUND AND EIGEN_TEST_CUDA)
     # compiler and linked against CUDA runtime + cuSOLVER. This avoids NVCC
     # instantiating Eigen's CPU packet operations for CUDA vector types.
     unset(EIGEN_ADD_TEST_FILENAME_EXTENSION)
-    foreach(_cusolver_test IN ITEMS gpu_cusolver_llt gpu_cusolver_lu)
+    foreach(_cusolver_test IN ITEMS gpu_cusolver_llt gpu_cusolver_lu gpu_cusolver_qr gpu_cusolver_svd gpu_cusolver_eigen)
       add_executable(${_cusolver_test} ${_cusolver_test}.cpp)
       target_include_directories(${_cusolver_test} PRIVATE
         "${CUDA_TOOLKIT_ROOT_DIR}/include"

test/gpu_cublas.cpp

@@ -16,6 +16,32 @@
 
 using namespace Eigen;
 
+// Unit roundoff for GPU GEMM compute precision.
+// TF32 (opt-in via EIGEN_CUDA_TF32) has eps ~ 2^{-10}.
+template <typename Scalar>
+typename NumTraits<Scalar>::Real gpu_unit_roundoff() {
+#if defined(EIGEN_CUDA_TF32) && !defined(EIGEN_NO_CUDA_TENSOR_OPS)
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  if (std::is_same<RealScalar, float>::value) return RealScalar(9.8e-4);
+#endif
+  return NumTraits<Scalar>::epsilon();
+}
+
+// Higham-Mary probabilistic error bound for GEMM:
+//   ||C - fl(C)||_F <= lambda * sqrt(k) * u * ||A||_F * ||B||_F
+// where k is the inner dimension, u is the unit roundoff, and
+// lambda = sqrt(2 * ln(2/delta)) with delta = failure probability.
+// lambda = 5 corresponds to delta ~ 10^{-6}.
+// Reference: Higham & Mary, "Probabilistic Error Analysis for Inner Products",
+// SIAM J. Matrix Anal. Appl., 2019.
+template <typename Scalar>
+typename NumTraits<Scalar>::Real gemm_error_bound(Index k, typename NumTraits<Scalar>::Real normA,
+                                                  typename NumTraits<Scalar>::Real normB) {
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  constexpr RealScalar lambda = 5;
+  return lambda * std::sqrt(static_cast<RealScalar>(k)) * gpu_unit_roundoff<Scalar>() * normA * normB;
+}
+
 // ---- Basic GEMM: C = A * B -------------------------------------------------
 
 template <typename Scalar>
@@ -36,7 +62,7 @@ void test_gemm_basic(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = A * B;
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -59,7 +85,7 @@ void test_gemm_adjoint_lhs(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = A.adjoint() * B;
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -82,7 +108,7 @@ void test_gemm_transpose_rhs(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = A * B.transpose();
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -106,7 +132,7 @@ void test_gemm_scaled(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = alpha * A * B;
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -130,7 +156,7 @@ void test_gemm_accumulate(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = C_init + A * B;
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -153,7 +179,7 @@ void test_gemm_accumulate_empty(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = A * B;
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -178,7 +204,7 @@ void test_gemm_subtract(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = C_init - A * B;
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -202,7 +228,7 @@ void test_gemm_subtract_empty(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = -(A * B);
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -226,7 +252,7 @@ void test_gemm_scaled_rhs(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = A * (alpha * B);
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -266,7 +292,7 @@ void test_gemm_explicit_context(Index m, Index n, Index k) {
   Mat C = d_C.toHost();
   Mat C_ref = A * B;
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -296,7 +322,7 @@ void test_gemm_cross_context_reuse(Index n) {
   Mat C = d_C.toHost();
   Mat C_ref = A * B + D * E;
 
-  RealScalar tol = RealScalar(2) * RealScalar(n) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(n, A.norm(), B.norm()) + gemm_error_bound<Scalar>(n, D.norm(), E.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -326,7 +352,7 @@ void test_gemm_cross_context_resize() {
   Mat C = d_C.toHost();
   Mat C_ref = D * E;
 
-  RealScalar tol = RealScalar(16) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(16, D.norm(), E.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -353,7 +379,9 @@ void test_gemm_chain(Index n) {
   Mat D = d_D.toHost();
   Mat D_ref = (A * B) * E;
 
-  RealScalar tol = RealScalar(2) * RealScalar(n) * NumTraits<Scalar>::epsilon() * D_ref.norm();
+  Mat C_ref = A * B;
+  RealScalar tol =
+      gemm_error_bound<Scalar>(n, A.norm(), B.norm()) * E.norm() + gemm_error_bound<Scalar>(n, C_ref.norm(), E.norm());
   VERIFY((D - D_ref).norm() < tol);
 }
 
@@ -401,7 +429,7 @@ void test_llt_solve_expr(Index n, Index nrhs) {
 
   Mat X = d_X.toHost();
   RealScalar residual = (A * X - B).norm() / B.norm();
-  VERIFY(residual < RealScalar(n) * NumTraits<Scalar>::epsilon());
+  VERIFY(residual < RealScalar(n) * gpu_unit_roundoff<Scalar>());
 }
 
 // ---- LLT solve with explicit context ----------------------------------------
@@ -423,7 +451,7 @@ void test_llt_solve_expr_context(Index n, Index nrhs) {
 
   Mat X = d_X.toHost();
   RealScalar residual = (A * X - B).norm() / B.norm();
-  VERIFY(residual < RealScalar(n) * NumTraits<Scalar>::epsilon());
+  VERIFY(residual < RealScalar(n) * gpu_unit_roundoff<Scalar>());
 }
 
 // ---- LU solve expression: d_X = d_A.lu().solve(d_B) ------------------------
@@ -444,7 +472,7 @@ void test_lu_solve_expr(Index n, Index nrhs) {
 
   Mat X = d_X.toHost();
   RealScalar residual = (A * X - B).norm() / (A.norm() * X.norm());
-  VERIFY(residual < RealScalar(10) * RealScalar(n) * NumTraits<Scalar>::epsilon());
+  VERIFY(residual < RealScalar(10) * RealScalar(n) * gpu_unit_roundoff<Scalar>());
 }
 
 // ---- GEMM + solver chain: C = A * B, X = C.llt().solve(D) ------------------
@@ -474,7 +502,7 @@ void test_gemm_then_solve(Index n) {
 
   Mat X = d_X.toHost();
   RealScalar residual = (C * X - D).norm() / D.norm();
-  VERIFY(residual < RealScalar(n) * NumTraits<Scalar>::epsilon());
+  VERIFY(residual < RealScalar(n) * gpu_unit_roundoff<Scalar>());
 }
 
 // ---- LLT solve with Upper triangle -----------------------------------------
@@ -495,7 +523,7 @@ void test_llt_solve_upper(Index n, Index nrhs) {
 
   Mat X = d_X.toHost();
   RealScalar residual = (A * X - B).norm() / B.norm();
-  VERIFY(residual < RealScalar(n) * NumTraits<Scalar>::epsilon());
+  VERIFY(residual < RealScalar(n) * gpu_unit_roundoff<Scalar>());
 }
 
 // ---- LU solve with explicit context -----------------------------------------
@@ -517,7 +545,7 @@ void test_lu_solve_expr_context(Index n, Index nrhs) {
 
   Mat X = d_X.toHost();
   RealScalar residual = (A * X - B).norm() / (A.norm() * X.norm());
-  VERIFY(residual < RealScalar(10) * RealScalar(n) * NumTraits<Scalar>::epsilon());
+  VERIFY(residual < RealScalar(10) * RealScalar(n) * gpu_unit_roundoff<Scalar>());
 }
 
 // ---- Zero-nrhs solver expressions ------------------------------------------
@@ -581,7 +609,7 @@ void test_trsm(Index n, Index nrhs) {
 
   Mat X = d_X.toHost();
   RealScalar residual = (A * X - B).norm() / B.norm();
-  VERIFY(residual < RealScalar(n) * NumTraits<Scalar>::epsilon());
+  VERIFY(residual < RealScalar(n) * gpu_unit_roundoff<Scalar>());
 }
 
 // ---- SYMM/HEMM: selfadjointView<UpLo>() * B --------------------------------
@@ -603,7 +631,7 @@ void test_symm(Index n, Index nrhs) {
   Mat C = d_C.toHost();
   Mat C_ref = A * B;  // A is symmetric, so full multiply == symm
 
-  RealScalar tol = RealScalar(n) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(n, A.norm(), B.norm());
   VERIFY((C - C_ref).norm() < tol);
 }
 
@@ -629,7 +657,7 @@ void test_syrk(Index n, Index k) {
   Mat C_lower = C.template triangularView<Lower>();
   Mat C_ref_lower = C_ref.template triangularView<Lower>();
 
-  RealScalar tol = RealScalar(k) * NumTraits<Scalar>::epsilon() * C_ref.norm();
+  RealScalar tol = gemm_error_bound<Scalar>(k, A.norm(), A.norm());
   VERIFY((C_lower - C_ref_lower).norm() < tol);
 }
 

test/gpu_cusolver_eigen.cpp

@@ -0,0 +1,180 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// Tests for GpuSelfAdjointEigenSolver: GPU symmetric/Hermitian eigenvalue
+// decomposition via cuSOLVER.
+
+#define EIGEN_USE_GPU
+#include "main.h"
+#include <Eigen/Eigenvalues>
+#include <Eigen/GPU>
+
+using namespace Eigen;
+
+// ---- Reconstruction: V * diag(W) * V^H ≈ A ---------------------------------
+
+template <typename Scalar>
+void test_eigen_reconstruction(Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  // Build a symmetric/Hermitian matrix.
+  Mat R = Mat::Random(n, n);
+  Mat A = R + R.adjoint();
+
+  GpuSelfAdjointEigenSolver<Scalar> es(A);
+  VERIFY_IS_EQUAL(es.info(), Success);
+
+  auto W = es.eigenvalues();
+  Mat V = es.eigenvectors();
+
+  VERIFY_IS_EQUAL(W.size(), n);
+  VERIFY_IS_EQUAL(V.rows(), n);
+  VERIFY_IS_EQUAL(V.cols(), n);
+
+  // Reconstruct: A_hat = V * diag(W) * V^H.
+  Mat A_hat = V * W.asDiagonal() * V.adjoint();
+  RealScalar tol = RealScalar(5) * std::sqrt(static_cast<RealScalar>(n)) * NumTraits<Scalar>::epsilon() * A.norm();
+  VERIFY((A_hat - A).norm() < tol);
+
+  // Orthogonality: V^H * V ≈ I.
+  Mat VhV = V.adjoint() * V;
+  Mat eye = Mat::Identity(n, n);
+  VERIFY((VhV - eye).norm() < tol);
+}
+
+// ---- Eigenvalues match CPU SelfAdjointEigenSolver ---------------------------
+
+template <typename Scalar>
+void test_eigen_values(Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat R = Mat::Random(n, n);
+  Mat A = R + R.adjoint();
+
+  GpuSelfAdjointEigenSolver<Scalar> gpu_es(A);
+  VERIFY_IS_EQUAL(gpu_es.info(), Success);
+  auto W_gpu = gpu_es.eigenvalues();
+
+  SelfAdjointEigenSolver<Mat> cpu_es(A);
+  auto W_cpu = cpu_es.eigenvalues();
+
+  RealScalar tol = RealScalar(5) * std::sqrt(static_cast<RealScalar>(n)) * NumTraits<Scalar>::epsilon() *
+                   W_cpu.cwiseAbs().maxCoeff();
+  VERIFY((W_gpu - W_cpu).norm() < tol);
+}
+
+// ---- Eigenvalues-only mode --------------------------------------------------
+
+template <typename Scalar>
+void test_eigen_values_only(Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat R = Mat::Random(n, n);
+  Mat A = R + R.adjoint();
+
+  GpuSelfAdjointEigenSolver<Scalar> gpu_es(A, GpuSelfAdjointEigenSolver<Scalar>::EigenvaluesOnly);
+  VERIFY_IS_EQUAL(gpu_es.info(), Success);
+  auto W_gpu = gpu_es.eigenvalues();
+
+  SelfAdjointEigenSolver<Mat> cpu_es(A, EigenvaluesOnly);
+  auto W_cpu = cpu_es.eigenvalues();
+
+  RealScalar tol = RealScalar(5) * std::sqrt(static_cast<RealScalar>(n)) * NumTraits<Scalar>::epsilon() *
+                   W_cpu.cwiseAbs().maxCoeff();
+  VERIFY((W_gpu - W_cpu).norm() < tol);
+}
+
+// ---- DeviceMatrix input path ------------------------------------------------
+
+template <typename Scalar>
+void test_eigen_device_matrix(Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat R = Mat::Random(n, n);
+  Mat A = R + R.adjoint();
+
+  auto d_A = DeviceMatrix<Scalar>::fromHost(A);
+  GpuSelfAdjointEigenSolver<Scalar> es;
+  es.compute(d_A);
+  VERIFY_IS_EQUAL(es.info(), Success);
+
+  auto W_gpu = es.eigenvalues();
+  Mat V = es.eigenvectors();
+
+  // Verify reconstruction.
+  Mat A_hat = V * W_gpu.asDiagonal() * V.adjoint();
+  RealScalar tol = RealScalar(5) * std::sqrt(static_cast<RealScalar>(n)) * NumTraits<Scalar>::epsilon() * A.norm();
+  VERIFY((A_hat - A).norm() < tol);
+}
+
+// ---- Recompute (reuse solver object) ----------------------------------------
+
+template <typename Scalar>
+void test_eigen_recompute(Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  GpuSelfAdjointEigenSolver<Scalar> es;
+
+  for (int trial = 0; trial < 3; ++trial) {
+    Mat R = Mat::Random(n, n);
+    Mat A = R + R.adjoint();
+    es.compute(A);
+    VERIFY_IS_EQUAL(es.info(), Success);
+
+    auto W = es.eigenvalues();
+    Mat V = es.eigenvectors();
+    Mat A_hat = V * W.asDiagonal() * V.adjoint();
+    RealScalar tol = RealScalar(5) * std::sqrt(static_cast<RealScalar>(n)) * NumTraits<Scalar>::epsilon() * A.norm();
+    VERIFY((A_hat - A).norm() < tol);
+  }
+}
+
+// ---- Empty matrix -----------------------------------------------------------
+
+void test_eigen_empty() {
+  GpuSelfAdjointEigenSolver<double> es(MatrixXd(0, 0));
+  VERIFY_IS_EQUAL(es.info(), Success);
+  VERIFY_IS_EQUAL(es.rows(), 0);
+  VERIFY_IS_EQUAL(es.cols(), 0);
+}
+
+// ---- Per-scalar driver ------------------------------------------------------
+
+template <typename Scalar>
+void test_scalar() {
+  // Reconstruction + orthogonality.
+  CALL_SUBTEST(test_eigen_reconstruction<Scalar>(64));
+  CALL_SUBTEST(test_eigen_reconstruction<Scalar>(128));
+
+  // Eigenvalues match CPU.
+  CALL_SUBTEST(test_eigen_values<Scalar>(64));
+  CALL_SUBTEST(test_eigen_values<Scalar>(128));
+
+  // Values-only mode.
+  CALL_SUBTEST(test_eigen_values_only<Scalar>(64));
+
+  // DeviceMatrix input.
+  CALL_SUBTEST(test_eigen_device_matrix<Scalar>(64));
+
+  // Recompute.
+  CALL_SUBTEST(test_eigen_recompute<Scalar>(32));
+}
+
+EIGEN_DECLARE_TEST(gpu_cusolver_eigen) {
+  CALL_SUBTEST(test_scalar<float>());
+  CALL_SUBTEST(test_scalar<double>());
+  CALL_SUBTEST(test_scalar<std::complex<float>>());
+  CALL_SUBTEST(test_scalar<std::complex<double>>());
+  CALL_SUBTEST(test_eigen_empty());
+}

test/gpu_cusolver_qr.cpp

@@ -0,0 +1,185 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// Tests for GpuQR: GPU QR decomposition via cuSOLVER.
+
+#define EIGEN_USE_GPU
+#include "main.h"
+#include <Eigen/QR>
+#include <Eigen/GPU>
+
+using namespace Eigen;
+
+// ---- Solve square system: A * X = B -----------------------------------------
+
+template <typename Scalar>
+void test_qr_solve_square(Index n, Index nrhs) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(n, n);
+  Mat B = Mat::Random(n, nrhs);
+
+  GpuQR<Scalar> qr(A);
+  VERIFY_IS_EQUAL(qr.info(), Success);
+
+  Mat X = qr.solve(B);
+  RealScalar residual = (A * X - B).norm() / (A.norm() * X.norm());
+  VERIFY(residual < RealScalar(10) * RealScalar(n) * NumTraits<Scalar>::epsilon());
+}
+
+// ---- Solve overdetermined system: m > n (least-squares) ---------------------
+
+template <typename Scalar>
+void test_qr_solve_overdetermined(Index m, Index n, Index nrhs) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  eigen_assert(m >= n);
+  Mat A = Mat::Random(m, n);
+  Mat B = Mat::Random(m, nrhs);
+
+  GpuQR<Scalar> qr(A);
+  VERIFY_IS_EQUAL(qr.info(), Success);
+
+  Mat X = qr.solve(B);
+  VERIFY_IS_EQUAL(X.rows(), n);
+  VERIFY_IS_EQUAL(X.cols(), nrhs);
+
+  // Compare with CPU QR.
+  Mat X_cpu = HouseholderQR<Mat>(A).solve(B);
+  RealScalar tol = RealScalar(100) * RealScalar(m) * NumTraits<Scalar>::epsilon();
+  VERIFY((X - X_cpu).norm() / X_cpu.norm() < tol);
+}
+
+// ---- Solve with DeviceMatrix input ------------------------------------------
+
+template <typename Scalar>
+void test_qr_solve_device(Index n, Index nrhs) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(n, n);
+  Mat B = Mat::Random(n, nrhs);
+
+  auto d_A = DeviceMatrix<Scalar>::fromHost(A);
+  auto d_B = DeviceMatrix<Scalar>::fromHost(B);
+
+  GpuQR<Scalar> qr;
+  qr.compute(d_A);
+  VERIFY_IS_EQUAL(qr.info(), Success);
+
+  DeviceMatrix<Scalar> d_X = qr.solve(d_B);
+  Mat X = d_X.toHost();
+
+  RealScalar residual = (A * X - B).norm() / (A.norm() * X.norm());
+  VERIFY(residual < RealScalar(10) * RealScalar(n) * NumTraits<Scalar>::epsilon());
+}
+
+// ---- Solve overdetermined via device path -----------------------------------
+
+template <typename Scalar>
+void test_qr_solve_overdetermined_device(Index m, Index n, Index nrhs) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  eigen_assert(m >= n);
+  Mat A = Mat::Random(m, n);
+  Mat B = Mat::Random(m, nrhs);
+
+  auto d_A = DeviceMatrix<Scalar>::fromHost(A);
+  auto d_B = DeviceMatrix<Scalar>::fromHost(B);
+
+  GpuQR<Scalar> qr;
+  qr.compute(d_A);
+  VERIFY_IS_EQUAL(qr.info(), Success);
+
+  DeviceMatrix<Scalar> d_X = qr.solve(d_B);
+  VERIFY_IS_EQUAL(d_X.rows(), n);
+  VERIFY_IS_EQUAL(d_X.cols(), nrhs);
+
+  Mat X = d_X.toHost();
+  Mat X_cpu = HouseholderQR<Mat>(A).solve(B);
+  RealScalar tol = RealScalar(100) * RealScalar(m) * NumTraits<Scalar>::epsilon();
+  VERIFY((X - X_cpu).norm() / X_cpu.norm() < tol);
+}
+
+// ---- Multiple solves reuse the factorization --------------------------------
+
+template <typename Scalar>
+void test_qr_multiple_solves(Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(n, n);
+  GpuQR<Scalar> qr(A);
+  VERIFY_IS_EQUAL(qr.info(), Success);
+
+  RealScalar tol = RealScalar(10) * RealScalar(n) * NumTraits<Scalar>::epsilon();
+  for (int k = 0; k < 5; ++k) {
+    Mat B = Mat::Random(n, 3);
+    Mat X = qr.solve(B);
+    RealScalar residual = (A * X - B).norm() / (A.norm() * X.norm());
+    VERIFY(residual < tol);
+  }
+}
+
+// ---- Agreement with CPU HouseholderQR ---------------------------------------
+
+template <typename Scalar>
+void test_qr_vs_cpu(Index n, Index nrhs) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(n, n);
+  Mat B = Mat::Random(n, nrhs);
+
+  GpuQR<Scalar> gpu_qr(A);
+  VERIFY_IS_EQUAL(gpu_qr.info(), Success);
+
+  Mat X_gpu = gpu_qr.solve(B);
+  Mat X_cpu = HouseholderQR<Mat>(A).solve(B);
+
+  RealScalar tol = RealScalar(100) * RealScalar(n) * NumTraits<Scalar>::epsilon();
+  VERIFY((X_gpu - X_cpu).norm() / X_cpu.norm() < tol);
+}
+
+// ---- Per-scalar driver ------------------------------------------------------
+
+template <typename Scalar>
+void test_scalar() {
+  CALL_SUBTEST(test_qr_solve_square<Scalar>(1, 1));
+  CALL_SUBTEST(test_qr_solve_square<Scalar>(64, 1));
+  CALL_SUBTEST(test_qr_solve_square<Scalar>(64, 4));
+  CALL_SUBTEST(test_qr_solve_square<Scalar>(256, 8));
+
+  CALL_SUBTEST(test_qr_solve_overdetermined<Scalar>(128, 64, 4));
+  CALL_SUBTEST(test_qr_solve_overdetermined<Scalar>(256, 128, 1));
+
+  CALL_SUBTEST(test_qr_solve_device<Scalar>(64, 4));
+  CALL_SUBTEST(test_qr_solve_overdetermined_device<Scalar>(128, 64, 4));
+  CALL_SUBTEST(test_qr_multiple_solves<Scalar>(64));
+  CALL_SUBTEST(test_qr_vs_cpu<Scalar>(64, 4));
+  CALL_SUBTEST(test_qr_vs_cpu<Scalar>(256, 8));
+}
+
+void test_qr_empty() {
+  GpuQR<double> qr(MatrixXd(0, 0));
+  VERIFY_IS_EQUAL(qr.info(), Success);
+  VERIFY_IS_EQUAL(qr.rows(), 0);
+  VERIFY_IS_EQUAL(qr.cols(), 0);
+}
+
+EIGEN_DECLARE_TEST(gpu_cusolver_qr) {
+  CALL_SUBTEST(test_scalar<float>());
+  CALL_SUBTEST(test_scalar<double>());
+  CALL_SUBTEST(test_scalar<std::complex<float>>());
+  CALL_SUBTEST(test_scalar<std::complex<double>>());
+  CALL_SUBTEST(test_qr_empty());
+}

test/gpu_cusolver_svd.cpp

@@ -0,0 +1,194 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2026 Rasmus Munk Larsen <rmlarsen@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// Tests for GpuSVD: GPU SVD via cuSOLVER.
+
+#define EIGEN_USE_GPU
+#include "main.h"
+#include <Eigen/SVD>
+#include <Eigen/GPU>
+
+using namespace Eigen;
+
+// ---- SVD reconstruction: U * diag(S) * VT ≈ A ------------------------------
+
+template <typename Scalar, unsigned int Options>
+void test_svd_reconstruction(Index m, Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(m, n);
+  GpuSVD<Scalar> svd(A, Options);
+  VERIFY_IS_EQUAL(svd.info(), Success);
+
+  auto S = svd.singularValues();
+  Mat U = svd.matrixU();
+  Mat VT = svd.matrixVT();
+
+  const Index k = (std::min)(m, n);
+
+  // Reconstruct: A_hat = U[:,:k] * diag(S) * VT[:k,:].
+  Mat A_hat = U.leftCols(k) * S.asDiagonal() * VT.topRows(k);
+  RealScalar tol = RealScalar(5) * std::sqrt(static_cast<RealScalar>(k)) * NumTraits<Scalar>::epsilon() * A.norm();
+  VERIFY((A_hat - A).norm() < tol);
+
+  // Orthogonality: U^H * U ≈ I.
+  Mat UtU = U.adjoint() * U;
+  Mat I_u = Mat::Identity(U.cols(), U.cols());
+  VERIFY((UtU - I_u).norm() < tol);
+
+  // Orthogonality: VT * VT^H ≈ I.
+  Mat VtVh = VT * VT.adjoint();
+  Mat I_v = Mat::Identity(VT.rows(), VT.rows());
+  VERIFY((VtVh - I_v).norm() < tol);
+}
+
+// ---- Singular values match CPU BDCSVD ---------------------------------------
+
+template <typename Scalar>
+void test_svd_singular_values(Index m, Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(m, n);
+  GpuSVD<Scalar> svd(A, 0);  // values only
+  VERIFY_IS_EQUAL(svd.info(), Success);
+
+  auto S_gpu = svd.singularValues();
+  auto S_cpu = BDCSVD<Mat>(A, 0).singularValues();
+
+  RealScalar tol =
+      RealScalar(5) * std::sqrt(static_cast<RealScalar>((std::min)(m, n))) * NumTraits<Scalar>::epsilon() * S_cpu(0);
+  VERIFY((S_gpu - S_cpu).norm() < tol);
+}
+
+// ---- Solve: pseudoinverse ---------------------------------------------------
+
+template <typename Scalar>
+void test_svd_solve(Index m, Index n, Index nrhs) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(m, n);
+  Mat B = Mat::Random(m, nrhs);
+
+  GpuSVD<Scalar> svd(A, ComputeThinU | ComputeThinV);
+  VERIFY_IS_EQUAL(svd.info(), Success);
+
+  Mat X = svd.solve(B);
+  VERIFY_IS_EQUAL(X.rows(), n);
+  VERIFY_IS_EQUAL(X.cols(), nrhs);
+
+  // Compare with CPU BDCSVD solve.
+  Mat X_cpu = BDCSVD<Mat>(A, ComputeThinU | ComputeThinV).solve(B);
+  RealScalar tol = RealScalar(100) * RealScalar((std::max)(m, n)) * NumTraits<Scalar>::epsilon();
+  VERIFY((X - X_cpu).norm() / X_cpu.norm() < tol);
+}
+
+// ---- Solve: truncated -------------------------------------------------------
+
+template <typename Scalar>
+void test_svd_solve_truncated(Index m, Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(m, n);
+  Mat B = Mat::Random(m, 1);
+  const Index k = (std::min)(m, n);
+  const Index trunc = k / 2;
+  eigen_assert(trunc > 0);
+
+  GpuSVD<Scalar> svd(A, ComputeThinU | ComputeThinV);
+  Mat X_trunc = svd.solve(B, trunc);
+
+  // Build CPU reference: truncated pseudoinverse.
+  auto cpu_svd = BDCSVD<Mat>(A, ComputeThinU | ComputeThinV);
+  auto S = cpu_svd.singularValues();
+  Mat U = cpu_svd.matrixU();
+  Mat V = cpu_svd.matrixV();
+
+  // D_ii = 1/S_i for i < trunc, 0 otherwise.
+  Matrix<RealScalar, Dynamic, 1> D = Matrix<RealScalar, Dynamic, 1>::Zero(k);
+  for (Index i = 0; i < trunc; ++i) D(i) = RealScalar(1) / S(i);
+  Mat X_ref = V * D.asDiagonal() * U.adjoint() * B;
+
+  RealScalar tol = RealScalar(100) * RealScalar(k) * NumTraits<Scalar>::epsilon();
+  VERIFY((X_trunc - X_ref).norm() / X_ref.norm() < tol);
+}
+
+// ---- Solve: Tikhonov regularized --------------------------------------------
+
+template <typename Scalar>
+void test_svd_solve_regularized(Index m, Index n) {
+  using Mat = Matrix<Scalar, Dynamic, Dynamic>;
+  using RealScalar = typename NumTraits<Scalar>::Real;
+
+  Mat A = Mat::Random(m, n);
+  Mat B = Mat::Random(m, 1);
+  RealScalar lambda = RealScalar(0.1);
+  const Index k = (std::min)(m, n);
+
+  GpuSVD<Scalar> svd(A, ComputeThinU | ComputeThinV);
+  Mat X_reg = svd.solve(B, lambda);
+
+  // CPU reference: D_ii = S_i / (S_i^2 + lambda^2).
+  auto cpu_svd = BDCSVD<Mat>(A, ComputeThinU | ComputeThinV);
+  auto S = cpu_svd.singularValues();
+  Mat U = cpu_svd.matrixU();
+  Mat V = cpu_svd.matrixV();
+
+  Matrix<RealScalar, Dynamic, 1> D(k);
+  for (Index i = 0; i < k; ++i) D(i) = S(i) / (S(i) * S(i) + lambda * lambda);
+  Mat X_ref = V * D.asDiagonal() * U.adjoint() * B;
+
+  RealScalar tol = RealScalar(100) * RealScalar(k) * NumTraits<Scalar>::epsilon();
+  VERIFY((X_reg - X_ref).norm() / X_ref.norm() < tol);
+}
+
+// ---- Empty matrix -----------------------------------------------------------
+
+void test_svd_empty() {
+  GpuSVD<double> svd(MatrixXd(0, 0), 0);
+  VERIFY_IS_EQUAL(svd.info(), Success);
+  VERIFY_IS_EQUAL(svd.rows(), 0);
+  VERIFY_IS_EQUAL(svd.cols(), 0);
+}
+
+// ---- Per-scalar driver ------------------------------------------------------
+
+template <typename Scalar>
+void test_scalar() {
+  // Reconstruction + orthogonality (thin and full, identical test logic).
+  CALL_SUBTEST((test_svd_reconstruction<Scalar, ComputeThinU | ComputeThinV>(64, 64)));
+  CALL_SUBTEST((test_svd_reconstruction<Scalar, ComputeThinU | ComputeThinV>(128, 64)));
+  CALL_SUBTEST((test_svd_reconstruction<Scalar, ComputeThinU | ComputeThinV>(64, 128)));  // wide (m < n)
+  CALL_SUBTEST((test_svd_reconstruction<Scalar, ComputeFullU | ComputeFullV>(64, 64)));
+  CALL_SUBTEST((test_svd_reconstruction<Scalar, ComputeFullU | ComputeFullV>(128, 64)));
+
+  // Singular values.
+  CALL_SUBTEST(test_svd_singular_values<Scalar>(64, 64));
+  CALL_SUBTEST(test_svd_singular_values<Scalar>(128, 64));
+
+  // Solve.
+  CALL_SUBTEST(test_svd_solve<Scalar>(64, 64, 4));
+  CALL_SUBTEST(test_svd_solve<Scalar>(128, 64, 4));
+  CALL_SUBTEST(test_svd_solve<Scalar>(64, 128, 4));  // wide (m < n)
+
+  // Truncated and regularized solve.
+  CALL_SUBTEST(test_svd_solve_truncated<Scalar>(64, 64));
+  CALL_SUBTEST(test_svd_solve_regularized<Scalar>(64, 64));
+}
+
+EIGEN_DECLARE_TEST(gpu_cusolver_svd) {
+  CALL_SUBTEST(test_scalar<float>());
+  CALL_SUBTEST(test_scalar<double>());
+  CALL_SUBTEST(test_scalar<std::complex<float>>());
+  CALL_SUBTEST(test_scalar<std::complex<double>>());
+  CALL_SUBTEST(test_svd_empty());
+}

test/gpu_device_matrix.cpp

@@ -35,7 +35,6 @@ void test_allocate(Index rows, Index cols) {
   VERIFY(!dm.empty());
   VERIFY_IS_EQUAL(dm.rows(), rows);
   VERIFY_IS_EQUAL(dm.cols(), cols);
-  VERIFY_IS_EQUAL(dm.outerStride(), rows);
   VERIFY(dm.data() != nullptr);
   VERIFY_IS_EQUAL(dm.sizeInBytes(), size_t(rows) * size_t(cols) * sizeof(Scalar));
 }
@@ -69,7 +68,7 @@ void test_roundtrip_async(Index rows, Index cols) {
   EIGEN_CUDA_RUNTIME_CHECK(cudaStreamCreate(&stream));
 
   // Async upload from raw pointer.
-  auto dm = DeviceMatrix<Scalar>::fromHostAsync(host.data(), rows, cols, rows, stream);
+  auto dm = DeviceMatrix<Scalar>::fromHostAsync(host.data(), rows, cols, stream);
   VERIFY_IS_EQUAL(dm.rows(), rows);
   VERIFY_IS_EQUAL(dm.cols(), cols);
 
@@ -185,7 +184,6 @@ void test_resize() {
   dm.resize(50, 30);
   VERIFY_IS_EQUAL(dm.rows(), 50);
   VERIFY_IS_EQUAL(dm.cols(), 30);
-  VERIFY_IS_EQUAL(dm.outerStride(), 50);
   VERIFY(dm.data() != nullptr);
 
   // Resize to same dimensions is a no-op.