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eigen/test/product_selfadjoint.cpp
2026-03-12 14:45:51 -07:00

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C++

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// 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/.
#include "main.h"
template <typename MatrixType>
void product_selfadjoint(const MatrixType& m) {
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> RowVectorType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, Dynamic, RowMajor> RhsMatrixType;
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3;
VectorType v1 = VectorType::Random(rows), v2 = VectorType::Random(rows), v3(rows);
RowVectorType r1 = RowVectorType::Random(rows), r2 = RowVectorType::Random(rows);
RhsMatrixType m4 = RhsMatrixType::Random(rows, 10);
Scalar s1 = internal::random<Scalar>(), s2 = internal::random<Scalar>(), s3 = internal::random<Scalar>();
m1 = (m1.adjoint() + m1).eval();
// rank2 update
m2 = m1.template triangularView<Lower>();
m2.template selfadjointView<Lower>().rankUpdate(v1, v2);
VERIFY_IS_APPROX(m2, (m1 + v1 * v2.adjoint() + v2 * v1.adjoint()).template triangularView<Lower>().toDenseMatrix());
m2 = m1.template triangularView<Upper>();
m2.template selfadjointView<Upper>().rankUpdate(-v1, s2 * v2, s3);
VERIFY_IS_APPROX(m2, (m1 + (s3 * (-v1) * (s2 * v2).adjoint() + numext::conj(s3) * (s2 * v2) * (-v1).adjoint()))
.template triangularView<Upper>()
.toDenseMatrix());
m2 = m1.template triangularView<Upper>();
m2.template selfadjointView<Upper>().rankUpdate(-s2 * r1.adjoint(), r2.adjoint() * s3, s1);
VERIFY_IS_APPROX(m2, (m1 + s1 * (-s2 * r1.adjoint()) * (r2.adjoint() * s3).adjoint() +
numext::conj(s1) * (r2.adjoint() * s3) * (-s2 * r1.adjoint()).adjoint())
.template triangularView<Upper>()
.toDenseMatrix());
if (rows > 1) {
m2 = m1.template triangularView<Lower>();
m2.block(1, 1, rows - 1, cols - 1)
.template selfadjointView<Lower>()
.rankUpdate(v1.tail(rows - 1), v2.head(cols - 1));
m3 = m1;
m3.block(1, 1, rows - 1, cols - 1) +=
v1.tail(rows - 1) * v2.head(cols - 1).adjoint() + v2.head(cols - 1) * v1.tail(rows - 1).adjoint();
VERIFY_IS_APPROX(m2, m3.template triangularView<Lower>().toDenseMatrix());
}
// matrix-vector
m2 = m1.template triangularView<Lower>();
VERIFY_IS_APPROX(m1 * m4, m2.template selfadjointView<Lower>() * m4);
}
// Test selfadjoint products at blocking boundary sizes.
// The existing test uses random sizes; this tests deterministic sizes
// at transitions (especially around the GEBP early-return threshold of 48).
template <int>
void product_selfadjoint_boundary() {
typedef double Scalar;
typedef Matrix<Scalar, Dynamic, Dynamic> Mat;
typedef Matrix<Scalar, Dynamic, 1> Vec;
const int sizes[] = {1, 2, 3, 4, 8, 16, 47, 48, 49, 64, 96, 128};
for (int si = 0; si < 12; ++si) {
int n = sizes[si];
Mat m1 = Mat::Random(n, n);
m1 = (m1 + m1.transpose()).eval(); // make symmetric
Vec v1 = Vec::Random(n);
Mat rhs = Mat::Random(n, 5);
// Lower selfadjointView * vector
Mat m2 = m1.triangularView<Lower>();
VERIFY_IS_APPROX(m2.selfadjointView<Lower>() * v1, m1 * v1);
// Upper selfadjointView * vector
m2 = m1.triangularView<Upper>();
VERIFY_IS_APPROX(m2.selfadjointView<Upper>() * v1, m1 * v1);
// selfadjointView * matrix
m2 = m1.triangularView<Lower>();
VERIFY_IS_APPROX(m2.selfadjointView<Lower>() * rhs, m1 * rhs);
// rankUpdate
Vec v2 = Vec::Random(n);
m2 = m1.triangularView<Lower>();
m2.selfadjointView<Lower>().rankUpdate(v1, v2);
VERIFY_IS_APPROX(m2, (m1 + v1 * v2.transpose() + v2 * v1.transpose()).triangularView<Lower>().toDenseMatrix());
}
}
// Same test for complex type (tests conjugation logic).
template <int>
void product_selfadjoint_boundary_complex() {
typedef std::complex<float> Scalar;
typedef Matrix<Scalar, Dynamic, Dynamic> Mat;
typedef Matrix<Scalar, Dynamic, 1> Vec;
const int sizes[] = {1, 8, 47, 48, 49, 64};
for (int si = 0; si < 6; ++si) {
int n = sizes[si];
Mat m1 = Mat::Random(n, n);
m1 = (m1 + m1.adjoint()).eval(); // make Hermitian
m1.diagonal() = m1.diagonal().real().template cast<Scalar>(); // real diagonal
Vec v1 = Vec::Random(n);
Mat rhs = Mat::Random(n, 3);
Mat m2 = m1.triangularView<Lower>();
VERIFY_IS_APPROX(m2.selfadjointView<Lower>() * v1, m1 * v1);
VERIFY_IS_APPROX(m2.selfadjointView<Lower>() * rhs, m1 * rhs);
m2 = m1.triangularView<Upper>();
VERIFY_IS_APPROX(m2.selfadjointView<Upper>() * v1, m1 * v1);
}
}
EIGEN_DECLARE_TEST(product_selfadjoint) {
int s = 0;
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(product_selfadjoint(Matrix<float, 1, 1>()));
CALL_SUBTEST_2(product_selfadjoint(Matrix<float, 2, 2>()));
CALL_SUBTEST_3(product_selfadjoint(Matrix3d()));
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2);
CALL_SUBTEST_4(product_selfadjoint(MatrixXcf(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2);
CALL_SUBTEST_5(product_selfadjoint(MatrixXcd(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_6(product_selfadjoint(MatrixXd(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_7(product_selfadjoint(Matrix<float, Dynamic, Dynamic, RowMajor>(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
// Deterministic blocking boundary tests (outside g_repeat).
CALL_SUBTEST_8(product_selfadjoint_boundary<0>());
CALL_SUBTEST_9(product_selfadjoint_boundary_complex<0>());
}