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Implement square root for real matrices via Schur.
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@@ -25,16 +25,45 @@
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#include "main.h"
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#include <unsupported/Eigen/MatrixFunctions>
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template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
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struct generateTestMatrix;
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// for real matrices, make sure none of the eigenvalues are negative
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template <typename MatrixType>
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struct generateTestMatrix<MatrixType,0>
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{
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static void run(MatrixType& result, typename MatrixType::Index size)
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{
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MatrixType mat = MatrixType::Random(size, size);
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EigenSolver<MatrixType> es(mat);
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typename EigenSolver<MatrixType>::EigenvalueType eivals = es.eigenvalues();
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for (typename MatrixType::Index i = 0; i < size; ++i) {
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if (eivals(i).imag() == 0 && eivals(i).real() < 0)
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eivals(i) = -eivals(i);
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}
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result = (es.eigenvectors() * eivals.asDiagonal() * es.eigenvectors().inverse()).real();
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}
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};
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// for complex matrices, any matrix is fine
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template <typename MatrixType>
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struct generateTestMatrix<MatrixType,1>
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{
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static void run(MatrixType& result, typename MatrixType::Index size)
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{
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result = MatrixType::Random(size, size);
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}
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};
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template<typename MatrixType>
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void testMatrixSqrt(const MatrixType& m)
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{
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typedef typename MatrixType::Index Index;
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const Index size = m.rows();
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MatrixType A = MatrixType::Random(size, size);
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MatrixType A;
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generateTestMatrix<MatrixType>::run(A, m.rows());
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MatrixSquareRoot<MatrixType> msr(A);
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MatrixType S;
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msr.compute(S);
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VERIFY_IS_APPROX(S*S, A);
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MatrixType sqrtA;
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msr.compute(sqrtA);
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VERIFY_IS_APPROX(sqrtA * sqrtA, A);
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}
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void test_matrix_square_root()
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@@ -42,5 +71,7 @@ void test_matrix_square_root()
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for (int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1(testMatrixSqrt(Matrix3cf()));
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CALL_SUBTEST_2(testMatrixSqrt(MatrixXcd(12,12)));
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CALL_SUBTEST_3(testMatrixSqrt(Matrix4f()));
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CALL_SUBTEST_4(testMatrixSqrt(Matrix<double,Dynamic,Dynamic,RowMajor>(9, 9)));
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}
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}
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