533 lines
18 KiB
C++
533 lines
18 KiB
C++
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define TEST_ENABLE_TEMPORARY_TRACKING
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#include "main.h"
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#include <Eigen/Cholesky>
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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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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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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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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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MatrixType symmLo = symm.template triangularView<Lower>();
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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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for (int k=0; k<10; ++k)
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{
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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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chollo.rankUpdate(vec, sigma);
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VERIFY_IS_APPROX(symmCpy, chollo.reconstructedMatrix());
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cholup.rankUpdate(vec, sigma);
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VERIFY_IS_APPROX(symmCpy, cholup.reconstructedMatrix());
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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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/* this test covers the following files:
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LLT.h LDLT.h
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*/
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Index rows = m.rows();
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Index cols = m.cols();
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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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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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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// 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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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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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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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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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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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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matX = cholup.solve(matB);
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VERIFY_IS_APPROX(symm * matX, matB);
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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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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_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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VERIFY_IS_APPROX(MatrixType(cholup.matrixL().transpose().conjugate()), MatrixType(cholup.matrixU()));
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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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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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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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m2 = m1;
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m2.noalias() += 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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m2 = m1;
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m2.noalias() -= 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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}
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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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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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}
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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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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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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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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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matX = ldltup.solve(matB);
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VERIFY_IS_APPROX(symm * matX, matB);
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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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rcond_est = ldltup.rcond();
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VERIFY(rcond_est >= rcond / 10 && rcond_est <= rcond * 10);
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VERIFY_IS_APPROX(MatrixType(ldltlo.matrixL().transpose().conjugate()), MatrixType(ldltlo.matrixU()));
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VERIFY_IS_APPROX(MatrixType(ldltlo.matrixU().transpose().conjugate()), MatrixType(ldltlo.matrixL()));
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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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// note : each inplace permutation requires a small temporary vector (mask)
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// check inplace solve
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matX = matB;
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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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// check matrices coming from linear constraints with Lagrange multipliers
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if(rows>=3)
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{
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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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// Make sure a solution exists:
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vecX.setRandom();
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vecB = A * vecX;
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vecX.setZero();
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ldltlo.compute(A);
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VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
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vecX = ldltlo.solve(vecB);
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VERIFY_IS_APPROX(A * vecX, vecB);
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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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SquareMatrixType A = a * a.adjoint();
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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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vecX.setZero();
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ldltlo.compute(A);
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VERIFY_IS_APPROX(A, ldltlo.reconstructedMatrix());
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vecX = ldltlo.solve(vecB);
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VERIFY_IS_APPROX(A * vecX, vecB);
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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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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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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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vecB = A * vecX;
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vecX.setZero();
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ldltlo.compute(A);
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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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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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--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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}
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template<typename MatrixType> void cholesky_cplx(const MatrixType& m)
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{
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// classic test
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cholesky(m);
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// test mixing real/scalar types
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Index rows = m.rows();
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Index cols = m.cols();
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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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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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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// 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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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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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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}
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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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if(sign == -1)
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{
|
||
|
|
symm = -symm; // test a negative matrix
|
||
|
|
}
|
||
|
|
|
||
|
|
RealMatrixType symmLo = symm.template triangularView<Lower>();
|
||
|
|
|
||
|
|
LDLT<RealMatrixType,Lower> ldltlo(symmLo);
|
||
|
|
VERIFY(ldltlo.info()==Success);
|
||
|
|
VERIFY_IS_APPROX(symm, ldltlo.reconstructedMatrix());
|
||
|
|
|
||
|
|
check_solverbase<VectorType, VectorType>(symm, ldltlo, rows, rows, 1);
|
||
|
|
//check_solverbase<MatrixType, MatrixType>(symm, ldltlo, rows, cols, rows);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
// regression test for bug 241
|
||
|
|
template<typename MatrixType> void cholesky_bug241(const MatrixType& m)
|
||
|
|
{
|
||
|
|
eigen_assert(m.rows() == 2 && m.cols() == 2);
|
||
|
|
|
||
|
|
typedef typename MatrixType::Scalar Scalar;
|
||
|
|
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
|
||
|
|
|
||
|
|
MatrixType matA;
|
||
|
|
matA << 1, 1, 1, 1;
|
||
|
|
VectorType vecB;
|
||
|
|
vecB << 1, 1;
|
||
|
|
VectorType vecX = matA.ldlt().solve(vecB);
|
||
|
|
VERIFY_IS_APPROX(matA * vecX, vecB);
|
||
|
|
}
|
||
|
|
|
||
|
|
// 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)
|
||
|
|
{
|
||
|
|
eigen_assert(m.rows() == 2 && m.cols() == 2);
|
||
|
|
MatrixType mat;
|
||
|
|
LDLT<MatrixType> ldlt(2);
|
||
|
|
|
||
|
|
{
|
||
|
|
mat << 1, 0, 0, -1;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
VERIFY(ldlt.info()==Success);
|
||
|
|
VERIFY(!ldlt.isNegative());
|
||
|
|
VERIFY(!ldlt.isPositive());
|
||
|
|
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||
|
|
}
|
||
|
|
{
|
||
|
|
mat << 1, 2, 2, 1;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
VERIFY(ldlt.info()==Success);
|
||
|
|
VERIFY(!ldlt.isNegative());
|
||
|
|
VERIFY(!ldlt.isPositive());
|
||
|
|
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||
|
|
}
|
||
|
|
{
|
||
|
|
mat << 0, 0, 0, 0;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
VERIFY(ldlt.info()==Success);
|
||
|
|
VERIFY(ldlt.isNegative());
|
||
|
|
VERIFY(ldlt.isPositive());
|
||
|
|
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||
|
|
}
|
||
|
|
{
|
||
|
|
mat << 0, 0, 0, 1;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
VERIFY(ldlt.info()==Success);
|
||
|
|
VERIFY(!ldlt.isNegative());
|
||
|
|
VERIFY(ldlt.isPositive());
|
||
|
|
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||
|
|
}
|
||
|
|
{
|
||
|
|
mat << -1, 0, 0, 0;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
VERIFY(ldlt.info()==Success);
|
||
|
|
VERIFY(ldlt.isNegative());
|
||
|
|
VERIFY(!ldlt.isPositive());
|
||
|
|
VERIFY_IS_APPROX(mat,ldlt.reconstructedMatrix());
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
template<typename>
|
||
|
|
void cholesky_faillure_cases()
|
||
|
|
{
|
||
|
|
MatrixXd mat;
|
||
|
|
LDLT<MatrixXd> ldlt;
|
||
|
|
|
||
|
|
{
|
||
|
|
mat.resize(2,2);
|
||
|
|
mat << 0, 1, 1, 0;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
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;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
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;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
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;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
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;
|
||
|
|
ldlt.compute(mat);
|
||
|
|
VERIFY(ldlt.info()==NumericalIssue);
|
||
|
|
VERIFY_IS_NOT_APPROX(mat,ldlt.reconstructedMatrix());
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
template<typename MatrixType> void cholesky_verify_assert()
|
||
|
|
{
|
||
|
|
MatrixType tmp;
|
||
|
|
|
||
|
|
LLT<MatrixType> llt;
|
||
|
|
VERIFY_RAISES_ASSERT(llt.matrixL())
|
||
|
|
VERIFY_RAISES_ASSERT(llt.matrixU())
|
||
|
|
VERIFY_RAISES_ASSERT(llt.solve(tmp))
|
||
|
|
VERIFY_RAISES_ASSERT(llt.transpose().solve(tmp))
|
||
|
|
VERIFY_RAISES_ASSERT(llt.adjoint().solve(tmp))
|
||
|
|
VERIFY_RAISES_ASSERT(llt.solveInPlace(tmp))
|
||
|
|
|
||
|
|
LDLT<MatrixType> ldlt;
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.matrixL())
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.transpositionsP())
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.vectorD())
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.isPositive())
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.isNegative())
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.solve(tmp))
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.transpose().solve(tmp))
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.adjoint().solve(tmp))
|
||
|
|
VERIFY_RAISES_ASSERT(ldlt.solveInPlace(tmp))
|
||
|
|
}
|
||
|
|
|
||
|
|
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()) );
|
||
|
|
|
||
|
|
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)) );
|
||
|
|
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||
|
|
}
|
||
|
|
// empty matrix, regression test for Bug 785:
|
||
|
|
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>() );
|
||
|
|
|
||
|
|
// Test problem size constructors
|
||
|
|
CALL_SUBTEST_9( LLT<MatrixXf>(10) );
|
||
|
|
CALL_SUBTEST_9( LDLT<MatrixXf>(10) );
|
||
|
|
|
||
|
|
CALL_SUBTEST_2( cholesky_faillure_cases<void>() );
|
||
|
|
|
||
|
|
TEST_SET_BUT_UNUSED_VARIABLE(nb_temporaries)
|
||
|
|
}
|