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Remove random retry loops in tests (batch 3: geometry, sparse, umeyama)
libeigen/eigen!2262 Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
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@@ -13,6 +13,7 @@
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#include <Eigen/Geometry>
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#include <Eigen/LU> // required for MatrixBase::determinant
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#include <Eigen/QR> // required for HouseholderQR
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#include <Eigen/SVD> // required for SVD
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using namespace Eigen;
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@@ -23,43 +24,9 @@ Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> randMatrixUnitary(int size) {
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typedef T Scalar;
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typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixType;
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MatrixType Q;
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int max_tries = 40;
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bool is_unitary = false;
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while (!is_unitary && max_tries > 0) {
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// initialize random matrix
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Q = MatrixType::Random(size, size);
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// orthogonalize columns using the Gram-Schmidt algorithm
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for (int col = 0; col < size; ++col) {
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typename MatrixType::ColXpr colVec = Q.col(col);
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for (int prevCol = 0; prevCol < col; ++prevCol) {
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typename MatrixType::ColXpr prevColVec = Q.col(prevCol);
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colVec -= colVec.dot(prevColVec) * prevColVec;
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}
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Q.col(col) = colVec.normalized();
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}
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// this additional orthogonalization is not necessary in theory but should enhance
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// the numerical orthogonality of the matrix
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for (int row = 0; row < size; ++row) {
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typename MatrixType::RowXpr rowVec = Q.row(row);
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for (int prevRow = 0; prevRow < row; ++prevRow) {
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typename MatrixType::RowXpr prevRowVec = Q.row(prevRow);
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rowVec -= rowVec.dot(prevRowVec) * prevRowVec;
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}
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Q.row(row) = rowVec.normalized();
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}
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// final check
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is_unitary = Q.isUnitary();
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--max_tries;
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}
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if (max_tries == 0) eigen_assert(false && "randMatrixUnitary: Could not construct unitary matrix!");
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// The Q factor of the QR decomposition of a random matrix is a random unitary matrix.
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// HouseholderQR is numerically stable and always succeeds, unlike Gram-Schmidt.
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MatrixType Q = MatrixType::Random(size, size).householderQr().householderQ();
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return Q;
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}
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