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Clang-format tests, examples, libraries, benchmarks, etc.
This commit is contained in:
Antonio Sánchez
committed by
Rasmus Munk Larsen
Rasmus Munk Larsen
parent
3252ecc7a4
commit
46e9cdb7fe
@@ -14,15 +14,15 @@
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#include <Eigen/QR>
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#include "solverbase.h"
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template<typename MatrixType, int UpLo>
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template <typename MatrixType, int UpLo>
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typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) {
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if(m.cols()==0) return typename MatrixType::RealScalar(0);
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if (m.cols() == 0) return typename MatrixType::RealScalar(0);
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MatrixType symm = m.template selfadjointView<UpLo>();
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return symm.cwiseAbs().colwise().sum().maxCoeff();
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}
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template<typename MatrixType,template <typename,int> class CholType> void test_chol_update(const MatrixType& symm)
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{
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template <typename MatrixType, template <typename, int> class CholType>
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void test_chol_update(const MatrixType& symm) {
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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@@ -31,19 +31,17 @@ template<typename MatrixType,template <typename,int> class CholType> void test_c
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MatrixType symmUp = symm.template triangularView<Upper>();
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MatrixType symmCpy = symm;
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CholType<MatrixType,Lower> chollo(symmLo);
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CholType<MatrixType,Upper> cholup(symmUp);
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CholType<MatrixType, Lower> chollo(symmLo);
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CholType<MatrixType, Upper> cholup(symmUp);
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for (int k=0; k<10; ++k)
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{
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for (int k = 0; k < 10; ++k) {
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VectorType vec = VectorType::Random(symm.rows());
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RealScalar sigma = internal::random<RealScalar>();
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symmCpy += sigma * vec * vec.adjoint();
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// we are doing some downdates, so it might be the case that the matrix is not SPD anymore
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CholType<MatrixType,Lower> chol(symmCpy);
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if(chol.info()!=Success)
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break;
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CholType<MatrixType, Lower> chol(symmCpy);
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if (chol.info() != Success) break;
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chollo.rankUpdate(vec, sigma);
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VERIFY_IS_APPROX(symmCpy, chollo.reconstructedMatrix());
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@@ -53,8 +51,8 @@ template<typename MatrixType,template <typename,int> class CholType> void test_c
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}
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}
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template<typename MatrixType> void cholesky(const MatrixType& m)
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{
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template <typename MatrixType>
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void cholesky(const MatrixType& m) {
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/* this test covers the following files:
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LLT.h LDLT.h
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*/
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@@ -66,40 +64,39 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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MatrixType a0 = MatrixType::Random(rows,cols);
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MatrixType a0 = MatrixType::Random(rows, cols);
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VectorType vecB = VectorType::Random(rows), vecX(rows);
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MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols);
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SquareMatrixType symm = a0 * a0.adjoint();
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MatrixType matB = MatrixType::Random(rows, cols), matX(rows, cols);
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SquareMatrixType symm = a0 * a0.adjoint();
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// let's make sure the matrix is not singular or near singular
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for (int k=0; k<3; ++k)
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{
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MatrixType a1 = MatrixType::Random(rows,cols);
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for (int k = 0; k < 3; ++k) {
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MatrixType a1 = MatrixType::Random(rows, cols);
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symm += a1 * a1.adjoint();
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}
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{
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STATIC_CHECK(( internal::is_same<typename LLT<MatrixType,Lower>::StorageIndex,int>::value ));
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STATIC_CHECK(( internal::is_same<typename LLT<MatrixType,Upper>::StorageIndex,int>::value ));
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STATIC_CHECK((internal::is_same<typename LLT<MatrixType, Lower>::StorageIndex, int>::value));
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STATIC_CHECK((internal::is_same<typename LLT<MatrixType, Upper>::StorageIndex, int>::value));
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SquareMatrixType symmUp = symm.template triangularView<Upper>();
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SquareMatrixType symmLo = symm.template triangularView<Lower>();
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LLT<SquareMatrixType,Lower> chollo(symmLo);
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LLT<SquareMatrixType, Lower> chollo(symmLo);
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VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix());
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check_solverbase<VectorType, VectorType>(symm, chollo, rows, rows, 1);
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check_solverbase<MatrixType, MatrixType>(symm, chollo, rows, cols, rows);
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const MatrixType symmLo_inverse = chollo.solve(MatrixType::Identity(rows,cols));
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RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) /
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matrix_l1_norm<MatrixType, Lower>(symmLo_inverse);
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const MatrixType symmLo_inverse = chollo.solve(MatrixType::Identity(rows, cols));
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RealScalar rcond =
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(RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) / matrix_l1_norm<MatrixType, Lower>(symmLo_inverse);
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RealScalar rcond_est = chollo.rcond();
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// Verify that the estimated condition number is within a factor of 10 of the
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// truth.
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VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
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// test the upper mode
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LLT<SquareMatrixType,Upper> cholup(symmUp);
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LLT<SquareMatrixType, Upper> cholup(symmUp);
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VERIFY_IS_APPROX(symm, cholup.reconstructedMatrix());
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vecX = cholup.solve(vecB);
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VERIFY_IS_APPROX(symm * vecX, vecB);
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@@ -108,16 +105,15 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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// Verify that the estimated condition number is within a factor of 10 of the
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// truth.
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const MatrixType symmUp_inverse = cholup.solve(MatrixType::Identity(rows,cols));
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rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) /
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matrix_l1_norm<MatrixType, Upper>(symmUp_inverse);
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const MatrixType symmUp_inverse = cholup.solve(MatrixType::Identity(rows, cols));
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rcond =
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(RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) / matrix_l1_norm<MatrixType, Upper>(symmUp_inverse);
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rcond_est = cholup.rcond();
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VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
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MatrixType neg = -symmLo;
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chollo.compute(neg);
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VERIFY(neg.size()==0 || chollo.info()==NumericalIssue);
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VERIFY(neg.size() == 0 || chollo.info() == NumericalIssue);
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VERIFY_IS_APPROX(MatrixType(chollo.matrixL().transpose().conjugate()), MatrixType(chollo.matrixU()));
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VERIFY_IS_APPROX(MatrixType(chollo.matrixU().transpose().conjugate()), MatrixType(chollo.matrixL()));
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@@ -125,7 +121,7 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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VERIFY_IS_APPROX(MatrixType(cholup.matrixU().transpose().conjugate()), MatrixType(cholup.matrixL()));
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// test some special use cases of SelfCwiseBinaryOp:
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MatrixType m1 = MatrixType::Random(rows,cols), m2(rows,cols);
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MatrixType m1 = MatrixType::Random(rows, cols), m2(rows, cols);
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m2 = m1;
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m2 += symmLo.template selfadjointView<Lower>().llt().solve(matB);
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VERIFY_IS_APPROX(m2, m1 + symmLo.template selfadjointView<Lower>().llt().solve(matB));
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@@ -142,37 +138,35 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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// LDLT
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{
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STATIC_CHECK(( internal::is_same<typename LDLT<MatrixType,Lower>::StorageIndex,int>::value ));
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STATIC_CHECK(( internal::is_same<typename LDLT<MatrixType,Upper>::StorageIndex,int>::value ));
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STATIC_CHECK((internal::is_same<typename LDLT<MatrixType, Lower>::StorageIndex, int>::value));
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STATIC_CHECK((internal::is_same<typename LDLT<MatrixType, Upper>::StorageIndex, int>::value));
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int sign = internal::random<int>()%2 ? 1 : -1;
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int sign = internal::random<int>() % 2 ? 1 : -1;
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if(sign == -1)
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{
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symm = -symm; // test a negative matrix
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if (sign == -1) {
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symm = -symm; // test a negative matrix
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}
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SquareMatrixType symmUp = symm.template triangularView<Upper>();
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SquareMatrixType symmLo = symm.template triangularView<Lower>();
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LDLT<SquareMatrixType,Lower> ldltlo(symmLo);
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VERIFY(ldltlo.info()==Success);
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LDLT<SquareMatrixType, Lower> ldltlo(symmLo);
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VERIFY(ldltlo.info() == Success);
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VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
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check_solverbase<VectorType, VectorType>(symm, ldltlo, rows, rows, 1);
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check_solverbase<MatrixType, MatrixType>(symm, ldltlo, rows, cols, rows);
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const MatrixType symmLo_inverse = ldltlo.solve(MatrixType::Identity(rows,cols));
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RealScalar rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) /
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matrix_l1_norm<MatrixType, Lower>(symmLo_inverse);
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const MatrixType symmLo_inverse = ldltlo.solve(MatrixType::Identity(rows, cols));
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RealScalar rcond =
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(RealScalar(1) / matrix_l1_norm<MatrixType, Lower>(symmLo)) / matrix_l1_norm<MatrixType, Lower>(symmLo_inverse);
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RealScalar rcond_est = ldltlo.rcond();
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// Verify that the estimated condition number is within a factor of 10 of the
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// truth.
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VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
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LDLT<SquareMatrixType,Upper> ldltup(symmUp);
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VERIFY(ldltup.info()==Success);
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LDLT<SquareMatrixType, Upper> ldltup(symmUp);
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VERIFY(ldltup.info() == Success);
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VERIFY_IS_APPROX(symm, ldltup.reconstructedMatrix());
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vecX = ldltup.solve(vecB);
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VERIFY_IS_APPROX(symm * vecX, vecB);
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@@ -181,9 +175,9 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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// Verify that the estimated condition number is within a factor of 10 of the
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// truth.
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const MatrixType symmUp_inverse = ldltup.solve(MatrixType::Identity(rows,cols));
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rcond = (RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) /
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matrix_l1_norm<MatrixType, Upper>(symmUp_inverse);
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const MatrixType symmUp_inverse = ldltup.solve(MatrixType::Identity(rows, cols));
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rcond =
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(RealScalar(1) / matrix_l1_norm<MatrixType, Upper>(symmUp)) / matrix_l1_norm<MatrixType, Upper>(symmUp_inverse);
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rcond_est = ldltup.rcond();
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VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
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@@ -192,8 +186,7 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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VERIFY_IS_APPROX(MatrixType(ldltup.matrixL().transpose().conjugate()), MatrixType(ldltup.matrixU()));
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VERIFY_IS_APPROX(MatrixType(ldltup.matrixU().transpose().conjugate()), MatrixType(ldltup.matrixL()));
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if(MatrixType::RowsAtCompileTime==Dynamic)
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{
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if (MatrixType::RowsAtCompileTime == Dynamic) {
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// note : each inplace permutation requires a small temporary vector (mask)
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// check inplace solve
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@@ -201,22 +194,19 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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VERIFY_EVALUATION_COUNT(matX = ldltlo.solve(matX), 0);
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VERIFY_IS_APPROX(matX, ldltlo.solve(matB).eval());
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matX = matB;
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VERIFY_EVALUATION_COUNT(matX = ldltup.solve(matX), 0);
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VERIFY_IS_APPROX(matX, ldltup.solve(matB).eval());
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}
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// restore
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if(sign == -1)
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symm = -symm;
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if (sign == -1) symm = -symm;
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// check matrices coming from linear constraints with Lagrange multipliers
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if(rows>=3)
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{
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if (rows >= 3) {
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SquareMatrixType A = symm;
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Index c = internal::random<Index>(0,rows-2);
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A.bottomRightCorner(c,c).setZero();
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Index c = internal::random<Index>(0, rows - 2);
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A.bottomRightCorner(c, c).setZero();
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// Make sure a solution exists:
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vecX.setRandom();
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vecB = A * vecX;
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@@ -228,10 +218,9 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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}
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// check non-full rank matrices
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if(rows>=3)
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{
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Index r = internal::random<Index>(1,rows-1);
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Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,r);
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if (rows >= 3) {
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Index r = internal::random<Index>(1, rows - 1);
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Matrix<Scalar, Dynamic, Dynamic> a = Matrix<Scalar, Dynamic, Dynamic>::Random(rows, r);
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SquareMatrixType A = a * a.adjoint();
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// Make sure a solution exists:
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vecX.setRandom();
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@@ -244,15 +233,13 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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}
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// check matrices with a wide spectrum
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if(rows>=3)
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{
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if (rows >= 3) {
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using std::pow;
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using std::sqrt;
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RealScalar s = (std::min)(16,std::numeric_limits<RealScalar>::max_exponent10/8);
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Matrix<Scalar,Dynamic,Dynamic> a = Matrix<Scalar,Dynamic,Dynamic>::Random(rows,rows);
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Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(rows);
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for(Index k=0; k<rows; ++k)
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d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s));
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RealScalar s = (std::min)(16, std::numeric_limits<RealScalar>::max_exponent10 / 8);
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Matrix<Scalar, Dynamic, Dynamic> a = Matrix<Scalar, Dynamic, Dynamic>::Random(rows, rows);
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Matrix<RealScalar, Dynamic, 1> d = Matrix<RealScalar, Dynamic, 1>::Random(rows);
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for (Index k = 0; k < rows; ++k) d(k) = d(k) * pow(RealScalar(10), internal::random<RealScalar>(-s, s));
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SquareMatrixType A = a * d.asDiagonal() * a.adjoint();
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// Make sure a solution exists:
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vecX.setRandom();
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@@ -262,29 +249,26 @@ template<typename MatrixType> void cholesky(const MatrixType& m)
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VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
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vecX = ldltlo.solve(vecB);
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if(ldltlo.vectorD().real().cwiseAbs().minCoeff()>RealScalar(0))
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{
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VERIFY_IS_APPROX(A * vecX,vecB);
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}
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else
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{
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RealScalar large_tol = sqrt(test_precision<RealScalar>());
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if (ldltlo.vectorD().real().cwiseAbs().minCoeff() > RealScalar(0)) {
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VERIFY_IS_APPROX(A * vecX, vecB);
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} else {
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RealScalar large_tol = sqrt(test_precision<RealScalar>());
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VERIFY((A * vecX).isApprox(vecB, large_tol));
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++g_test_level;
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VERIFY_IS_APPROX(A * vecX,vecB);
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VERIFY_IS_APPROX(A * vecX, vecB);
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--g_test_level;
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}
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}
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}
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// update/downdate
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CALL_SUBTEST(( test_chol_update<SquareMatrixType,LLT>(symm) ));
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CALL_SUBTEST(( test_chol_update<SquareMatrixType,LDLT>(symm) ));
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CALL_SUBTEST((test_chol_update<SquareMatrixType, LLT>(symm)));
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CALL_SUBTEST((test_chol_update<SquareMatrixType, LDLT>(symm)));
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}
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template<typename MatrixType> void cholesky_cplx(const MatrixType& m)
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{
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template <typename MatrixType>
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void cholesky_cplx(const MatrixType& m) {
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// classic test
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cholesky(m);
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@@ -298,50 +282,48 @@ template<typename MatrixType> void cholesky_cplx(const MatrixType& m)
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typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RealMatrixType;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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RealMatrixType a0 = RealMatrixType::Random(rows,cols);
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RealMatrixType a0 = RealMatrixType::Random(rows, cols);
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VectorType vecB = VectorType::Random(rows), vecX(rows);
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MatrixType matB = MatrixType::Random(rows,cols), matX(rows,cols);
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RealMatrixType symm = a0 * a0.adjoint();
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MatrixType matB = MatrixType::Random(rows, cols), matX(rows, cols);
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RealMatrixType symm = a0 * a0.adjoint();
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// let's make sure the matrix is not singular or near singular
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for (int k=0; k<3; ++k)
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{
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RealMatrixType a1 = RealMatrixType::Random(rows,cols);
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for (int k = 0; k < 3; ++k) {
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RealMatrixType a1 = RealMatrixType::Random(rows, cols);
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symm += a1 * a1.adjoint();
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}
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{
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RealMatrixType symmLo = symm.template triangularView<Lower>();
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LLT<RealMatrixType,Lower> chollo(symmLo);
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LLT<RealMatrixType, Lower> chollo(symmLo);
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VERIFY_IS_APPROX(symm, chollo.reconstructedMatrix());
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check_solverbase<VectorType, VectorType>(symm, chollo, rows, rows, 1);
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//check_solverbase<MatrixType, MatrixType>(symm, chollo, rows, cols, rows);
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// check_solverbase<MatrixType, MatrixType>(symm, chollo, rows, cols, rows);
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}
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// LDLT
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{
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int sign = internal::random<int>()%2 ? 1 : -1;
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int sign = internal::random<int>() % 2 ? 1 : -1;
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if(sign == -1)
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{
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symm = -symm; // test a negative matrix
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if (sign == -1) {
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symm = -symm; // test a negative matrix
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}
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RealMatrixType symmLo = symm.template triangularView<Lower>();
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LDLT<RealMatrixType,Lower> ldltlo(symmLo);
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VERIFY(ldltlo.info()==Success);
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LDLT<RealMatrixType, Lower> ldltlo(symmLo);
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VERIFY(ldltlo.info() == Success);
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VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
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check_solverbase<VectorType, VectorType>(symm, ldltlo, rows, rows, 1);
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//check_solverbase<MatrixType, MatrixType>(symm, ldltlo, rows, cols, rows);
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// check_solverbase<MatrixType, MatrixType>(symm, ldltlo, rows, cols, rows);
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}
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}
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// regression test for bug 241
|
||||
template<typename MatrixType> void cholesky_bug241(const MatrixType& m)
|
||||
{
|
||||
template <typename MatrixType>
|
||||
void cholesky_bug241(const MatrixType& m) {
|
||||
eigen_assert(m.rows() == 2 && m.cols() == 2);
|
||||
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
@@ -358,8 +340,8 @@ template<typename MatrixType> void cholesky_bug241(const MatrixType& m)
|
||||
// LDLT is not guaranteed to work for indefinite matrices, but happens to work fine if matrix is diagonal.
|
||||
// This test checks that LDLT reports correctly that matrix is indefinite.
|
||||
// See http://forum.kde.org/viewtopic.php?f=74&t=106942 and bug 736
|
||||
template<typename MatrixType> void cholesky_definiteness(const MatrixType& m)
|
||||
{
|
||||
template <typename MatrixType>
|
||||
void cholesky_definiteness(const MatrixType& m) {
|
||||
eigen_assert(m.rows() == 2 && m.cols() == 2);
|
||||
MatrixType mat;
|
||||
LDLT<MatrixType> ldlt(2);
|
||||
@@ -367,109 +349,96 @@ template<typename MatrixType> void cholesky_definiteness(const MatrixType& m)
|
||||
{
|
||||
mat << 1, 0, 0, -1;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==Success);
|
||||
VERIFY(ldlt.info() == Success);
|
||||
VERIFY(!ldlt.isNegative());
|
||||
VERIFY(!ldlt.isPositive());
|
||||
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY_IS_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
{
|
||||
mat << 1, 2, 2, 1;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==Success);
|
||||
VERIFY(ldlt.info() == Success);
|
||||
VERIFY(!ldlt.isNegative());
|
||||
VERIFY(!ldlt.isPositive());
|
||||
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY_IS_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
{
|
||||
mat << 0, 0, 0, 0;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==Success);
|
||||
VERIFY(ldlt.info() == Success);
|
||||
VERIFY(ldlt.isNegative());
|
||||
VERIFY(ldlt.isPositive());
|
||||
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY_IS_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
{
|
||||
mat << 0, 0, 0, 1;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==Success);
|
||||
VERIFY(ldlt.info() == Success);
|
||||
VERIFY(!ldlt.isNegative());
|
||||
VERIFY(ldlt.isPositive());
|
||||
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY_IS_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
{
|
||||
mat << -1, 0, 0, 0;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==Success);
|
||||
VERIFY(ldlt.info() == Success);
|
||||
VERIFY(ldlt.isNegative());
|
||||
VERIFY(!ldlt.isPositive());
|
||||
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY_IS_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
}
|
||||
|
||||
template<typename>
|
||||
void cholesky_faillure_cases()
|
||||
{
|
||||
template <typename>
|
||||
void cholesky_faillure_cases() {
|
||||
MatrixXd mat;
|
||||
LDLT<MatrixXd> ldlt;
|
||||
|
||||
{
|
||||
mat.resize(2,2);
|
||||
mat.resize(2, 2);
|
||||
mat << 0, 1, 1, 0;
|
||||
ldlt.compute(mat);
|
||||
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY(ldlt.info()==NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
VERIFY(ldlt.info() == NumericalIssue);
|
||||
}
|
||||
#if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE_SSE2)
|
||||
{
|
||||
mat.resize(3,3);
|
||||
mat << -1, -3, 3,
|
||||
-3, -8.9999999999999999999, 1,
|
||||
3, 1, 0;
|
||||
mat.resize(3, 3);
|
||||
mat << -1, -3, 3, -3, -8.9999999999999999999, 1, 3, 1, 0;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY(ldlt.info() == NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
#endif
|
||||
{
|
||||
mat.resize(3,3);
|
||||
mat << 1, 2, 3,
|
||||
2, 4, 1,
|
||||
3, 1, 0;
|
||||
mat.resize(3, 3);
|
||||
mat << 1, 2, 3, 2, 4, 1, 3, 1, 0;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY(ldlt.info() == NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
|
||||
{
|
||||
mat.resize(8,8);
|
||||
mat << 0.1, 0, -0.1, 0, 0, 0, 1, 0,
|
||||
0, 4.24667, 0, 2.00333, 0, 0, 0, 0,
|
||||
-0.1, 0, 0.2, 0, -0.1, 0, 0, 0,
|
||||
0, 2.00333, 0, 8.49333, 0, 2.00333, 0, 0,
|
||||
0, 0, -0.1, 0, 0.1, 0, 0, 1,
|
||||
0, 0, 0, 2.00333, 0, 4.24667, 0, 0,
|
||||
1, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 1, 0, 0, 0;
|
||||
mat.resize(8, 8);
|
||||
mat << 0.1, 0, -0.1, 0, 0, 0, 1, 0, 0, 4.24667, 0, 2.00333, 0, 0, 0, 0, -0.1, 0, 0.2, 0, -0.1, 0, 0, 0, 0, 2.00333,
|
||||
0, 8.49333, 0, 2.00333, 0, 0, 0, 0, -0.1, 0, 0.1, 0, 0, 1, 0, 0, 0, 2.00333, 0, 4.24667, 0, 0, 1, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 1, 0, 0, 0;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY(ldlt.info() == NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
|
||||
// bug 1479
|
||||
{
|
||||
mat.resize(4,4);
|
||||
mat << 1, 2, 0, 1,
|
||||
2, 4, 0, 2,
|
||||
0, 0, 0, 1,
|
||||
1, 2, 1, 1;
|
||||
mat.resize(4, 4);
|
||||
mat << 1, 2, 0, 1, 2, 4, 0, 2, 0, 0, 0, 1, 1, 2, 1, 1;
|
||||
ldlt.compute(mat);
|
||||
VERIFY(ldlt.info()==NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
||||
VERIFY(ldlt.info() == NumericalIssue);
|
||||
VERIFY_IS_NOT_APPROX(mat, ldlt.reconstructedMatrix());
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType> void cholesky_verify_assert()
|
||||
{
|
||||
template <typename MatrixType>
|
||||
void cholesky_verify_assert() {
|
||||
MatrixType tmp;
|
||||
|
||||
LLT<MatrixType> llt;
|
||||
@@ -492,41 +461,40 @@ template<typename MatrixType> void cholesky_verify_assert()
|
||||
VERIFY_RAISES_ASSERT(ldlt.solveInPlace(tmp))
|
||||
}
|
||||
|
||||
EIGEN_DECLARE_TEST(cholesky)
|
||||
{
|
||||
EIGEN_DECLARE_TEST(cholesky) {
|
||||
int s = 0;
|
||||
for(int i = 0; i < g_repeat; i++) {
|
||||
CALL_SUBTEST_1( cholesky(Matrix<double,1,1>()) );
|
||||
CALL_SUBTEST_3( cholesky(Matrix2d()) );
|
||||
CALL_SUBTEST_3( cholesky_bug241(Matrix2d()) );
|
||||
CALL_SUBTEST_3( cholesky_definiteness(Matrix2d()) );
|
||||
CALL_SUBTEST_4( cholesky(Matrix3f()) );
|
||||
CALL_SUBTEST_5( cholesky(Matrix4d()) );
|
||||
for (int i = 0; i < g_repeat; i++) {
|
||||
CALL_SUBTEST_1(cholesky(Matrix<double, 1, 1>()));
|
||||
CALL_SUBTEST_3(cholesky(Matrix2d()));
|
||||
CALL_SUBTEST_3(cholesky_bug241(Matrix2d()));
|
||||
CALL_SUBTEST_3(cholesky_definiteness(Matrix2d()));
|
||||
CALL_SUBTEST_4(cholesky(Matrix3f()));
|
||||
CALL_SUBTEST_5(cholesky(Matrix4d()));
|
||||
|
||||
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
|
||||
CALL_SUBTEST_2( cholesky(MatrixXd(s,s)) );
|
||||
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE);
|
||||
CALL_SUBTEST_2(cholesky(MatrixXd(s, s)));
|
||||
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||||
|
||||
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
|
||||
CALL_SUBTEST_6( cholesky_cplx(MatrixXcd(s,s)) );
|
||||
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2);
|
||||
CALL_SUBTEST_6(cholesky_cplx(MatrixXcd(s, s)));
|
||||
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||||
}
|
||||
// empty matrix, regression test for Bug 785:
|
||||
CALL_SUBTEST_2( cholesky(MatrixXd(0,0)) );
|
||||
CALL_SUBTEST_2(cholesky(MatrixXd(0, 0)));
|
||||
|
||||
// This does not work yet:
|
||||
// CALL_SUBTEST_2( cholesky(Matrix<double,0,0>()) );
|
||||
|
||||
CALL_SUBTEST_4( cholesky_verify_assert<Matrix3f>() );
|
||||
CALL_SUBTEST_7( cholesky_verify_assert<Matrix3d>() );
|
||||
CALL_SUBTEST_8( cholesky_verify_assert<MatrixXf>() );
|
||||
CALL_SUBTEST_2( cholesky_verify_assert<MatrixXd>() );
|
||||
CALL_SUBTEST_4(cholesky_verify_assert<Matrix3f>());
|
||||
CALL_SUBTEST_7(cholesky_verify_assert<Matrix3d>());
|
||||
CALL_SUBTEST_8(cholesky_verify_assert<MatrixXf>());
|
||||
CALL_SUBTEST_2(cholesky_verify_assert<MatrixXd>());
|
||||
|
||||
// Test problem size constructors
|
||||
CALL_SUBTEST_9( LLT<MatrixXf>(10) );
|
||||
CALL_SUBTEST_9( LDLT<MatrixXf>(10) );
|
||||
CALL_SUBTEST_9(LLT<MatrixXf>(10));
|
||||
CALL_SUBTEST_9(LDLT<MatrixXf>(10));
|
||||
|
||||
CALL_SUBTEST_2( cholesky_faillure_cases<void>() );
|
||||
CALL_SUBTEST_2(cholesky_faillure_cases<void>());
|
||||
|
||||
TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries)
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user