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https://github.com/triqs/dft_tools
synced 2024-12-25 22:03:43 +01:00
arrays: clean inverse
- does not use view - determinant not lazy any more
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800aea4c5e
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@ -76,7 +76,7 @@ int main(int argc, char **argv) {
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assert ( (abs(A(i,j) - Wkeep(i,j))) <1.e-10 );
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double det = determinant(W);
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std::cout<<"determinant "<<determinant(W)<< " = "<< det<< std::endl<<std::endl;
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std::cout<<"determinant"<< " = "<< det<< std::endl<<std::endl;
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//
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matrix_view<double> V(W(range(0,3,2), range(0,3,2)));
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@ -34,10 +34,7 @@ inverse(
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[0.25641,-0.512821,0.25641]
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[-0.628205,0.25641,-0.128205]]
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determinant determinant(
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[[-0.702564,1.20513,-0.102564]
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[0.25641,-0.512821,0.25641]
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[-0.628205,0.25641,-0.128205]]) = -0.128205
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determinant = -0.128205
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view =
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[[-0.702564,-0.102564]
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@ -54,7 +54,7 @@ struct test {
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D.inverse_matrix() << D.matrix() << triqs::arrays::matrix<std::complex<double>>(inverse(D.matrix()))<< std::endl;
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std::cerr << "det_old = " << det_old << "detratio = "<< detratio<< " determin "<< D.determinant() <<std::endl;
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#endif
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assert_close(D.determinant() , long(1)/determinant(D.inverse_matrix())(), PRECISION);
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assert_close(D.determinant() , long(1)/determinant(D.inverse_matrix()), PRECISION);
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triqs::arrays::assert_all_close( inverse(D.matrix()) , D.inverse_matrix(), PRECISION, true);
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assert_close( det_old * detratio , D.determinant(), PRECISION);
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}
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@ -55,7 +55,7 @@ struct test {
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D.inverse_matrix() << D.matrix() << triqs::arrays::matrix<std::complex<double>>(inverse(D.matrix()))<< std::endl;
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std::cerr << "det_old = " << det_old << "detratio = "<< detratio<< " determin "<< D.determinant() <<std::endl;
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#endif
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assert_close(D.determinant() , long(1)/determinant(D.inverse_matrix())(), PRECISION);
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assert_close(D.determinant() , long(1)/determinant(D.inverse_matrix()), PRECISION);
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triqs::arrays::assert_all_close( inverse(D.matrix()) , D.inverse_matrix(), PRECISION, true);
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assert_close( det_old * detratio , D.determinant(), PRECISION);
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}
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@ -72,8 +72,7 @@ int main() {
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TEST( tql::eval(Gv(om_), om_=0) ) ;
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// tail
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BOOST_AUTO( t, G(inf));
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//local::gf<meshes::tail> t2 = t + 2.4;
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auto t = G(inf);
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TEST(t.order_min());
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TEST( t( 2) ) ;
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@ -20,16 +20,13 @@
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******************************************************************************/
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#ifndef TRIQS_ARRAYS_LINALG_DET_INV_H
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#define TRIQS_ARRAYS_LINALG_DET_INV_H
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#include <boost/type_traits/is_same.hpp>
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#include <boost/typeof/typeof.hpp>
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#include <boost/utility/enable_if.hpp>
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#include "../impl/common.hpp"
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#include "../matrix.hpp"
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#include "../blas_lapack/getrf.hpp"
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#include "../blas_lapack/getri.hpp"
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namespace triqs { namespace arrays {
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namespace triqs {
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namespace arrays {
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/// Error which occurs during the matrix inversion
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class matrix_inverse_exception : public triqs::runtime_error {};
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@ -42,154 +39,163 @@ namespace triqs { namespace arrays {
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* It can be copied at no cost
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* It keeps view of the object A if it a matrix, a copy if it is a formal expression.
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*/
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template<typename A, class Enable = void> struct inverse_lazy;
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template<typename A> struct inverse_lazy_impl;//debug ony
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template <typename A> struct inverse_lazy;
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///
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template<typename A> struct determinant_lazy;
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/// Lazy inversion
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template<class A> inverse_lazy<typename utility::remove_rvalue_ref<A>::type> inverse (A && a) { return {std::forward<A>(a)}; }
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/// Lazy computation of det
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template<typename A> determinant_lazy<A> determinant (A const & a) { return determinant_lazy<A>(a); }
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template <class A> inverse_lazy<typename utility::remove_rvalue_ref<A>::type> inverse(A &&a) {
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return {std::forward<A>(a)};
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}
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// ----------------- implementation -----------------------------------------
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//worker takes a contiguous view and compute the det and inverse in two steps.
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//it is separated in case of multiple use (no reallocation of ipvi, etc...)
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//NB a view does not resize, only its elements can be changed
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template<typename ViewType> class det_and_inverse_worker {
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static_assert ( (is_matrix_view<ViewType>::value),"class must have be a view");
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typedef typename ViewType::value_type VT;
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typedef matrix_view<VT> V_type;
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ViewType V;
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const size_t dim;
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triqs::arrays::vector <int> ipiv;
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short step;
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// worker takes a contiguous view and compute the det and inverse in two steps.
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// it is separated in case of multiple use (no reallocation of ipvi, etc...)
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// A can be a matrix, a matrix_view
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template <typename A> class det_and_inverse_worker {
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typedef typename A::value_type value_type;
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typedef matrix_view<value_type> V_type;
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A a;
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int dim;
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triqs::arrays::vector<int> ipiv;
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int step, info;
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value_type _det;
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public:
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det_and_inverse_worker (ViewType const & a): V(a), dim(first_dim(a)), ipiv(dim), step(0) {
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if (first_dim(a)!=second_dim(a))
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TRIQS_RUNTIME_ERROR<<"Inverse/Det error : non-square matrix. Dimensions are : ("<<first_dim(a)<<","<<second_dim(a)<<")"<<"\n ";
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if (!(has_contiguous_data(a))) TRIQS_RUNTIME_ERROR<<"det_and_inverse_worker only takes a contiguous view";
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det_and_inverse_worker(A a_) : a(std::move(a_)), dim(first_dim(a)), ipiv(dim), step(0) {
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if (first_dim(a) != second_dim(a))
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TRIQS_RUNTIME_ERROR << "Inverse/Det error:non-square matrix. Dimensions are :(" << first_dim(a) << "," << second_dim(a)
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<< ")\n ";
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if (!(has_contiguous_data(a))) TRIQS_RUNTIME_ERROR << "det_and_inverse_worker only takes a contiguous view";
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}
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value_type det() {
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V_type W = fortran_view(a);
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_step1(W);
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_compute_det(W);
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return _det;
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}
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A const &inverse() {
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if (step < 2) {
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V_type W = fortran_view(a);
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_step1(W);
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_step2(W);
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}
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return a;
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}
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VT det() { V_type W = fortran_view(V); _step1(W); _compute_det(W); return _det;}
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ViewType const & inverse() { if (step<2) { V_type W = fortran_view(V); _step1(W); _step2(W);} return V;}
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private:
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int info; VT _det;
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// no need of special traversal
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template<typename MT> V_type fortran_view (MT const &x) { return (x.indexmap().memory_layout_is_c() ? x.transpose() : x);}
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template <typename MT> V_type fortran_view(MT const &x) {
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if (x.indexmap().memory_layout_is_c())
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return x.transpose();
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else
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return x;
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}
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void _step1(V_type & W) {
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if (step >0) return;
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step=1;
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void _step1(V_type &W) {
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if (step > 0) return;
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step = 1;
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info = lapack::getrf(W, ipiv);
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if (info<0) throw matrix_inverse_exception() << "Inverse/Det error : failure of getrf lapack routine ";
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if (info < 0) throw matrix_inverse_exception() << "Inverse/Det error : failure of getrf lapack routine ";
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}
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void _compute_det(V_type const & W) {
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if (step>1) return;
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_det =1;
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for (size_t i =0; i<dim; i++) _det *= W(i,i);
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bool flip=false;// compute the sign of the permutation
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for (size_t i=0; i<dim; i++) {if (ipiv(i)!=int(i)+1) flip = !(flip);}
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_det= (flip ? - _det : _det) ;
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void _compute_det(V_type const &W) {
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if (step > 1) return;
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_det = 1;
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for (size_t i = 0; i < dim; i++) _det *= W(i, i);
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bool flip = false; // compute the sign of the permutation
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for (size_t i = 0; i < dim; i++) {
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if (ipiv(i) != int(i) + 1) flip = !(flip);
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}
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_det = (flip ? -_det : _det);
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}
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void _step2(V_type & W) {
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assert(step==1); //if (step==1) return;
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step=2;
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void _step2(V_type &W) {
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assert(step == 1); // if (step==1) return;
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step = 2;
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_compute_det(W);
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info = lapack::getri(W, ipiv);
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if (info!=0) throw matrix_inverse_exception() << "Inverse/Det error : matrix is not invertible";
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if (info != 0) throw matrix_inverse_exception() << "Inverse/Det error : matrix is not invertible";
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}
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};
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//-----------------------------------------------------------
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// an implementation class to gather the common part to matrix and expression....
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template<typename A> struct inverse_lazy_impl : TRIQS_CONCEPT_TAG_NAME(ImmutableMatrix) {
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public:
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template <typename A> struct inverse_lazy : TRIQS_CONCEPT_TAG_NAME(ImmutableMatrix) {
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typedef typename std::remove_reference<A>::type A_t;
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typedef typename std::remove_const<typename A_t::value_type>::type value_type;
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typedef typename A_t::domain_type domain_type;
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A a;
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template<typename AA> inverse_lazy_impl(AA && a_):a (std::forward<AA>(a_)) {
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if (first_dim(a) != second_dim(a)) TRIQS_RUNTIME_ERROR<< "Inverse : matrix is not square but of size "<< first_dim(a)<<" x "<< second_dim(a);
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}
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domain_type domain() const { return a.domain(); }
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template<typename K0, typename K1> value_type operator() (K0 const & k0, K1 const & k1) const { activate(); return _id->M(k0,k1); }
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friend std::ostream & operator<<(std::ostream & out,inverse_lazy_impl const&x){return out<<"inverse("<<x.a<<")";}
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protected:
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struct internal_data { // implementing the pattern LazyPreCompute
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typedef matrix<value_type> M_type;
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typedef matrix_view<value_type> M_view_type;
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M_type M;
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internal_data(inverse_lazy_impl const & P):M(P.a){
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det_and_inverse_worker<M_view_type> worker(M);
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template <typename AA> inverse_lazy(AA &&a_) : a(std::forward<AA>(a_)), M{}, computed{false} {
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if (first_dim(a) != second_dim(a))
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TRIQS_RUNTIME_ERROR << "Inverse : matrix is not square but of size " << first_dim(a) << " x " << second_dim(a);
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}
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domain_type domain() const { return a.domain(); }
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A const & input() const { return a;}
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template <typename K0, typename K1> value_type const &operator()(K0 const &k0, K1 const &k1) const {
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activate();
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return M(k0, k1);
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}
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M_type const &operator()() const {
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activate();
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return M;
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}
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friend std::ostream &operator<<(std::ostream &out, inverse_lazy const &x) { return out << "inverse(" << x.a << ")"; }
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private:
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A a;
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mutable M_type M;
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mutable bool computed;
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void activate() const {
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if (computed) return;
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M = a;
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auto worker = det_and_inverse_worker<M_view_type> {M};
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worker.inverse();
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computed = true;
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}
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};
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friend struct internal_data;
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mutable std::shared_ptr<internal_data> _id;
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void activate() const { if (!_id) _id= std::make_shared<internal_data>(*this);}
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};
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// The general case
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template<typename A>
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struct inverse_lazy<A, DISABLE_IF(is_matrix_or_view<typename std::remove_reference<A>::type>) > : inverse_lazy_impl<A> {
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template<typename AA> inverse_lazy(AA && a_):inverse_lazy_impl<A>(std::forward<AA>(a_)) {}
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};
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// for matrix and matrix_views, we have more optimisation possible ....
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template<typename A>
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struct inverse_lazy<A, ENABLE_IF(is_matrix_or_view<typename std::remove_reference<A>::type>) >:inverse_lazy_impl<A>{
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template<typename AA> inverse_lazy(AA && a_):inverse_lazy_impl<A>(std::forward<AA>(a_)) {}
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template<typename MT> // Optimized implementation of =
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friend void triqs_arrays_assign_delegation (MT & lhs, inverse_lazy const & rhs) {
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//std::cerr << " DELEGATING1"<< lhs <<std::endl;
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std::cerr << " DELEGATING2"<< rhs << rhs.a<<std::endl;
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static_assert(is_matrix_or_view<MT>::value, "Internal error");
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//std::cerr << " DELEGATING"<< lhs << std::endl;
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if ((lhs.indexmap().memory_indices_layout() !=rhs.a.indexmap().memory_indices_layout())||
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(lhs.data_start() != rhs.a.data_start()) || !(has_contiguous_data(lhs))) { rhs.activate(); lhs = rhs._id->M;}
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else {// if M = inverse(M) with the SAME object, then we do not need to copy the data
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//std::cerr << " DELEGATING"<< lhs << std::endl;
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blas_lapack_tools::reflexive_qcache<MT> C(lhs);// a reflexive cache will use a temporary "regrouping" copy if and only if needed
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det_and_inverse_worker<typename MT::view_type> W(C());// the worker to make the inversion of the lhs...
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// Optimized implementation of =
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// if M = inverse(M) with the SAME object, then we do not need to copy the data
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template <typename A, typename MT>
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ENABLE_IF(is_matrix_or_view<typename std::remove_reference<A>::type>)
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triqs_arrays_assign_delegation(MT &lhs, inverse_lazy<A> const &rhs) {
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static_assert(is_matrix_or_view<MT>::value, "Can only assign an inverse matrix to a matrix or a matrix_view");
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bool M_eq_inverse_M = ((lhs.indexmap().memory_indices_layout() == rhs.input().indexmap().memory_indices_layout()) &&
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(lhs.data_start() == rhs.input().data_start()) && (has_contiguous_data(lhs)));
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if (!M_eq_inverse_M) {
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lhs = rhs();
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} else {
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blas_lapack_tools::reflexive_qcache<MT> C(lhs);
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// a reflexive cache will use a temporary "regrouping" copy if and only if needed
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det_and_inverse_worker<typename MT::view_type> W(C()); // the worker to make the inversion of the lhs...
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W.inverse(); // worker is working ...
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//std::cerr << " DELEGATING"<< lhs << std::endl;
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}
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//std::cerr << " okok "<< lhs << std::endl;
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}
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friend std::ostream & operator<<(std::ostream & out,inverse_lazy const&x){return out<<"inverse("<<x.a<<")";}
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};
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//------------------- det ----------------------------------------
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template<typename A> struct determinant_lazy {
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typedef typename A::value_type value_type;
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typedef typename const_view_type_if_exists_else_type<A>::type A_type;
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A_type a;
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determinant_lazy(A const & a_):a(a_){}
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operator value_type() { activate(); return _id->det; }
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value_type const & operator()() { activate(); return _id->det; }
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friend std::ostream & operator<<(std::ostream & out, determinant_lazy const & x){ return out<<"determinant("<<x.a<<")";}
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protected:
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struct internal_data {
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typedef typename A_type::regular_type M_type;
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M_type M; typename A::value_type det;
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internal_data(determinant_lazy const & P):M(P.a){det_and_inverse_worker<A_type> worker(M); det = worker.det();}
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};
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friend struct internal_data;
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mutable std::shared_ptr<internal_data> _id;
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void activate() const { if (!_id) _id= std::make_shared<internal_data>(*this);}
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};
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template <typename A> typename std::remove_reference<A>::type::value_type determinant(A &&a) {
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// makes a temporary copy of A if A is a const &
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// If a is a matrix &&, it is moved into the worker.
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auto worker = det_and_inverse_worker<matrix<typename std::remove_reference<A>::type::value_type>>(std::forward<A>(a));
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return worker.det();
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}
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}
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}} // namespace triqs::arrays
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#ifndef TRIQS_COMPILER_OBSOLETE_GCC
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namespace clef {
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TRIQS_CLEF_MAKE_FNT_LAZY(determinant);
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}
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#endif
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} // namespace triqs::arrays
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#endif
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