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add sparse * permutation products with assiciated unit tests
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@ -44,6 +44,7 @@ struct Sparse {};
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#include "src/SparseCore/SparseCwiseUnaryOp.h"
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#include "src/SparseCore/SparseCwiseUnaryOp.h"
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#include "src/SparseCore/SparseCwiseBinaryOp.h"
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#include "src/SparseCore/SparseCwiseBinaryOp.h"
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#include "src/SparseCore/SparseDot.h"
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#include "src/SparseCore/SparseDot.h"
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#include "src/SparseCore/SparsePermutation.h"
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#include "src/SparseCore/SparseAssign.h"
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#include "src/SparseCore/SparseAssign.h"
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#include "src/SparseCore/SparseRedux.h"
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#include "src/SparseCore/SparseRedux.h"
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#include "src/SparseCore/SparseFuzzy.h"
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#include "src/SparseCore/SparseFuzzy.h"
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160
Eigen/src/SparseCore/SparsePermutation.h
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160
Eigen/src/SparseCore/SparsePermutation.h
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@ -0,0 +1,160 @@
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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) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#ifndef EIGEN_SPARSE_PERMUTATION_H
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#define EIGEN_SPARSE_PERMUTATION_H
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// This file implements sparse * permutation products
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namespace internal {
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template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
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struct traits<permut_sparsematrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
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{
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typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
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typedef typename MatrixTypeNestedCleaned::Scalar Scalar;
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typedef typename MatrixTypeNestedCleaned::Index Index;
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enum {
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SrcStorageOrder = MatrixTypeNestedCleaned::Flags&RowMajorBit ? RowMajor : ColMajor,
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MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight
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};
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typedef typename internal::conditional<MoveOuter,
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SparseMatrix<Scalar,SrcStorageOrder,Index>,
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SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> >::type ReturnType;
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};
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template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
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struct permut_sparsematrix_product_retval
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: public ReturnByValue<permut_sparsematrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
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{
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typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
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typedef typename MatrixTypeNestedCleaned::Scalar Scalar;
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typedef typename MatrixTypeNestedCleaned::Index Index;
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enum {
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SrcStorageOrder = MatrixTypeNestedCleaned::Flags&RowMajorBit ? RowMajor : ColMajor,
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MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight
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};
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permut_sparsematrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
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: m_permutation(perm), m_matrix(matrix)
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{}
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inline int rows() const { return m_matrix.rows(); }
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inline int cols() const { return m_matrix.cols(); }
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template<typename Dest> inline void evalTo(Dest& dst) const
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{
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if(MoveOuter)
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{
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SparseMatrix<Scalar,SrcStorageOrder,Index> tmp(m_matrix.rows(), m_matrix.cols());
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VectorXi sizes(m_matrix.outerSize());
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for(Index j=0; j<m_matrix.outerSize(); ++j)
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{
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Index jp = m_permutation.indices().coeff(j);
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sizes[((Side==OnTheLeft) ^ Transposed) ? jp : j] = m_matrix.innerVector(((Side==OnTheRight) ^ Transposed) ? jp : j).size();
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}
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tmp.reserve(sizes);
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for(Index j=0; j<m_matrix.outerSize(); ++j)
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{
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Index jp = m_permutation.indices().coeff(j);
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Index jsrc = ((Side==OnTheRight) ^ Transposed) ? jp : j;
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Index jdst = ((Side==OnTheLeft) ^ Transposed) ? jp : j;
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for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,jsrc); it; ++it)
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tmp.insertByOuterInner(jdst,it.index()) = it.value();
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}
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dst = tmp;
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}
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else
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{
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SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> tmp(m_matrix.rows(), m_matrix.cols());
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VectorXi sizes(tmp.outerSize());
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sizes.setZero();
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PermutationMatrix<Dynamic,Dynamic,Index> perm;
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if((Side==OnTheLeft) ^ Transposed)
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perm = m_permutation;
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else
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perm = m_permutation.transpose();
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for(Index j=0; j<m_matrix.outerSize(); ++j)
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for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,j); it; ++it)
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sizes[perm.indices().coeff(it.index())]++;
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tmp.reserve(sizes);
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for(Index j=0; j<m_matrix.outerSize(); ++j)
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for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,j); it; ++it)
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tmp.insertByOuterInner(perm.indices().coeff(it.index()),j) = it.value();
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dst = tmp;
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}
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}
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protected:
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const PermutationType& m_permutation;
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typename MatrixType::Nested m_matrix;
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};
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}
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/** \returns the matrix with the permutation applied to the columns
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*/
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template<typename SparseDerived, typename PermDerived>
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inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, false>
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operator*(const SparseMatrixBase<SparseDerived>& matrix, const PermutationBase<PermDerived>& perm)
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{
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return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, false>(perm, matrix.derived());
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}
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/** \returns the matrix with the permutation applied to the rows
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*/
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template<typename SparseDerived, typename PermDerived>
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inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, false>
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operator*( const PermutationBase<PermDerived>& perm, const SparseMatrixBase<SparseDerived>& matrix)
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{
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return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, false>(perm, matrix.derived());
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}
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/** \returns the matrix with the inverse permutation applied to the columns.
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*/
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template<typename SparseDerived, typename PermDerived>
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inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, true>
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operator*(const SparseMatrixBase<SparseDerived>& matrix, const Transpose<PermutationBase<PermDerived> >& tperm)
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{
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return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, true>(tperm.nestedPermutation(), matrix.derived());
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}
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/** \returns the matrix with the inverse permutation applied to the rows.
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*/
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template<typename SparseDerived, typename PermDerived>
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inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, true>
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operator*(const Transpose<PermutationBase<PermDerived> >& tperm, const SparseMatrixBase<SparseDerived>& matrix)
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{
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return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, true>(tperm.nestedPermutation(), matrix.derived());
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}
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#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
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@ -59,6 +59,26 @@ template<int OtherStorage, typename SparseMatrixType> void sparse_permutations(c
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randomPermutationVector(pi, cols);
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randomPermutationVector(pi, cols);
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p.indices() = pi;
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p.indices() = pi;
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res = mat*p;
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res_d = mat_d*p;
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VERIFY(res.isApprox(res_d) && "mat*p");
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res = p*mat;
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res_d = p*mat_d;
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VERIFY(res.isApprox(res_d) && "p*mat");
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res = mat*p.inverse();
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res_d = mat*p.inverse();
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VERIFY(res.isApprox(res_d) && "mat*inv(p)");
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res = p.inverse()*mat;
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res_d = p.inverse()*mat_d;
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VERIFY(res.isApprox(res_d) && "inv(p)*mat");
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res = mat.twistedBy(p);
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res_d = (p * mat_d) * p.inverse();
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VERIFY(res.isApprox(res_d) && "p*mat*inv(p)");
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res = mat.template selfadjointView<Upper>().twistedBy(p_null);
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res = mat.template selfadjointView<Upper>().twistedBy(p_null);
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res_d = up_sym_d;
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res_d = up_sym_d;
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@ -76,6 +96,41 @@ template<int OtherStorage, typename SparseMatrixType> void sparse_permutations(c
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res = lo.template selfadjointView<Lower>().twistedBy(p_null);
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res = lo.template selfadjointView<Lower>().twistedBy(p_null);
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res_d = lo_sym_d;
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res_d = lo_sym_d;
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VERIFY(res.isApprox(res_d) && "lower selfadjoint full");
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VERIFY(res.isApprox(res_d) && "lower selfadjoint full");
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res = mat.template selfadjointView<Upper>();
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res_d = up_sym_d;
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VERIFY(res.isApprox(res_d) && "full selfadjoint upper to full");
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res = mat.template selfadjointView<Lower>();
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res_d = lo_sym_d;
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VERIFY(res.isApprox(res_d) && "full selfadjoint lower to full");
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res = up.template selfadjointView<Upper>();
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res_d = up_sym_d;
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VERIFY(res.isApprox(res_d) && "upper selfadjoint to full");
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res = lo.template selfadjointView<Lower>();
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res_d = lo_sym_d;
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VERIFY(res.isApprox(res_d) && "lower selfadjoint full");
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res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>();
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res_d = up_sym_d.template triangularView<Upper>();
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VERIFY(res.isApprox(res_d) && "full selfadjoint upper to upper");
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res.template selfadjointView<Lower>() = mat.template selfadjointView<Upper>();
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res_d = up_sym_d.template triangularView<Lower>();
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VERIFY(res.isApprox(res_d) && "full selfadjoint upper to lower");
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res.template selfadjointView<Upper>() = mat.template selfadjointView<Lower>();
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res_d = lo_sym_d.template triangularView<Upper>();
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VERIFY(res.isApprox(res_d) && "full selfadjoint lower to upper");
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res.template selfadjointView<Lower>() = mat.template selfadjointView<Lower>();
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res_d = lo_sym_d.template triangularView<Lower>();
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VERIFY(res.isApprox(res_d) && "full selfadjoint lower to lower");
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res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>().twistedBy(p);
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res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>().twistedBy(p);
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