Mercurial > octave-antonio
annotate scripts/sparse/pcr.m @ 14237:11949c9795a0
Revamp %!demos in m-files to use Octave coding conventions on spacing, etc.
Add clf() to all demos using plot features to get reproducibility.
Use 64 as input to all colormaps (jet (64)) to get reproducibility.
* bicubic.m, cell2mat.m, celldisp.m, cplxpair.m, interp1.m, interp2.m,
interpft.m, interpn.m, profile.m, profshow.m, convhull.m, delaunay.m,
griddata.m, inpolygon.m, voronoi.m, autumn.m, bone.m, contrast.m, cool.m,
copper.m, flag.m, gmap40.m, gray.m, hot.m, hsv.m, image.m, imshow.m, jet.m,
ocean.m, pink.m, prism.m, rainbow.m, spring.m, summer.m, white.m, winter.m,
condest.m, onenormest.m, axis.m, clabel.m, colorbar.m, comet.m, comet3.m,
compass.m, contour.m, contour3.m, contourf.m, cylinder.m, daspect.m,
ellipsoid.m, errorbar.m, ezcontour.m, ezcontourf.m, ezmesh.m, ezmeshc.m,
ezplot.m, ezplot3.m, ezpolar.m, ezsurf.m, ezsurfc.m, feather.m, fill.m,
fplot.m, grid.m, hold.m, isosurface.m, legend.m, loglog.m, loglogerr.m,
pareto.m, patch.m, pbaspect.m, pcolor.m, pie.m, pie3.m, plot3.m, plotmatrix.m,
plotyy.m, polar.m, quiver.m, quiver3.m, rectangle.m, refreshdata.m, ribbon.m,
rose.m, scatter.m, scatter3.m, semilogx.m, semilogxerr.m, semilogy.m,
semilogyerr.m, shading.m, slice.m, sombrero.m, stairs.m, stem.m, stem3.m,
subplot.m, surf.m, surfc.m, surfl.m, surfnorm.m, text.m, title.m, trimesh.m,
triplot.m, trisurf.m, uigetdir.m, uigetfile.m, uimenu.m, uiputfile.m,
waitbar.m, xlim.m, ylim.m, zlim.m, mkpp.m, pchip.m, polyaffine.m, spline.m,
bicgstab.m, cgs.m, gplot.m, pcg.m, pcr.m, treeplot.m, strtok.m, demo.m,
example.m, rundemos.m, speed.m, test.m, calendar.m, datestr.m, datetick.m,
weekday.m: Revamp %!demos to use Octave coding conventions on spacing, etc.
author | Rik <octave@nomad.inbox5.com> |
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date | Fri, 20 Jan 2012 12:59:53 -0800 |
parents | 72c96de7a403 |
children | ce2b59a6d0e5 |
rev | line source |
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1 ## Copyright (C) 2004-2012 Piotr Krzyzanowski |
5837 | 2 ## |
3 ## This file is part of Octave. | |
4 ## | |
5 ## Octave is free software; you can redistribute it and/or modify it | |
6 ## under the terms of the GNU General Public License as published by | |
7016 | 7 ## the Free Software Foundation; either version 3 of the License, or (at |
8 ## your option) any later version. | |
5837 | 9 ## |
10 ## Octave is distributed in the hope that it will be useful, but | |
11 ## WITHOUT ANY WARRANTY; without even the implied warranty of | |
12 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU | |
13 ## General Public License for more details. | |
14 ## | |
15 ## You should have received a copy of the GNU General Public License | |
7016 | 16 ## along with Octave; see the file COPYING. If not, see |
17 ## <http://www.gnu.org/licenses/>. | |
5837 | 18 |
19 ## -*- texinfo -*- | |
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20 ## @deftypefn {Function File} {@var{x} =} pcr (@var{A}, @var{b}, @var{tol}, @var{maxit}, @var{m}, @var{x0}, @dots{}) |
5837 | 21 ## @deftypefnx {Function File} {[@var{x}, @var{flag}, @var{relres}, @var{iter}, @var{resvec}] =} pcr (@dots{}) |
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22 ## |
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23 ## Solve the linear system of equations @code{@var{A} * @var{x} = @var{b}} |
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24 ## by means of the Preconditioned Conjugate Residuals iterative |
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25 ## method. The input arguments are |
5837 | 26 ## |
27 ## @itemize | |
28 ## @item | |
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29 ## @var{A} can be either a square (preferably sparse) matrix or a |
5837 | 30 ## function handle, inline function or string containing the name |
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31 ## of a function which computes @code{@var{A} * @var{x}}. In principle |
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32 ## @var{A} should be symmetric and non-singular; if @code{pcr} |
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33 ## finds @var{A} to be numerically singular, you will get a warning |
5837 | 34 ## message and the @var{flag} output parameter will be set. |
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35 ## |
5837 | 36 ## @item |
37 ## @var{b} is the right hand side vector. | |
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38 ## |
5837 | 39 ## @item |
40 ## @var{tol} is the required relative tolerance for the residual error, | |
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41 ## @code{@var{b} - @var{A} * @var{x}}. The iteration stops if |
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42 ## @code{norm (@var{b} - @var{A} * @var{x}) <= |
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43 ## @var{tol} * norm (@var{b} - @var{A} * @var{x0})}. |
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44 ## If @var{tol} is empty or is omitted, the function sets |
5837 | 45 ## @code{@var{tol} = 1e-6} by default. |
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46 ## |
5837 | 47 ## @item |
48 ## @var{maxit} is the maximum allowable number of iterations; if | |
49 ## @code{[]} is supplied for @code{maxit}, or @code{pcr} has less | |
50 ## arguments, a default value equal to 20 is used. | |
51 ## | |
52 ## @item | |
5838 | 53 ## @var{m} is the (left) preconditioning matrix, so that the iteration is |
5837 | 54 ## (theoretically) equivalent to solving by @code{pcr} @code{@var{P} * |
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55 ## @var{x} = @var{m} \ @var{b}}, with @code{@var{P} = @var{m} \ @var{A}}. |
5837 | 56 ## Note that a proper choice of the preconditioner may dramatically |
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57 ## improve the overall performance of the method. Instead of matrix |
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58 ## @var{m}, the user may pass a function which returns the results of |
5838 | 59 ## applying the inverse of @var{m} to a vector (usually this is the |
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60 ## preferred way of using the preconditioner). If @code{[]} is supplied |
5838 | 61 ## for @var{m}, or @var{m} is omitted, no preconditioning is applied. |
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62 ## |
5837 | 63 ## @item |
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64 ## @var{x0} is the initial guess. If @var{x0} is empty or omitted, the |
5837 | 65 ## function sets @var{x0} to a zero vector by default. |
66 ## @end itemize | |
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67 ## |
5837 | 68 ## The arguments which follow @var{x0} are treated as parameters, and |
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69 ## passed in a proper way to any of the functions (@var{A} or @var{m}) |
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70 ## which are passed to @code{pcr}. See the examples below for further |
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71 ## details. The output arguments are |
5837 | 72 ## |
73 ## @itemize | |
74 ## @item | |
75 ## @var{x} is the computed approximation to the solution of | |
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76 ## @code{@var{A} * @var{x} = @var{b}}. |
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77 ## |
5837 | 78 ## @item |
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79 ## @var{flag} reports on the convergence. @code{@var{flag} = 0} means |
5837 | 80 ## the solution converged and the tolerance criterion given by @var{tol} |
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81 ## is satisfied. @code{@var{flag} = 1} means that the @var{maxit} limit |
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82 ## for the iteration count was reached. @code{@var{flag} = 3} reports t |
5837 | 83 ## @code{pcr} breakdown, see [1] for details. |
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84 ## |
5837 | 85 ## @item |
86 ## @var{relres} is the ratio of the final residual to its initial value, | |
87 ## measured in the Euclidean norm. | |
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88 ## |
5837 | 89 ## @item |
90 ## @var{iter} is the actual number of iterations performed. | |
91 ## | |
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92 ## @item |
5837 | 93 ## @var{resvec} describes the convergence history of the method, |
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94 ## so that @code{@var{resvec} (i)} contains the Euclidean norms of the |
7001 | 95 ## residual after the (@var{i}-1)-th iteration, @code{@var{i} = |
5838 | 96 ## 1,2, @dots{}, @var{iter}+1}. |
5837 | 97 ## @end itemize |
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98 ## |
5837 | 99 ## Let us consider a trivial problem with a diagonal matrix (we exploit the |
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100 ## sparsity of A) |
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101 ## |
5837 | 102 ## @example |
103 ## @group | |
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104 ## n = 10; |
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105 ## A = sparse (diag (1:n)); |
10549 | 106 ## b = rand (N, 1); |
5837 | 107 ## @end group |
108 ## @end example | |
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109 ## |
5837 | 110 ## @sc{Example 1:} Simplest use of @code{pcr} |
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111 ## |
5837 | 112 ## @example |
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113 ## x = pcr (A, b) |
5837 | 114 ## @end example |
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115 ## |
5837 | 116 ## @sc{Example 2:} @code{pcr} with a function which computes |
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117 ## @code{@var{A} * @var{x}}. |
5837 | 118 ## |
119 ## @example | |
120 ## @group | |
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121 ## function y = apply_a (x) |
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122 ## y = [1:10]'.*x; |
5837 | 123 ## endfunction |
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124 ## |
8507 | 125 ## x = pcr ("apply_a", b) |
5837 | 126 ## @end group |
127 ## @end example | |
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128 ## |
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129 ## @sc{Example 3:} Preconditioned iteration, with full diagnostics. The |
5837 | 130 ## preconditioner (quite strange, because even the original matrix |
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131 ## @var{A} is trivial) is defined as a function |
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132 ## |
5837 | 133 ## @example |
134 ## @group | |
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135 ## function y = apply_m (x) |
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136 ## k = floor (length(x)-2); |
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137 ## y = x; |
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138 ## y(1:k) = x(1:k)./[1:k]'; |
5837 | 139 ## endfunction |
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140 ## |
7031 | 141 ## [x, flag, relres, iter, resvec] = ... |
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142 ## pcr (A, b, [], [], "apply_m") |
5837 | 143 ## semilogy([1:iter+1], resvec); |
144 ## @end group | |
145 ## @end example | |
146 ## | |
147 ## @sc{Example 4:} Finally, a preconditioner which depends on a | |
148 ## parameter @var{k}. | |
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149 ## |
5837 | 150 ## @example |
151 ## @group | |
8507 | 152 ## function y = apply_m (x, varargin) |
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153 ## k = varargin@{1@}; |
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154 ## y = x; y(1:k) = x(1:k)./[1:k]'; |
5837 | 155 ## endfunction |
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156 ## |
7031 | 157 ## [x, flag, relres, iter, resvec] = ... |
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158 ## pcr (A, b, [], [], "apply_m"', [], 3) |
5837 | 159 ## @end group |
160 ## @end example | |
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161 ## |
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162 ## References: |
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163 ## |
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164 ## [1] W. Hackbusch, @cite{Iterative Solution of Large Sparse Systems of |
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165 ## Equations}, section 9.5.4; Springer, 1994 |
5837 | 166 ## |
167 ## @seealso{sparse, pcg} | |
168 ## @end deftypefn | |
169 | |
5838 | 170 ## Author: Piotr Krzyzanowski <piotr.krzyzanowski@mimuw.edu.pl> |
5837 | 171 |
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172 function [x, flag, relres, iter, resvec] = pcr (A, b, tol, maxit, m, x0, varargin) |
5837 | 173 |
174 breakdown = false; | |
175 | |
5838 | 176 if (nargin < 6 || isempty (x0)) |
177 x = zeros (size (b)); | |
5837 | 178 else |
179 x = x0; | |
180 endif | |
181 | |
182 if (nargin < 5) | |
8507 | 183 m = []; |
5837 | 184 endif |
185 | |
5838 | 186 if (nargin < 4 || isempty (maxit)) |
5837 | 187 maxit = 20; |
188 endif | |
189 | |
5838 | 190 maxit += 2; |
5837 | 191 |
5838 | 192 if (nargin < 3 || isempty (tol)) |
5837 | 193 tol = 1e-6; |
194 endif | |
195 | |
196 if (nargin < 2) | |
5838 | 197 print_usage (); |
5837 | 198 endif |
199 | |
200 ## init | |
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201 if (isnumeric (A)) # is A a matrix? |
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202 r = b - A*x; |
10549 | 203 else # then A should be a function! |
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204 r = b - feval (A, x, varargin{:}); |
5837 | 205 endif |
206 | |
10549 | 207 if (isnumeric (m)) # is M a matrix? |
208 if (isempty (m)) # if M is empty, use no precond | |
5837 | 209 p = r; |
10549 | 210 else # otherwise, apply the precond |
8507 | 211 p = m \ r; |
5837 | 212 endif |
10549 | 213 else # then M should be a function! |
8507 | 214 p = feval (m, r, varargin{:}); |
5837 | 215 endif |
216 | |
217 iter = 2; | |
218 | |
219 b_bot_old = 1; | |
5838 | 220 q_old = p_old = s_old = zeros (size (x)); |
5837 | 221 |
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222 if (isnumeric (A)) # is A a matrix? |
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223 q = A * p; |
10549 | 224 else # then A should be a function! |
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225 q = feval (A, p, varargin{:}); |
5837 | 226 endif |
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227 |
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228 resvec(1) = abs (norm (r)); |
5837 | 229 |
230 ## iteration | |
5838 | 231 while (resvec(iter-1) > tol*resvec(1) && iter < maxit) |
232 | |
10549 | 233 if (isnumeric (m)) # is M a matrix? |
234 if (isempty (m)) # if M is empty, use no precond | |
235 s = q; | |
236 else # otherwise, apply the precond | |
237 s = m \ q; | |
5837 | 238 endif |
10549 | 239 else # then M should be a function! |
8507 | 240 s = feval (m, q, varargin{:}); |
5837 | 241 endif |
5838 | 242 b_top = r' * s; |
243 b_bot = q' * s; | |
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244 |
5837 | 245 if (b_bot == 0.0) |
246 breakdown = true; | |
247 break; | |
248 endif | |
5838 | 249 lambda = b_top / b_bot; |
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250 |
5838 | 251 x += lambda*p; |
252 r -= lambda*q; | |
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253 |
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254 if (isnumeric(A)) # is A a matrix? |
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255 t = A*s; |
10549 | 256 else # then A should be a function! |
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257 t = feval (A, s, varargin{:}); |
5837 | 258 endif |
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259 |
5838 | 260 alpha0 = (t'*s) / b_bot; |
261 alpha1 = (t'*s_old) / b_bot_old; | |
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262 |
5838 | 263 p_temp = p; |
264 q_temp = q; | |
265 | |
5837 | 266 p = s - alpha0*p - alpha1*p_old; |
267 q = t - alpha0*q - alpha1*q_old; | |
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268 |
5837 | 269 s_old = s; |
270 p_old = p_temp; | |
271 q_old = q_temp; | |
272 b_bot_old = b_bot; | |
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273 |
5838 | 274 resvec(iter) = abs (norm (r)); |
275 iter++; | |
5837 | 276 endwhile |
277 | |
278 flag = 0; | |
5838 | 279 relres = resvec(iter-1) ./ resvec(1); |
280 iter -= 2; | |
281 if (iter >= maxit-2) | |
5837 | 282 flag = 1; |
283 if (nargout < 2) | |
6498 | 284 warning ("pcr: maximum number of iterations (%d) reached\n", iter); |
285 warning ("the initial residual norm was reduced %g times.\n", 1.0/relres); | |
5837 | 286 endif |
5838 | 287 elseif (nargout < 2 && ! breakdown) |
6498 | 288 fprintf (stderr, "pcr: converged in %d iterations. \n", iter); |
289 fprintf (stderr, "the initial residual norm was reduced %g times.\n", | |
10549 | 290 1.0 / relres); |
5837 | 291 endif |
5838 | 292 |
5837 | 293 if (breakdown) |
294 flag = 3; | |
295 if (nargout < 2) | |
7001 | 296 warning ("pcr: breakdown occurred:\n"); |
6498 | 297 warning ("system matrix singular or preconditioner indefinite?\n"); |
5837 | 298 endif |
299 endif | |
300 | |
301 endfunction | |
302 | |
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303 |
5837 | 304 %!demo |
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305 %! # Simplest usage of PCR (see also 'help pcr') |
5837 | 306 %! |
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307 %! N = 20; |
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308 %! A = diag (linspace (-3.1,3,N)); b = rand (N,1); |
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309 %! y = A \ b; # y is the true solution |
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310 %! x = pcr (A,b); |
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311 %! printf ("The solution relative error is %g\n", norm (x-y) / norm (y)); |
5837 | 312 %! |
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313 %! # You shouldn't be afraid if PCR issues some warning messages in this |
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314 %! # example: watch out in the second example, why it takes N iterations |
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315 %! # of PCR to converge to (a very accurate, by the way) solution |
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316 |
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317 %!demo |
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318 %! # Full output from PCR |
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319 %! # We use this output to plot the convergence history |
5837 | 320 %! |
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321 %! N = 20; |
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322 %! A = diag (linspace(-3.1,30,N)); b = rand (N,1); |
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323 %! X = A \ b; # X is the true solution |
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324 %! [x, flag, relres, iter, resvec] = pcr (A,b); |
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325 %! printf ("The solution relative error is %g\n", norm (x-X) / norm (X)); |
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326 %! clf; |
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327 %! title ("Convergence history"); |
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328 %! xlabel ("Iteration"); ylabel ("log(||b-Ax||/||b||)"); |
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329 %! semilogy ([0:iter], resvec/resvec(1), "o-g;relative residual;"); |
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330 |
5837 | 331 %!demo |
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332 %! # Full output from PCR |
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333 %! # We use indefinite matrix based on the Hilbert matrix, with one |
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334 %! # strongly negative eigenvalue |
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335 %! # Hilbert matrix is extremely ill conditioned, so is ours, |
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336 %! # and that's why PCR WILL have problems |
5837 | 337 %! |
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338 %! N = 10; |
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339 %! A = hilb (N); A(1,1) = -A(1,1); b = rand (N,1); |
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340 %! X = A \ b; # X is the true solution |
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341 %! printf ("Condition number of A is %g\n", cond (A)); |
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342 %! [x, flag, relres, iter, resvec] = pcr (A,b,[],200); |
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343 %! if (flag == 3) |
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344 %! printf ("PCR breakdown. System matrix is [close to] singular\n"); |
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345 %! end |
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346 %! clf; |
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347 %! title ("Convergence history"); |
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348 %! xlabel ("Iteration"); ylabel ("log(||b-Ax||)"); |
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349 %! semilogy ([0:iter], resvec, "o-g;absolute residual;"); |
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350 |
5837 | 351 %!demo |
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352 %! # Full output from PCR |
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353 %! # We use an indefinite matrix based on the 1-D Laplacian matrix for A, |
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354 %! # and here we have cond(A) = O(N^2) |
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355 %! # That's the reason we need some preconditioner; here we take |
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356 %! # a very simple and not powerful Jacobi preconditioner, |
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357 %! # which is the diagonal of A |
5837 | 358 %! |
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359 %! # Note that we use here indefinite preconditioners! |
5837 | 360 %! |
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361 %! N = 100; |
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362 %! A = zeros (N,N); |
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363 %! for i=1:N-1 # form 1-D Laplacian matrix |
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364 %! A(i:i+1,i:i+1) = [2 -1; -1 2]; |
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365 %! endfor |
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366 %! A = [A, zeros(size(A)); zeros(size(A)), -A]; |
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367 %! b = rand (2*N,1); |
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368 %! X = A \ b; # X is the true solution |
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369 %! maxit = 80; |
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370 %! printf ("System condition number is %g\n", cond (A)); |
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371 %! # No preconditioner: the convergence is very slow! |
5837 | 372 %! |
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373 %! [x, flag, relres, iter, resvec] = pcr (A,b,[],maxit); |
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374 %! clf; |
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375 %! title ("Convergence history"); |
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376 %! xlabel ("Iteration"); ylabel ("log(||b-Ax||)"); |
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377 %! semilogy ([0:iter], resvec, "o-g;NO preconditioning: absolute residual;"); |
5837 | 378 %! |
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379 %! pause (1); |
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380 %! # Test Jacobi preconditioner: it will not help much!!! |
5837 | 381 %! |
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382 %! M = diag (diag (A)); # Jacobi preconditioner |
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383 %! [x, flag, relres, iter, resvec] = pcr (A,b,[],maxit,M); |
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384 %! hold on; |
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385 %! semilogy ([0:iter],resvec,"o-r;JACOBI preconditioner: absolute residual;"); |
5837 | 386 %! |
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387 %! pause (1); |
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388 %! # Test nonoverlapping block Jacobi preconditioner: this one should give |
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389 %! # some convergence speedup! |
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390 %! |
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391 %! M = zeros (N,N); k = 4; |
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392 %! for i=1:k:N # get k x k diagonal blocks of A |
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393 %! M(i:i+k-1,i:i+k-1) = A(i:i+k-1,i:i+k-1); |
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394 %! endfor |
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395 %! M = [M, zeros(size (M)); zeros(size(M)), -M]; |
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396 %! [x, flag, relres, iter, resvec] = pcr (A,b,[],maxit,M); |
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397 %! semilogy ([0:iter], resvec, "o-b;BLOCK JACOBI preconditioner: absolute residual;"); |
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398 %! hold off; |
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399 |
5837 | 400 %!test |
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401 %! # solve small indefinite diagonal system |
5837 | 402 %! |
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403 %! N = 10; |
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404 %! A = diag (linspace (-10.1,10,N)); b = ones (N,1); |
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405 %! X = A \ b; # X is the true solution |
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406 %! [x, flag] = pcr (A,b,[],N+1); |
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407 %! assert (norm (x-X) / norm (X) < 1e-10); |
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408 %! assert (flag, 0); |
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409 |
5837 | 410 %!test |
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411 %! # solve tridiagonal system, do not converge in default 20 iterations |
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412 %! # should perform max allowable default number of iterations |
5837 | 413 %! |
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414 %! N = 100; |
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415 %! A = zeros (N,N); |
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416 %! for i=1:N-1 # form 1-D Laplacian matrix |
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417 %! A(i:i+1,i:i+1) = [2 -1; -1 2]; |
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418 %! endfor |
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419 %! b = ones (N,1); |
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420 %! X = A \ b; # X is the true solution |
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421 %! [x, flag, relres, iter, resvec] = pcr (A,b,1e-12); |
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422 %! assert (flag, 1); |
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423 %! assert (relres > 0.6); |
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424 %! assert (iter, 20); |
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425 |
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426 %!test |
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427 %! # solve tridiagonal system with "perfect" preconditioner |
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428 %! # converges in one iteration |
5837 | 429 %! |
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430 %! N = 100; |
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431 %! A = zeros (N,N); |
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432 %! for i=1:N-1 # form 1-D Laplacian matrix |
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433 %! A(i:i+1,i:i+1) = [2 -1; -1 2]; |
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434 %! endfor |
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435 %! b = ones (N,1); |
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436 %! X = A \ b; # X is the true solution |
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437 %! [x, flag, relres, iter] = pcr (A,b,[],[],A,b); |
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438 %! assert (norm (x-X) / norm(X) < 1e-6); |
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439 %! assert (relres < 1e-6); |
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440 %! assert (flag, 0); |
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441 %! assert (iter, 1); # should converge in one iteration |
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442 |