Mercurial > hg > octave-lyh
view src/DLD-FUNCTIONS/qr.cc @ 12329:5f203b5bbf98
Use testif to only run some sparse tests when necessary libraries are installed.
author | Rik <octave@nomad.inbox5.com> |
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date | Wed, 02 Feb 2011 21:31:33 -0800 |
parents | 12df7854fa7c |
children | f96b9b9f141b |
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/* Copyright (C) 1996-2011 John W. Eaton Copyright (C) 2008-2009 Jaroslav Hajek Copyright (C) 2008-2009 VZLU Prague This file is part of Octave. Octave is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. Octave is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with Octave; see the file COPYING. If not, see <http://www.gnu.org/licenses/>. */ #ifdef HAVE_CONFIG_H #include <config.h> #endif #include "CmplxQR.h" #include "CmplxQRP.h" #include "dbleQR.h" #include "dbleQRP.h" #include "fCmplxQR.h" #include "fCmplxQRP.h" #include "floatQR.h" #include "floatQRP.h" #include "SparseQR.h" #include "SparseCmplxQR.h" #include "defun-dld.h" #include "error.h" #include "gripes.h" #include "oct-obj.h" #include "utils.h" template <class MT> static octave_value get_qr_r (const base_qr<MT>& fact) { MT R = fact.R (); if (R.is_square () && fact.regular ()) return octave_value (R, MatrixType (MatrixType::Upper)); else return R; } // [Q, R] = qr (X): form Q unitary and R upper triangular such // that Q * R = X // // [Q, R] = qr (X, 0): form the economy decomposition such that if X is // m by n then only the first n columns of Q are // computed. // // [Q, R, P] = qr (X): form QRP factorization of X where // P is a permutation matrix such that // A * P = Q * R // // [Q, R, P] = qr (X, 0): form the economy decomposition with // permutation vector P such that Q * R = X (:, P) // // qr (X) alone returns the output of the LAPACK routine dgeqrf, such // that R = triu (qr (X)) DEFUN_DLD (qr, args, nargout, "-*- texinfo -*-\n\ @deftypefn {Loadable Function} {[@var{Q}, @var{R}, @var{P}] =} qr (@var{A})\n\ @deftypefnx {Loadable Function} {[@var{Q}, @var{R}, @var{P}] =} qr (@var{A}, '0')\n\ @cindex QR factorization\n\ Compute the QR@tie{}factorization of @var{A}, using standard @sc{lapack}\n\ subroutines. For example, given the matrix @code{@var{A} = [1, 2; 3, 4]},\n\ \n\ @example\n\ [@var{Q}, @var{R}] = qr (@var{A})\n\ @end example\n\ \n\ @noindent\n\ returns\n\ \n\ @example\n\ @group\n\ @var{Q} =\n\ \n\ -0.31623 -0.94868\n\ -0.94868 0.31623\n\ \n\ @var{R} =\n\ \n\ -3.16228 -4.42719\n\ 0.00000 -0.63246\n\ @end group\n\ @end example\n\ \n\ The @code{qr} factorization has applications in the solution of least\n\ squares problems\n\ @tex\n\ $$\n\ \\min_x \\left\\Vert A x - b \\right\\Vert_2\n\ $$\n\ @end tex\n\ @ifnottex\n\ \n\ @example\n\ @code{min norm(A x - b)}\n\ @end example\n\ \n\ @end ifnottex\n\ for overdetermined systems of equations (i.e.,\n\ @tex\n\ $A$\n\ @end tex\n\ @ifnottex\n\ @var{A}\n\ @end ifnottex\n\ is a tall, thin matrix). The QR@tie{}factorization is\n\ @tex\n\ $QR = A$ where $Q$ is an orthogonal matrix and $R$ is upper triangular.\n\ @end tex\n\ @ifnottex\n\ @code{@var{Q} * @var{Q} = @var{A}} where @var{Q} is an orthogonal matrix and\n\ @var{R} is upper triangular.\n\ @end ifnottex\n\ \n\ If given a second argument of '0', @code{qr} returns an economy-sized\n\ QR@tie{}factorization, omitting zero rows of @var{R} and the corresponding\n\ columns of @var{Q}.\n\ \n\ If the matrix @var{A} is full, the permuted QR@tie{}factorization\n\ @code{[@var{Q}, @var{R}, @var{P}] = qr (@var{A})} forms the\n\ QR@tie{}factorization such that the diagonal entries of @var{R} are\n\ decreasing in magnitude order. For example, given the matrix @code{a = [1,\n\ 2; 3, 4]},\n\ \n\ @example\n\ [@var{Q}, @var{R}, @var{P}] = qr (@var{A})\n\ @end example\n\ \n\ @noindent\n\ returns\n\ \n\ @example\n\ @group\n\ @var{Q} = \n\ \n\ -0.44721 -0.89443\n\ -0.89443 0.44721\n\ \n\ @var{R} =\n\ \n\ -4.47214 -3.13050\n\ 0.00000 0.44721\n\ \n\ @var{P} =\n\ \n\ 0 1\n\ 1 0\n\ @end group\n\ @end example\n\ \n\ The permuted @code{qr} factorization @code{[@var{Q}, @var{R}, @var{P}] = qr\n\ (@var{A})} factorization allows the construction of an orthogonal basis of\n\ @code{span (A)}.\n\ \n\ If the matrix @var{A} is sparse, then compute the sparse\n\ QR@tie{}factorization of @var{A}, using @sc{CSparse}. As the matrix @var{Q}\n\ is in general a full matrix, this function returns the @var{Q}-less\n\ factorization @var{R} of @var{A}, such that @code{@var{R} = chol (@var{A}' *\n\ @var{A})}.\n\ \n\ If the final argument is the scalar @code{0} and the number of rows is\n\ larger than the number of columns, then an economy factorization is\n\ returned. That is @var{R} will have only @code{size (@var{A},1)} rows.\n\ \n\ If an additional matrix @var{B} is supplied, then @code{qr} returns\n\ @var{C}, where @code{@var{C} = @var{Q}' * @var{B}}. This allows the\n\ least squares approximation of @code{@var{A} \\ @var{B}} to be calculated\n\ as\n\ \n\ @example\n\ @group\n\ [@var{C},@var{R}] = spqr (@var{A},@var{B})\n\ x = @var{R} \\ @var{C}\n\ @end group\n\ @end example\n\ @end deftypefn") { octave_value_list retval; int nargin = args.length (); if (nargin < 1 || nargin > (args(0).is_sparse_type() ? 3 : 2)) { print_usage (); return retval; } octave_value arg = args(0); int arg_is_empty = empty_arg ("qr", arg.rows (), arg.columns ()); if (arg_is_empty < 0) return retval; if (arg.is_sparse_type ()) { bool economy = false; bool is_cmplx = false; int have_b = 0; if (arg.is_complex_type ()) is_cmplx = true; if (nargin > 1) { have_b = 1; if (args(nargin-1).is_scalar_type ()) { int val = args(nargin-1).int_value (); if (val == 0) { economy = true; have_b = (nargin > 2 ? 2 : 0); } } if (have_b > 0 && args(have_b).is_complex_type ()) is_cmplx = true; } if (!error_state) { if (have_b && nargout < 2) error ("qr: incorrect number of output arguments"); else if (is_cmplx) { SparseComplexQR q (arg.sparse_complex_matrix_value ()); if (!error_state) { if (have_b > 0) { retval(1) = q.R (economy); retval(0) = q.C (args(have_b).complex_matrix_value ()); if (arg.rows() < arg.columns()) warning ("qr: non minimum norm solution for under-determined problem"); } else if (nargout > 1) { retval(1) = q.R (economy); retval(0) = q.Q (); } else retval(0) = q.R (economy); } } else { SparseQR q (arg.sparse_matrix_value ()); if (!error_state) { if (have_b > 0) { retval(1) = q.R (economy); retval(0) = q.C (args(have_b).matrix_value ()); if (args(0).rows() < args(0).columns()) warning ("qr: non minimum norm solution for under-determined problem"); } else if (nargout > 1) { retval(1) = q.R (economy); retval(0) = q.Q (); } else retval(0) = q.R (economy); } } } } else { QR::type type = (nargout == 0 || nargout == 1) ? QR::raw : (nargin == 2 ? QR::economy : QR::std); if (arg.is_single_type ()) { if (arg.is_real_type ()) { FloatMatrix m = arg.float_matrix_value (); if (! error_state) { switch (nargout) { case 0: case 1: { FloatQR fact (m, type); retval(0) = fact.R (); } break; case 2: { FloatQR fact (m, type); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } break; default: { FloatQRP fact (m, type); if (type == QR::economy) retval(2) = fact.Pvec (); else retval(2) = fact.P (); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } break; } } } else if (arg.is_complex_type ()) { FloatComplexMatrix m = arg.float_complex_matrix_value (); if (! error_state) { switch (nargout) { case 0: case 1: { FloatComplexQR fact (m, type); retval(0) = fact.R (); } break; case 2: { FloatComplexQR fact (m, type); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } break; default: { FloatComplexQRP fact (m, type); if (type == QR::economy) retval(2) = fact.Pvec (); else retval(2) = fact.P (); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } break; } } } } else { if (arg.is_real_type ()) { Matrix m = arg.matrix_value (); if (! error_state) { switch (nargout) { case 0: case 1: { QR fact (m, type); retval(0) = fact.R (); } break; case 2: { QR fact (m, type); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } break; default: { QRP fact (m, type); if (type == QR::economy) retval(2) = fact.Pvec (); else retval(2) = fact.P (); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } break; } } } else if (arg.is_complex_type ()) { ComplexMatrix m = arg.complex_matrix_value (); if (! error_state) { switch (nargout) { case 0: case 1: { ComplexQR fact (m, type); retval(0) = fact.R (); } break; case 2: { ComplexQR fact (m, type); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } break; default: { ComplexQRP fact (m, type); if (type == QR::economy) retval(2) = fact.Pvec (); else retval(2) = fact.P (); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } break; } } } else gripe_wrong_type_arg ("qr", arg); } } return retval; } /* %!test %! a = [0, 2, 1; 2, 1, 2]; %! %! [q, r] = qr (a); %! %! [qe, re] = qr (a, 0); %! %! assert (q * r, a, sqrt (eps)); %! assert (qe * re, a, sqrt (eps)); %!test %! a = [0, 2, 1; 2, 1, 2]; %! %! [q, r, p] = qr (a); # not giving right dimensions. FIXME %! %! [qe, re, pe] = qr (a, 0); %! %! assert (q * r, a * p, sqrt (eps)); %! assert (qe * re, a(:, pe), sqrt (eps)); %!test %! a = [0, 2; 2, 1; 1, 2]; %! %! [q, r] = qr (a); %! %! [qe, re] = qr (a, 0); %! %! assert (q * r, a, sqrt (eps)); %! assert (qe * re, a, sqrt (eps)); %!test %! a = [0, 2; 2, 1; 1, 2]; %! %! [q, r, p] = qr (a); %! %! [qe, re, pe] = qr (a, 0); %! %! assert (q * r, a * p, sqrt (eps)); %! assert (qe * re, a(:, pe), sqrt (eps)); %!error <Invalid call to qr.*> qr (); %!error <Invalid call to qr.*> qr ([1, 2; 3, 4], 0, 2); %!function retval = testqr (q, r, a, p) %! tol = 100*eps (class(q)); %! retval = 0; %! if (nargin == 3) %! n1 = norm (q*r-a); %! n2 = norm (q'*q-eye(columns(q))); %! retval = (n1 < tol && n2 < tol); %! else %! n1 = norm (q'*q-eye(columns(q))); %! retval = (n1 < tol); %! if (isvector (p)) %! n2 = norm (q*r-a(:,p)); %! retval = (retval && n2 < tol); %! else %! n2 = norm (q*r - a*p); %! retval = (retval && n2 < tol); %! endif %! endif %!test %! %! t = ones (24, 1); %! j = 1; %! %! if false # eliminate big matrix tests %! a = rand(5000,20); %! [q,r]=qr(a,0); t(j++) = testqr(q,r,a); %! [q,r]=qr(a',0); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a,0); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a',0); t(j++) = testqr(q,r,a',p); %! %! a = a+1i*eps; %! [q,r]=qr(a,0); t(j++) = testqr(q,r,a); %! [q,r]=qr(a',0); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a,0); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a',0); t(j++) = testqr(q,r,a',p); %! endif %! %! a = [ ones(1,15); sqrt(eps)*eye(15) ]; %! [q,r]=qr(a); t(j++) = testqr(q,r,a); %! [q,r]=qr(a'); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a'); t(j++) = testqr(q,r,a',p); %! %! a = a+1i*eps; %! [q,r]=qr(a); t(j++) = testqr(q,r,a); %! [q,r]=qr(a'); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a'); t(j++) = testqr(q,r,a',p); %! %! a = [ ones(1,15); sqrt(eps)*eye(15) ]; %! [q,r]=qr(a,0); t(j++) = testqr(q,r,a); %! [q,r]=qr(a',0); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a,0); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a',0); t(j++) = testqr(q,r,a',p); %! %! a = a+1i*eps; %! [q,r]=qr(a,0); t(j++) = testqr(q,r,a); %! [q,r]=qr(a',0); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a,0); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a',0); t(j++) = testqr(q,r,a',p); %! %! a = [ %! 611 196 -192 407 -8 -52 -49 29 %! 196 899 113 -192 -71 -43 -8 -44 %! -192 113 899 196 61 49 8 52 %! 407 -192 196 611 8 44 59 -23 %! -8 -71 61 8 411 -599 208 208 %! -52 -43 49 44 -599 411 208 208 %! -49 -8 8 59 208 208 99 -911 %! 29 -44 52 -23 208 208 -911 99 %! ]; %! [q,r] = qr(a); %! %! assert(all (t) && norm(q*r-a) < 5000*eps); %!test %! a = single ([0, 2, 1; 2, 1, 2]); %! %! [q, r] = qr (a); %! %! [qe, re] = qr (a, 0); %! %! assert (q * r, a, sqrt (eps ('single'))); %! assert (qe * re, a, sqrt (eps ('single'))); %!test %! a = single([0, 2, 1; 2, 1, 2]); %! %! [q, r, p] = qr (a); # not giving right dimensions. FIXME %! %! [qe, re, pe] = qr (a, 0); %! %! assert (q * r, a * p, sqrt (eps('single'))); %! assert (qe * re, a(:, pe), sqrt (eps('single'))); %!test %! a = single([0, 2; 2, 1; 1, 2]); %! %! [q, r] = qr (a); %! %! [qe, re] = qr (a, 0); %! %! assert (q * r, a, sqrt (eps('single'))); %! assert (qe * re, a, sqrt (eps('single'))); %!test %! a = single([0, 2; 2, 1; 1, 2]); %! %! [q, r, p] = qr (a); %! %! [qe, re, pe] = qr (a, 0); %! %! assert (q * r, a * p, sqrt (eps('single'))); %! assert (qe * re, a(:, pe), sqrt (eps('single'))); %!error <Invalid call to qr.*> qr (); %!error <Invalid call to qr.*> qr ([1, 2; 3, 4], 0, 2); %!test %! %! t = ones (24, 1); %! j = 1; %! %! if false # eliminate big matrix tests %! a = rand(5000,20); %! [q,r]=qr(a,0); t(j++) = testqr(q,r,a); %! [q,r]=qr(a',0); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a,0); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a',0); t(j++) = testqr(q,r,a',p); %! %! a = a+1i*eps('single'); %! [q,r]=qr(a,0); t(j++) = testqr(q,r,a); %! [q,r]=qr(a',0); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a,0); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a',0); t(j++) = testqr(q,r,a',p); %! endif %! %! a = [ ones(1,15); sqrt(eps('single'))*eye(15) ]; %! [q,r]=qr(a); t(j++) = testqr(q,r,a); %! [q,r]=qr(a'); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a'); t(j++) = testqr(q,r,a',p); %! %! a = a+1i*eps('single'); %! [q,r]=qr(a); t(j++) = testqr(q,r,a); %! [q,r]=qr(a'); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a'); t(j++) = testqr(q,r,a',p); %! %! a = [ ones(1,15); sqrt(eps('single'))*eye(15) ]; %! [q,r]=qr(a,0); t(j++) = testqr(q,r,a); %! [q,r]=qr(a',0); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a,0); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a',0); t(j++) = testqr(q,r,a',p); %! %! a = a+1i*eps('single'); %! [q,r]=qr(a,0); t(j++) = testqr(q,r,a); %! [q,r]=qr(a',0); t(j++) = testqr(q,r,a'); %! [q,r,p]=qr(a,0); t(j++) = testqr(q,r,a,p); %! [q,r,p]=qr(a',0); t(j++) = testqr(q,r,a',p); %! %! a = [ %! 611 196 -192 407 -8 -52 -49 29 %! 196 899 113 -192 -71 -43 -8 -44 %! -192 113 899 196 61 49 8 52 %! 407 -192 196 611 8 44 59 -23 %! -8 -71 61 8 411 -599 208 208 %! -52 -43 49 44 -599 411 208 208 %! -49 -8 8 59 208 208 99 -911 %! 29 -44 52 -23 208 208 -911 99 %! ]; %! [q,r] = qr(a); %! %! assert(all (t) && norm(q*r-a) < 5000*eps('single')); %% The deactivated tests below can't be tested till rectangular back-subs is %% implemented for sparse matrices. %!testif HAVE_CXSPARSE %! n = 20; d= 0.2; %! a = sprandn(n,n,d)+speye(n,n); %! r = qr(a); %! assert(r'*r,a'*a,1e-10) %!testif HAVE_COLAMD %! n = 20; d= 0.2; %! a = sprandn(n,n,d)+speye(n,n); %! q = symamd(a); %! a = a(q,q); %! r = qr(a); %! assert(r'*r,a'*a,1e-10) %!testif HAVE_CXSPARSE %! n = 20; d= 0.2; %! a = sprandn(n,n,d)+speye(n,n); %! [c,r] = qr(a,ones(n,1)); %! assert (r\c,full(a)\ones(n,1),10e-10) %!testif HAVE_CXSPARSE %! n = 20; d= 0.2; %! a = sprandn(n,n,d)+speye(n,n); %! b = randn(n,2); %! [c,r] = qr(a,b); %! assert (r\c,full(a)\b,10e-10) %% Test under-determined systems!! %!#testif HAVE_CXSPARSE %! n = 20; d= 0.2; %! a = sprandn(n,n+1,d)+speye(n,n+1); %! b = randn(n,2); %! [c,r] = qr(a,b); %! assert (r\c,full(a)\b,10e-10) %!testif HAVE_CXSPARSE %! n = 20; d= 0.2; %! a = 1i*sprandn(n,n,d)+speye(n,n); %! r = qr(a); %! assert(r'*r,a'*a,1e-10) %!testif HAVE_COLAMD %! n = 20; d= 0.2; %! a = 1i*sprandn(n,n,d)+speye(n,n); %! q = symamd(a); %! a = a(q,q); %! r = qr(a); %! assert(r'*r,a'*a,1e-10) %!testif HAVE_CXSPARSE %! n = 20; d= 0.2; %! a = 1i*sprandn(n,n,d)+speye(n,n); %! [c,r] = qr(a,ones(n,1)); %! assert (r\c,full(a)\ones(n,1),10e-10) %!testif HAVE_CXSPARSE %! n = 20; d= 0.2; %! a = 1i*sprandn(n,n,d)+speye(n,n); %! b = randn(n,2); %! [c,r] = qr(a,b); %! assert (r\c,full(a)\b,10e-10) %% Test under-determined systems!! %!#testif HAVE_CXSPARSE %! n = 20; d= 0.2; %! a = 1i*sprandn(n,n+1,d)+speye(n,n+1); %! b = randn(n,2); %! [c,r] = qr(a,b); %! assert (r\c,full(a)\b,10e-10) %!error qr(sprandn(10,10,0.2),ones(10,1)); */ static bool check_qr_dims (const octave_value& q, const octave_value& r, bool allow_ecf = false) { octave_idx_type m = q.rows (), k = r.rows (), n = r.columns (); return ((q.ndims () == 2 && r.ndims () == 2 && k == q.columns ()) && (m == k || (allow_ecf && k == n && k < m))); } static bool check_index (const octave_value& i, bool vector_allowed = false) { return ((i.is_real_type () || i.is_integer_type ()) && (i.is_scalar_type () || vector_allowed)); } DEFUN_DLD (qrupdate, args, , "-*- texinfo -*-\n\ @deftypefn {Loadable Function} {[@var{Q1}, @var{R1}] =} qrupdate (@var{Q}, @var{R}, @var{u}, @var{v})\n\ Given a QR@tie{}factorization of a real or complex matrix\n\ @w{@var{A} = @var{Q}*@var{R}}, @var{Q}@tie{}unitary and\n\ @var{R}@tie{}upper trapezoidal, return the QR@tie{}factorization\n\ of @w{@var{A} + @var{u}*@var{v}'}, where @var{u} and @var{v} are\n\ column vectors (rank-1 update) or matrices with equal number of columns\n\ (rank-k update). Notice that the latter case is done as a sequence of rank-1\n\ updates; thus, for k large enough, it will be both faster and more accurate\n\ to recompute the factorization from scratch.\n\ \n\ The QR@tie{}factorization supplied may be either full\n\ (Q is square) or economized (R is square).\n\ \n\ @seealso{qr, qrinsert, qrdelete}\n\ @end deftypefn") { octave_idx_type nargin = args.length (); octave_value_list retval; if (nargin != 4) { print_usage (); return retval; } octave_value argq = args(0); octave_value argr = args(1); octave_value argu = args(2); octave_value argv = args(3); if (argq.is_numeric_type () && argr.is_numeric_type () && argu.is_numeric_type () && argv.is_numeric_type ()) { if (check_qr_dims (argq, argr, true)) { if (argq.is_real_type () && argr.is_real_type () && argu.is_real_type () && argv.is_real_type ()) { // all real case if (argq.is_single_type () || argr.is_single_type () || argu.is_single_type () || argv.is_single_type ()) { FloatMatrix Q = argq.float_matrix_value (); FloatMatrix R = argr.float_matrix_value (); FloatMatrix u = argu.float_matrix_value (); FloatMatrix v = argv.float_matrix_value (); FloatQR fact (Q, R); fact.update (u, v); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } else { Matrix Q = argq.matrix_value (); Matrix R = argr.matrix_value (); Matrix u = argu.matrix_value (); Matrix v = argv.matrix_value (); QR fact (Q, R); fact.update (u, v); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } } else { // complex case if (argq.is_single_type () || argr.is_single_type () || argu.is_single_type () || argv.is_single_type ()) { FloatComplexMatrix Q = argq.float_complex_matrix_value (); FloatComplexMatrix R = argr.float_complex_matrix_value (); FloatComplexMatrix u = argu.float_complex_matrix_value (); FloatComplexMatrix v = argv.float_complex_matrix_value (); FloatComplexQR fact (Q, R); fact.update (u, v); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } else { ComplexMatrix Q = argq.complex_matrix_value (); ComplexMatrix R = argr.complex_matrix_value (); ComplexMatrix u = argu.complex_matrix_value (); ComplexMatrix v = argv.complex_matrix_value (); ComplexQR fact (Q, R); fact.update (u, v); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } } } else error ("qrupdate: Q and R dimensions don't match"); } else error ("qrupdate: Q, R, U, and V must be numeric"); return retval; } /* %!shared A, u, v, Ac, uc, vc %! A = [0.091364 0.613038 0.999083; %! 0.594638 0.425302 0.603537; %! 0.383594 0.291238 0.085574; %! 0.265712 0.268003 0.238409; %! 0.669966 0.743851 0.445057 ]; %! %! u = [0.85082; %! 0.76426; %! 0.42883; %! 0.53010; %! 0.80683 ]; %! %! v = [0.98810; %! 0.24295; %! 0.43167 ]; %! %! Ac = [0.620405 + 0.956953i 0.480013 + 0.048806i 0.402627 + 0.338171i; %! 0.589077 + 0.658457i 0.013205 + 0.279323i 0.229284 + 0.721929i; %! 0.092758 + 0.345687i 0.928679 + 0.241052i 0.764536 + 0.832406i; %! 0.912098 + 0.721024i 0.049018 + 0.269452i 0.730029 + 0.796517i; %! 0.112849 + 0.603871i 0.486352 + 0.142337i 0.355646 + 0.151496i ]; %! %! uc = [0.20351 + 0.05401i; %! 0.13141 + 0.43708i; %! 0.29808 + 0.08789i; %! 0.69821 + 0.38844i; %! 0.74871 + 0.25821i ]; %! %! vc = [0.85839 + 0.29468i; %! 0.20820 + 0.93090i; %! 0.86184 + 0.34689i ]; %! %!test %! [Q,R] = qr(A); %! [Q,R] = qrupdate(Q,R,u,v); %! assert(norm(vec(Q'*Q - eye(5)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - A - u*v'),Inf) < norm(A)*1e1*eps) %! %!test %! [Q,R] = qr(Ac); %! [Q,R] = qrupdate(Q,R,uc,vc); %! assert(norm(vec(Q'*Q - eye(5)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - Ac - uc*vc'),Inf) < norm(Ac)*1e1*eps) %!test %! [Q,R] = qr(single(A)); %! [Q,R] = qrupdate(Q,R,single(u),single(v)); %! assert(norm(vec(Q'*Q - eye(5,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - single(A) - single(u)*single(v)'),Inf) < norm(single(A))*1e1*eps('single')) %! %!test %! [Q,R] = qr(single(Ac)); %! [Q,R] = qrupdate(Q,R,single(uc),single(vc)); %! assert(norm(vec(Q'*Q - eye(5,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - single(Ac) - single(uc)*single(vc)'),Inf) < norm(single(Ac))*1e1*eps('single')) */ DEFUN_DLD (qrinsert, args, , "-*- texinfo -*-\n\ @deftypefn {Loadable Function} {[@var{Q1}, @var{R1}] =} qrinsert (@var{Q}, @var{R}, @var{j}, @var{x}, @var{orient})\n\ Given a QR@tie{}factorization of a real or complex matrix\n\ @w{@var{A} = @var{Q}*@var{R}}, @var{Q}@tie{}unitary and\n\ @var{R}@tie{}upper trapezoidal, return the QR@tie{}factorization of\n\ @w{[A(:,1:j-1) x A(:,j:n)]}, where @var{u} is a column vector to be\n\ inserted into @var{A} (if @var{orient} is @code{\"col\"}), or the\n\ QR@tie{}factorization of @w{[A(1:j-1,:);x;A(:,j:n)]}, where @var{x}\n\ is a row vector to be inserted into @var{A} (if @var{orient} is\n\ @code{\"row\"}).\n\ \n\ The default value of @var{orient} is @code{\"col\"}.\n\ If @var{orient} is @code{\"col\"},\n\ @var{u} may be a matrix and @var{j} an index vector\n\ resulting in the QR@tie{}factorization of a matrix @var{B} such that\n\ @w{B(:,@var{j})} gives @var{u} and @w{B(:,@var{j}) = []} gives @var{A}.\n\ Notice that the latter case is done as a sequence of k insertions;\n\ thus, for k large enough, it will be both faster and more accurate to\n\ recompute the factorization from scratch.\n\ \n\ If @var{orient} is @code{\"col\"},\n\ the QR@tie{}factorization supplied may be either full\n\ (Q is square) or economized (R is square).\n\ \n\ If @var{orient} is @code{\"row\"}, full factorization is needed.\n\ @seealso{qr, qrupdate, qrdelete}\n\ @end deftypefn") { octave_idx_type nargin = args.length (); octave_value_list retval; if (nargin < 4 || nargin > 5) { print_usage (); return retval; } octave_value argq = args(0); octave_value argr = args(1); octave_value argj = args(2); octave_value argx = args(3); if (argq.is_numeric_type () && argr.is_numeric_type () && argx.is_numeric_type () && (nargin < 5 || args(4).is_string ())) { std::string orient = (nargin < 5) ? "col" : args(4).string_value (); bool col = orient == "col"; if (col || orient == "row") if (check_qr_dims (argq, argr, col) && (col || argx.rows () == 1)) { if (check_index (argj, col)) { MArray<octave_idx_type> j = argj.octave_idx_type_vector_value (); octave_idx_type one = 1; if (argq.is_real_type () && argr.is_real_type () && argx.is_real_type ()) { // real case if (argq.is_single_type () || argr.is_single_type () || argx.is_single_type ()) { FloatMatrix Q = argq.float_matrix_value (); FloatMatrix R = argr.float_matrix_value (); FloatMatrix x = argx.float_matrix_value (); FloatQR fact (Q, R); if (col) fact.insert_col (x, j-one); else fact.insert_row (x.row (0), j(0)-one); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } else { Matrix Q = argq.matrix_value (); Matrix R = argr.matrix_value (); Matrix x = argx.matrix_value (); QR fact (Q, R); if (col) fact.insert_col (x, j-one); else fact.insert_row (x.row (0), j(0)-one); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } } else { // complex case if (argq.is_single_type () || argr.is_single_type () || argx.is_single_type ()) { FloatComplexMatrix Q = argq.float_complex_matrix_value (); FloatComplexMatrix R = argr.float_complex_matrix_value (); FloatComplexMatrix x = argx.float_complex_matrix_value (); FloatComplexQR fact (Q, R); if (col) fact.insert_col (x, j-one); else fact.insert_row (x.row (0), j(0)-one); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } else { ComplexMatrix Q = argq.complex_matrix_value (); ComplexMatrix R = argr.complex_matrix_value (); ComplexMatrix x = argx.complex_matrix_value (); ComplexQR fact (Q, R); if (col) fact.insert_col (x, j-one); else fact.insert_row (x.row (0), j(0)-one); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } } } else error ("qrinsert: invalid index J"); } else error ("qrinsert: dimension mismatch"); else error ("qrinsert: ORIENT must be \"col\" or \"row\""); } else print_usage (); return retval; } /* %!test %! [Q,R] = qr(A); %! [Q,R] = qrinsert(Q,R,3,u); %! assert(norm(vec(Q'*Q - eye(5)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [A(:,1:2) u A(:,3)]),Inf) < norm(A)*1e1*eps) %!test %! [Q,R] = qr(Ac); %! [Q,R] = qrinsert(Q,R,3,uc); %! assert(norm(vec(Q'*Q - eye(5)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [Ac(:,1:2) uc Ac(:,3)]),Inf) < norm(Ac)*1e1*eps) %!test %! x = [0.85082 0.76426 0.42883 ]; %! %! [Q,R] = qr(A); %! [Q,R] = qrinsert(Q,R,3,x,'row'); %! assert(norm(vec(Q'*Q - eye(6)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [A(1:2,:);x;A(3:5,:)]),Inf) < norm(A)*1e1*eps) %!test %! x = [0.20351 + 0.05401i 0.13141 + 0.43708i 0.29808 + 0.08789i ]; %! %! [Q,R] = qr(Ac); %! [Q,R] = qrinsert(Q,R,3,x,'row'); %! assert(norm(vec(Q'*Q - eye(6)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [Ac(1:2,:);x;Ac(3:5,:)]),Inf) < norm(Ac)*1e1*eps) %!test %! [Q,R] = qr(single(A)); %! [Q,R] = qrinsert(Q,R,3,single(u)); %! assert(norm(vec(Q'*Q - eye(5,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - single([A(:,1:2) u A(:,3)])),Inf) < norm(single(A))*1e1*eps('single')) %!test %! [Q,R] = qr(single(Ac)); %! [Q,R] = qrinsert(Q,R,3,single(uc)); %! assert(norm(vec(Q'*Q - eye(5,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - single([Ac(:,1:2) uc Ac(:,3)])),Inf) < norm(single(Ac))*1e1*eps('single')) %!test %! x = single([0.85082 0.76426 0.42883 ]); %! %! [Q,R] = qr(single(A)); %! [Q,R] = qrinsert(Q,R,3,x,'row'); %! assert(norm(vec(Q'*Q - eye(6,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - single([A(1:2,:);x;A(3:5,:)])),Inf) < norm(single(A))*1e1*eps('single')) %!test %! x = single([0.20351 + 0.05401i 0.13141 + 0.43708i 0.29808 + 0.08789i ]); %! %! [Q,R] = qr(single(Ac)); %! [Q,R] = qrinsert(Q,R,3,x,'row'); %! assert(norm(vec(Q'*Q - eye(6,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - single([Ac(1:2,:);x;Ac(3:5,:)])),Inf) < norm(single(Ac))*1e1*eps('single')) */ DEFUN_DLD (qrdelete, args, , "-*- texinfo -*-\n\ @deftypefn {Loadable Function} {[@var{Q1}, @var{R1}] =} qrdelete (@var{Q}, @var{R}, @var{j}, @var{orient})\n\ Given a QR@tie{}factorization of a real or complex matrix\n\ @w{@var{A} = @var{Q}*@var{R}}, @var{Q}@tie{}unitary and\n\ @var{R}@tie{}upper trapezoidal, return the QR@tie{}factorization of\n\ @w{[A(:,1:j-1) A(:,j+1:n)]}, i.e., @var{A} with one column deleted\n\ (if @var{orient} is \"col\"), or the QR@tie{}factorization of\n\ @w{[A(1:j-1,:);A(:,j+1:n)]}, i.e., @var{A} with one row deleted (if\n\ @var{orient} is \"row\").\n\ \n\ The default value of @var{orient} is \"col\".\n\ \n\ If @var{orient} is @code{\"col\"},\n\ @var{j} may be an index vector\n\ resulting in the QR@tie{}factorization of a matrix @var{B} such that\n\ @w{A(:,@var{j}) = []} gives @var{B}.\n\ Notice that the latter case is done as a sequence of k deletions;\n\ thus, for k large enough, it will be both faster and more accurate to\n\ recompute the factorization from scratch.\n\ \n\ If @var{orient} is @code{\"col\"},\n\ the QR@tie{}factorization supplied may be either full\n\ (Q is square) or economized (R is square).\n\ \n\ If @var{orient} is @code{\"row\"}, full factorization is needed.\n\ @seealso{qr, qrinsert, qrupdate}\n\ @end deftypefn") { octave_idx_type nargin = args.length (); octave_value_list retval; if (nargin < 3 || nargin > 4) { print_usage (); return retval; } octave_value argq = args(0); octave_value argr = args(1); octave_value argj = args(2); if (argq.is_numeric_type () && argr.is_numeric_type () && (nargin < 4 || args(3).is_string ())) { std::string orient = (nargin < 4) ? "col" : args(3).string_value (); bool col = orient == "col"; if (col || orient == "row") if (check_qr_dims (argq, argr, col)) { if (check_index (argj, col)) { MArray<octave_idx_type> j = argj.octave_idx_type_vector_value (); octave_idx_type one = 1; if (argq.is_real_type () && argr.is_real_type ()) { // real case if (argq.is_single_type () || argr.is_single_type ()) { FloatMatrix Q = argq.float_matrix_value (); FloatMatrix R = argr.float_matrix_value (); FloatQR fact (Q, R); if (col) fact.delete_col (j-one); else fact.delete_row (j(0)-one); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } else { Matrix Q = argq.matrix_value (); Matrix R = argr.matrix_value (); QR fact (Q, R); if (col) fact.delete_col (j-one); else fact.delete_row (j(0)-one); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } } else { // complex case if (argq.is_single_type () || argr.is_single_type ()) { FloatComplexMatrix Q = argq.float_complex_matrix_value (); FloatComplexMatrix R = argr.float_complex_matrix_value (); FloatComplexQR fact (Q, R); if (col) fact.delete_col (j-one); else fact.delete_row (j(0)-one); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } else { ComplexMatrix Q = argq.complex_matrix_value (); ComplexMatrix R = argr.complex_matrix_value (); ComplexQR fact (Q, R); if (col) fact.delete_col (j-one); else fact.delete_row (j(0)-one); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } } } else error ("qrdelete: invalid index J"); } else error ("qrdelete: dimension mismatch"); else error ("qrdelete: ORIENT must be \"col\" or \"row\""); } else print_usage (); return retval; } /* %!test %! AA = [0.091364 0.613038 0.027504 0.999083; %! 0.594638 0.425302 0.562834 0.603537; %! 0.383594 0.291238 0.742073 0.085574; %! 0.265712 0.268003 0.783553 0.238409; %! 0.669966 0.743851 0.457255 0.445057 ]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3); %! assert(norm(vec(Q'*Q - eye(5)),Inf) < 16*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(:,1:2) AA(:,4)]),Inf) < norm(AA)*1e1*eps) %! %!test %! AA = [0.364554 + 0.993117i 0.669818 + 0.510234i 0.426568 + 0.041337i 0.847051 + 0.233291i; %! 0.049600 + 0.242783i 0.448946 + 0.484022i 0.141155 + 0.074420i 0.446746 + 0.392706i; %! 0.581922 + 0.657416i 0.581460 + 0.030016i 0.219909 + 0.447288i 0.201144 + 0.069132i; %! 0.694986 + 0.000571i 0.682327 + 0.841712i 0.807537 + 0.166086i 0.192767 + 0.358098i; %! 0.945002 + 0.066788i 0.350492 + 0.642638i 0.579629 + 0.048102i 0.600170 + 0.636938i ] * I; %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3); %! assert(norm(vec(Q'*Q - eye(5)),Inf) < 16*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(:,1:2) AA(:,4)]),Inf) < norm(AA)*1e1*eps) %! %!test %! AA = [0.091364 0.613038 0.027504 0.999083; %! 0.594638 0.425302 0.562834 0.603537; %! 0.383594 0.291238 0.742073 0.085574; %! 0.265712 0.268003 0.783553 0.238409; %! 0.669966 0.743851 0.457255 0.445057 ]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3,'row'); %! assert(norm(vec(Q'*Q - eye(4)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(1:2,:);AA(4:5,:)]),Inf) < norm(AA)*1e1*eps) %! %!test %! AA = [0.364554 + 0.993117i 0.669818 + 0.510234i 0.426568 + 0.041337i 0.847051 + 0.233291i; %! 0.049600 + 0.242783i 0.448946 + 0.484022i 0.141155 + 0.074420i 0.446746 + 0.392706i; %! 0.581922 + 0.657416i 0.581460 + 0.030016i 0.219909 + 0.447288i 0.201144 + 0.069132i; %! 0.694986 + 0.000571i 0.682327 + 0.841712i 0.807537 + 0.166086i 0.192767 + 0.358098i; %! 0.945002 + 0.066788i 0.350492 + 0.642638i 0.579629 + 0.048102i 0.600170 + 0.636938i ] * I; %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3,'row'); %! assert(norm(vec(Q'*Q - eye(4)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(1:2,:);AA(4:5,:)]),Inf) < norm(AA)*1e1*eps) %!test %! AA = single([0.091364 0.613038 0.027504 0.999083; %! 0.594638 0.425302 0.562834 0.603537; %! 0.383594 0.291238 0.742073 0.085574; %! 0.265712 0.268003 0.783553 0.238409; %! 0.669966 0.743851 0.457255 0.445057 ]); %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3); %! assert(norm(vec(Q'*Q - eye(5,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(:,1:2) AA(:,4)]),Inf) < norm(AA)*1e1*eps('single')) %! %!test %! AA = single([0.364554 + 0.993117i 0.669818 + 0.510234i 0.426568 + 0.041337i 0.847051 + 0.233291i; %! 0.049600 + 0.242783i 0.448946 + 0.484022i 0.141155 + 0.074420i 0.446746 + 0.392706i; %! 0.581922 + 0.657416i 0.581460 + 0.030016i 0.219909 + 0.447288i 0.201144 + 0.069132i; %! 0.694986 + 0.000571i 0.682327 + 0.841712i 0.807537 + 0.166086i 0.192767 + 0.358098i; %! 0.945002 + 0.066788i 0.350492 + 0.642638i 0.579629 + 0.048102i 0.600170 + 0.636938i ]) * I; %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3); %! assert(norm(vec(Q'*Q - eye(5,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(:,1:2) AA(:,4)]),Inf) < norm(AA)*1e1*eps('single')) %! %!test %! AA = single([0.091364 0.613038 0.027504 0.999083; %! 0.594638 0.425302 0.562834 0.603537; %! 0.383594 0.291238 0.742073 0.085574; %! 0.265712 0.268003 0.783553 0.238409; %! 0.669966 0.743851 0.457255 0.445057 ]); %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3,'row'); %! assert(norm(vec(Q'*Q - eye(4,'single')),Inf) < 1.5e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(1:2,:);AA(4:5,:)]),Inf) < norm(AA)*1e1*eps('single')) %!testif HAVE_QRUPDATE %! # Same test as above but with more precicision %! AA = single([0.091364 0.613038 0.027504 0.999083; %! 0.594638 0.425302 0.562834 0.603537; %! 0.383594 0.291238 0.742073 0.085574; %! 0.265712 0.268003 0.783553 0.238409; %! 0.669966 0.743851 0.457255 0.445057 ]); %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3,'row'); %! assert(norm(vec(Q'*Q - eye(4,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(1:2,:);AA(4:5,:)]),Inf) < norm(AA)*1e1*eps('single')) %! %!test %! AA = single([0.364554 + 0.993117i 0.669818 + 0.510234i 0.426568 + 0.041337i 0.847051 + 0.233291i; %! 0.049600 + 0.242783i 0.448946 + 0.484022i 0.141155 + 0.074420i 0.446746 + 0.392706i; %! 0.581922 + 0.657416i 0.581460 + 0.030016i 0.219909 + 0.447288i 0.201144 + 0.069132i; %! 0.694986 + 0.000571i 0.682327 + 0.841712i 0.807537 + 0.166086i 0.192767 + 0.358098i; %! 0.945002 + 0.066788i 0.350492 + 0.642638i 0.579629 + 0.048102i 0.600170 + 0.636938i ]) * I; %! %! [Q,R] = qr(AA); %! [Q,R] = qrdelete(Q,R,3,'row'); %! assert(norm(vec(Q'*Q - eye(4,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - [AA(1:2,:);AA(4:5,:)]),Inf) < norm(AA)*1e1*eps('single')) */ DEFUN_DLD (qrshift, args, , "-*- texinfo -*-\n\ @deftypefn {Loadable Function} {[@var{Q1}, @var{R1}] =} qrshift (@var{Q}, @var{R}, @var{i}, @var{j})\n\ Given a QR@tie{}factorization of a real or complex matrix\n\ @w{@var{A} = @var{Q}*@var{R}}, @var{Q}@tie{}unitary and\n\ @var{R}@tie{}upper trapezoidal, return the QR@tie{}factorization\n\ of @w{@var{A}(:,p)}, where @w{p} is the permutation @*\n\ @code{p = [1:i-1, shift(i:j, 1), j+1:n]} if @w{@var{i} < @var{j}} @*\n\ or @*\n\ @code{p = [1:j-1, shift(j:i,-1), i+1:n]} if @w{@var{j} < @var{i}}. @*\n\ \n\ @seealso{qr, qrinsert, qrdelete}\n\ @end deftypefn") { octave_idx_type nargin = args.length (); octave_value_list retval; if (nargin != 4) { print_usage (); return retval; } octave_value argq = args(0); octave_value argr = args(1); octave_value argi = args(2); octave_value argj = args(3); if (argq.is_numeric_type () && argr.is_numeric_type ()) { if (check_qr_dims (argq, argr, true)) { if (check_index (argi) && check_index (argj)) { octave_idx_type i = argi.int_value (); octave_idx_type j = argj.int_value (); if (argq.is_real_type () && argr.is_real_type ()) { // all real case if (argq.is_single_type () && argr.is_single_type ()) { FloatMatrix Q = argq.float_matrix_value (); FloatMatrix R = argr.float_matrix_value (); FloatQR fact (Q, R); fact.shift_cols (i-1, j-1); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } else { Matrix Q = argq.matrix_value (); Matrix R = argr.matrix_value (); QR fact (Q, R); fact.shift_cols (i-1, j-1); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } } else { // complex case if (argq.is_single_type () && argr.is_single_type ()) { FloatComplexMatrix Q = argq.float_complex_matrix_value (); FloatComplexMatrix R = argr.float_complex_matrix_value (); FloatComplexQR fact (Q, R); fact.shift_cols (i-1, j-1); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } else { ComplexMatrix Q = argq.complex_matrix_value (); ComplexMatrix R = argr.complex_matrix_value (); ComplexQR fact (Q, R); fact.shift_cols (i-1, j-1); retval(1) = get_qr_r (fact); retval(0) = fact.Q (); } } } else error ("qrshift: invalid index I or J"); } else error ("qrshift: dimensions mismatch"); } else error ("qrshift: Q and R must be numeric"); return retval; } /* %!test %! AA = A.'; %! i = 2; j = 4; p = [1:i-1, shift(i:j,-1), j+1:5]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrshift(Q,R,i,j); %! assert(norm(vec(Q'*Q - eye(3)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - AA(:,p)),Inf) < norm(AA)*1e1*eps) %! %! j = 2; i = 4; p = [1:j-1, shift(j:i,+1), i+1:5]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrshift(Q,R,i,j); %! assert(norm(vec(Q'*Q - eye(3)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - AA(:,p)),Inf) < norm(AA)*1e1*eps) %! %!test %! AA = Ac.'; %! i = 2; j = 4; p = [1:i-1, shift(i:j,-1), j+1:5]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrshift(Q,R,i,j); %! assert(norm(vec(Q'*Q - eye(3)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - AA(:,p)),Inf) < norm(AA)*1e1*eps) %! %! j = 2; i = 4; p = [1:j-1, shift(j:i,+1), i+1:5]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrshift(Q,R,i,j); %! assert(norm(vec(Q'*Q - eye(3)),Inf) < 1e1*eps) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - AA(:,p)),Inf) < norm(AA)*1e1*eps) %!test %! AA = single (A).'; %! i = 2; j = 4; p = [1:i-1, shift(i:j,-1), j+1:5]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrshift(Q,R,i,j); %! assert(norm(vec(Q'*Q - eye(3,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - AA(:,p)),Inf) < norm(AA)*1e1*eps('single')) %! %! j = 2; i = 4; p = [1:j-1, shift(j:i,+1), i+1:5]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrshift(Q,R,i,j); %! assert(norm(vec(Q'*Q - eye(3,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - AA(:,p)),Inf) < norm(AA)*1e1*eps('single')) %! %!test %! AA = single(Ac).'; %! i = 2; j = 4; p = [1:i-1, shift(i:j,-1), j+1:5]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrshift(Q,R,i,j); %! assert(norm(vec(Q'*Q - eye(3,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - AA(:,p)),Inf) < norm(AA)*1e1*eps('single')) %! %! j = 2; i = 4; p = [1:j-1, shift(j:i,+1), i+1:5]; %! %! [Q,R] = qr(AA); %! [Q,R] = qrshift(Q,R,i,j); %! assert(norm(vec(Q'*Q - eye(3,'single')),Inf) < 1e1*eps('single')) %! assert(norm(vec(triu(R)-R),Inf) == 0) %! assert(norm(vec(Q*R - AA(:,p)),Inf) < norm(AA)*1e1*eps('single')) */