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annotate scripts/sparse/pcg.m @ 14363:f3d52523cde1
Use Octave coding conventions in all m-file %!test blocks
* wavread.m, acosd.m, acot.m, acotd.m, acoth.m, acsc.m, acscd.m, acsch.m,
asec.m, asecd.m, asech.m, asind.m, atand.m, cosd.m, cot.m, cotd.m, coth.m,
csc.m, cscd.m, csch.m, sec.m, secd.m, sech.m, sind.m, tand.m, accumarray.m,
accumdim.m, bitcmp.m, bitget.m, bitset.m, blkdiag.m, cart2pol.m, cart2sph.m,
celldisp.m, chop.m, circshift.m, colon.m, common_size.m, cplxpair.m,
cumtrapz.m, curl.m, dblquad.m, deal.m, divergence.m, flipdim.m, fliplr.m,
flipud.m, genvarname.m, gradient.m, idivide.m, int2str.m, interp1.m,
interp1q.m, interp2.m, interp3.m, interpft.m, interpn.m, isa.m, isdir.m,
isequal.m, isequalwithequalnans.m, issquare.m, logspace.m, nargchk.m,
narginchk.m, nargoutchk.m, nextpow2.m, nthargout.m, num2str.m, pol2cart.m,
polyarea.m, postpad.m, prepad.m, profile.m, profshow.m, quadgk.m, quadv.m,
randi.m, rat.m, repmat.m, rot90.m, rotdim.m, shift.m, shiftdim.m, sph2cart.m,
structfun.m, trapz.m, triplequad.m, convhull.m, dsearch.m, dsearchn.m,
griddata3.m, griddatan.m, rectint.m, tsearchn.m, __makeinfo__.m, doc.m,
get_first_help_sentence.m, help.m, type.m, unimplemented.m, which.m, imread.m,
imwrite.m, dlmwrite.m, fileread.m, is_valid_file_id.m, strread.m, textread.m,
textscan.m, commutation_matrix.m, cond.m, condest.m, cross.m,
duplication_matrix.m, expm.m, housh.m, isdefinite.m, ishermitian.m,
issymmetric.m, logm.m, normest.m, null.m, onenormest.m, orth.m, planerot.m,
qzhess.m, rank.m, rref.m, trace.m, vech.m, ans.m, bincoeff.m, bug_report.m,
bzip2.m, comma.m, compare_versions.m, computer.m, edit.m, fileparts.m,
fullfile.m, getfield.m, gzip.m, info.m, inputname.m, isappdata.m, isdeployed.m,
ismac.m, ispc.m, isunix.m, list_primes.m, ls.m, mexext.m, namelengthmax.m,
news.m, orderfields.m, paren.m, recycle.m, rmappdata.m, semicolon.m,
setappdata.m, setfield.m, substruct.m, symvar.m, ver.m, version.m,
warning_ids.m, xor.m, fminbnd.m, fsolve.m, fzero.m, lsqnonneg.m, optimset.m,
pqpnonneg.m, sqp.m, matlabroot.m, __gnuplot_drawnow__.m,
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ginput.m, graphics_toolkit.m, gtext.m, hggroup.m, hist.m, hold.m, isfigure.m,
ishghandle.m, ishold.m, isocolors.m, isonormals.m, isosurface.m, isprop.m,
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orient.m, patch.m, plot3.m, plotyy.m, __print_parse_opts__.m, quiver3.m,
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range.m, ranks.m, run_count.m, runlength.m, skewness.m, spearman.m,
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expinv.m, exppdf.m, exprnd.m, fcdf.m, finv.m, fpdf.m, frnd.m, gamcdf.m,
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hygecdf.m, hygeinv.m, hygepdf.m, hygernd.m, kolmogorov_smirnov_cdf.m,
laplace_cdf.m, laplace_inv.m, laplace_pdf.m, laplace_rnd.m, logistic_cdf.m,
logistic_inv.m, logistic_pdf.m, logistic_rnd.m, logncdf.m, logninv.m,
lognpdf.m, lognrnd.m, nbincdf.m, nbininv.m, nbinpdf.m, nbinrnd.m, normcdf.m,
norminv.m, normpdf.m, normrnd.m, poisscdf.m, poissinv.m, poisspdf.m,
poissrnd.m, stdnormal_cdf.m, stdnormal_inv.m, stdnormal_pdf.m, stdnormal_rnd.m,
tcdf.m, tinv.m, tpdf.m, trnd.m, unidcdf.m, unidinv.m, unidpdf.m, unidrnd.m,
unifcdf.m, unifinv.m, unifpdf.m, unifrnd.m, wblcdf.m, wblinv.m, wblpdf.m,
wblrnd.m, kolmogorov_smirnov_test.m, kruskal_wallis_test.m, base2dec.m,
bin2dec.m, blanks.m, cstrcat.m, deblank.m, dec2base.m, dec2bin.m, dec2hex.m,
findstr.m, hex2dec.m, index.m, isletter.m, mat2str.m, rindex.m, str2num.m,
strcat.m, strjust.m, strmatch.m, strsplit.m, strtok.m, strtrim.m, strtrunc.m,
substr.m, validatestring.m, demo.m, example.m, fail.m, speed.m, addtodate.m,
asctime.m, clock.m, ctime.m, date.m, datenum.m, datetick.m, datevec.m,
eomday.m, etime.m, is_leap_year.m, now.m:
Use Octave coding conventions in all m-file %!test blocks
author | Rik <octave@nomad.inbox5.com> |
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date | Mon, 13 Feb 2012 07:29:44 -0800 |
parents | ce2b59a6d0e5 |
children | 5d3a684236b0 |
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} =} pcg (@var{A}, @var{b}, @var{tol}, @var{maxit}, @var{m1}, @var{m2}, @var{x0}, @dots{}) |
5837 | 21 ## @deftypefnx {Function File} {[@var{x}, @var{flag}, @var{relres}, @var{iter}, @var{resvec}, @var{eigest}] =} pcg (@dots{}) |
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 Gradient 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 positive definite; if @code{pcg} |
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33 ## finds @var{A} to not be positive definite, 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{pcg} has less | |
50 ## arguments, a default value equal to 20 is used. | |
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51 ## |
5837 | 52 ## @item |
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53 ## @var{m} = @var{m1} * @var{m2} is the (left) preconditioning matrix, so that |
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54 ## the iteration is (theoretically) equivalent to solving by @code{pcg} |
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55 ## @code{@var{P} * |
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56 ## @var{x} = @var{m} \ @var{b}}, with @code{@var{P} = @var{m} \ @var{A}}. |
5837 | 57 ## Note that a proper choice of the preconditioner may dramatically |
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58 ## improve the overall performance of the method. Instead of matrices |
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59 ## @var{m1} and @var{m2}, the user may pass two functions which return |
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60 ## the results of applying the inverse of @var{m1} and @var{m2} to |
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61 ## a vector (usually this is the preferred way of using the preconditioner). |
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62 ## If @code{[]} is supplied for @var{m1}, or @var{m1} is omitted, no |
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63 ## preconditioning is applied. If @var{m2} is omitted, @var{m} = @var{m1} |
6747 | 64 ## will be used as preconditioner. |
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65 ## |
5837 | 66 ## @item |
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67 ## @var{x0} is the initial guess. If @var{x0} is empty or omitted, the |
5837 | 68 ## function sets @var{x0} to a zero vector by default. |
69 ## @end itemize | |
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70 ## |
5837 | 71 ## The arguments which follow @var{x0} are treated as parameters, and |
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72 ## passed in a proper way to any of the functions (@var{A} or @var{m}) |
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73 ## which are passed to @code{pcg}. See the examples below for further |
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74 ## details. The output arguments are |
5837 | 75 ## |
76 ## @itemize | |
77 ## @item | |
78 ## @var{x} is the computed approximation to the solution of | |
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79 ## @code{@var{A} * @var{x} = @var{b}}. |
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80 ## |
5837 | 81 ## @item |
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82 ## @var{flag} reports on the convergence. @code{@var{flag} = 0} means |
5837 | 83 ## the solution converged and the tolerance criterion given by @var{tol} |
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84 ## is satisfied. @code{@var{flag} = 1} means that the @var{maxit} limit |
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85 ## for the iteration count was reached. @code{@var{flag} = 3} reports that |
5837 | 86 ## the (preconditioned) matrix was found not positive definite. |
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87 ## |
5837 | 88 ## @item |
89 ## @var{relres} is the ratio of the final residual to its initial value, | |
90 ## measured in the Euclidean norm. | |
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91 ## |
5837 | 92 ## @item |
93 ## @var{iter} is the actual number of iterations performed. | |
94 ## | |
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95 ## @item |
5837 | 96 ## @var{resvec} describes the convergence history of the method. |
97 ## @code{@var{resvec} (i,1)} is the Euclidean norm of the residual, and | |
98 ## @code{@var{resvec} (i,2)} is the preconditioned residual norm, | |
99 ## after the (@var{i}-1)-th iteration, @code{@var{i} = | |
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100 ## 1, 2, @dots{}, @var{iter}+1}. The preconditioned residual norm |
6547 | 101 ## is defined as |
5838 | 102 ## @code{norm (@var{r}) ^ 2 = @var{r}' * (@var{m} \ @var{r})} where |
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103 ## @code{@var{r} = @var{b} - @var{A} * @var{x}}, see also the |
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104 ## description of @var{m}. If @var{eigest} is not required, only |
5837 | 105 ## @code{@var{resvec} (:,1)} is returned. |
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106 ## |
5837 | 107 ## @item |
108 ## @var{eigest} returns the estimate for the smallest @code{@var{eigest} | |
109 ## (1)} and largest @code{@var{eigest} (2)} eigenvalues of the | |
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110 ## preconditioned matrix @code{@var{P} = @var{m} \ @var{A}}. In |
7007 | 111 ## particular, if no preconditioning is used, the estimates for the |
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112 ## extreme eigenvalues of @var{A} are returned. @code{@var{eigest} (1)} |
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113 ## is an overestimate and @code{@var{eigest} (2)} is an underestimate, |
5837 | 114 ## so that @code{@var{eigest} (2) / @var{eigest} (1)} is a lower bound |
115 ## for @code{cond (@var{P}, 2)}, which nevertheless in the limit should | |
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116 ## theoretically be equal to the actual value of the condition number. |
5837 | 117 ## The method which computes @var{eigest} works only for symmetric positive |
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118 ## definite @var{A} and @var{m}, and the user is responsible for |
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119 ## verifying this assumption. |
5837 | 120 ## @end itemize |
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121 ## |
5837 | 122 ## Let us consider a trivial problem with a diagonal matrix (we exploit the |
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123 ## sparsity of A) |
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124 ## |
5837 | 125 ## @example |
126 ## @group | |
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127 ## n = 10; |
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128 ## A = diag (sparse (1:n)); |
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129 ## b = rand (n, 1); |
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130 ## [l, u, p, q] = luinc (A, 1.e-3); |
5837 | 131 ## @end group |
132 ## @end example | |
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133 ## |
5837 | 134 ## @sc{Example 1:} Simplest use of @code{pcg} |
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135 ## |
5837 | 136 ## @example |
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137 ## x = pcg (A,b) |
5837 | 138 ## @end example |
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139 ## |
5837 | 140 ## @sc{Example 2:} @code{pcg} with a function which computes |
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141 ## @code{@var{A} * @var{x}} |
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142 ## |
5837 | 143 ## @example |
144 ## @group | |
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145 ## function y = apply_a (x) |
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146 ## y = [1:N]' .* x; |
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147 ## endfunction |
5837 | 148 ## |
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149 ## x = pcg ("apply_a", b) |
5837 | 150 ## @end group |
151 ## @end example | |
6747 | 152 ## |
153 ## @sc{Example 3:} @code{pcg} with a preconditioner: @var{l} * @var{u} | |
154 ## | |
155 ## @example | |
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156 ## x = pcg (A, b, 1.e-6, 500, l*u) |
6747 | 157 ## @end example |
158 ## | |
159 ## @sc{Example 4:} @code{pcg} with a preconditioner: @var{l} * @var{u}. | |
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160 ## Faster than @sc{Example 3} since lower and upper triangular matrices |
6747 | 161 ## are easier to invert |
162 ## | |
163 ## @example | |
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164 ## x = pcg (A, b, 1.e-6, 500, l, u) |
6747 | 165 ## @end example |
166 ## | |
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167 ## @sc{Example 5:} Preconditioned iteration, with full diagnostics. The |
5837 | 168 ## preconditioner (quite strange, because even the original matrix |
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169 ## @var{A} is trivial) is defined as a function |
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170 ## |
5837 | 171 ## @example |
172 ## @group | |
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173 ## function y = apply_m (x) |
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174 ## k = floor (length (x) - 2); |
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175 ## y = x; |
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176 ## y(1:k) = x(1:k) ./ [1:k]'; |
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177 ## endfunction |
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178 ## |
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179 ## [x, flag, relres, iter, resvec, eigest] = ... |
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180 ## pcg (A, b, [], [], "apply_m"); |
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181 ## semilogy (1:iter+1, resvec); |
5837 | 182 ## @end group |
183 ## @end example | |
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184 ## |
6747 | 185 ## @sc{Example 6:} Finally, a preconditioner which depends on a |
5837 | 186 ## parameter @var{k}. |
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187 ## |
5837 | 188 ## @example |
189 ## @group | |
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190 ## function y = apply_M (x, varargin) |
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191 ## K = varargin@{1@}; |
6547 | 192 ## y = x; |
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193 ## y(1:K) = x(1:K) ./ [1:K]'; |
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194 ## endfunction |
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195 ## |
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196 ## [x, flag, relres, iter, resvec, eigest] = ... |
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197 ## pcg (A, b, [], [], "apply_m", [], [], 3) |
5837 | 198 ## @end group |
199 ## @end example | |
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200 ## |
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201 ## References: |
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202 ## |
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203 ## @enumerate |
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204 ## @item |
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205 ## C.T. Kelley, @cite{Iterative Methods for Linear and Nonlinear Equations}, |
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206 ## SIAM, 1995. (the base PCG algorithm) |
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207 ## |
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208 ## @item |
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209 ## Y. Saad, @cite{Iterative Methods for Sparse Linear Systems}, PWS 1996. |
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210 ## (condition number estimate from PCG) Revised version of this book is |
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211 ## available online at @url{http://www-users.cs.umn.edu/~saad/books.html} |
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212 ## @end enumerate |
5837 | 213 ## |
214 ## @seealso{sparse, pcr} | |
215 ## @end deftypefn | |
216 | |
5838 | 217 ## Author: Piotr Krzyzanowski <piotr.krzyzanowski@mimuw.edu.pl> |
6747 | 218 ## Modified by: Vittoria Rezzonico <vittoria.rezzonico@epfl.ch> |
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219 ## - Add the ability to provide the pre-conditioner as two separate matrices |
5837 | 220 |
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221 function [x, flag, relres, iter, resvec, eigest] = pcg (A, b, tol, maxit, m1, m2, x0, varargin) |
5837 | 222 |
8507 | 223 ## M = M1*M2 |
6747 | 224 |
225 if (nargin < 7 || isempty (x0)) | |
5838 | 226 x = zeros (size (b)); |
5837 | 227 else |
228 x = x0; | |
229 endif | |
230 | |
8507 | 231 if (nargin < 5 || isempty (m1)) |
232 exist_m1 = 0; | |
233 else | |
234 exist_m1 = 1; | |
235 endif | |
6747 | 236 |
8507 | 237 if (nargin < 6 || isempty (m2)) |
238 exist_m2 = 0; | |
239 else | |
240 exist_m2 = 1; | |
241 endif | |
5837 | 242 |
5838 | 243 if (nargin < 4 || isempty (maxit)) |
244 maxit = min (size (b, 1), 20); | |
5837 | 245 endif |
246 | |
5838 | 247 maxit += 2; |
5837 | 248 |
5838 | 249 if (nargin < 3 || isempty (tol)) |
5837 | 250 tol = 1e-6; |
251 endif | |
252 | |
253 preconditioned_residual_out = false; | |
254 if (nargout > 5) | |
5838 | 255 T = zeros (maxit, maxit); |
5837 | 256 preconditioned_residual_out = true; |
257 endif | |
258 | |
8506 | 259 ## Assume A is positive definite. |
260 matrix_positive_definite = true; | |
5837 | 261 |
5838 | 262 p = zeros (size (b)); |
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263 oldtau = 1; |
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264 if (isnumeric (A)) |
8506 | 265 ## A is a matrix. |
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266 r = b - A*x; |
8506 | 267 else |
268 ## A should be a function. | |
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269 r = b - feval (A, x, varargin{:}); |
5837 | 270 endif |
271 | |
5838 | 272 resvec(1,1) = norm (r); |
5837 | 273 alpha = 1; |
274 iter = 2; | |
275 | |
6747 | 276 while (resvec (iter-1,1) > tol * resvec (1,1) && iter < maxit) |
8507 | 277 if (exist_m1) |
278 if(isnumeric (m1)) | |
10549 | 279 y = m1 \ r; |
6747 | 280 else |
10549 | 281 y = feval (m1, r, varargin{:}); |
5837 | 282 endif |
6747 | 283 else |
284 y = r; | |
285 endif | |
8507 | 286 if (exist_m2) |
287 if (isnumeric (m2)) | |
10549 | 288 z = m2 \ y; |
6747 | 289 else |
10549 | 290 z = feval (m2, y, varargin{:}); |
6747 | 291 endif |
292 else | |
293 z = y; | |
5837 | 294 endif |
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295 tau = z' * r; |
6747 | 296 resvec (iter-1,2) = sqrt (tau); |
5838 | 297 beta = tau / oldtau; |
5837 | 298 oldtau = tau; |
6747 | 299 p = z + beta * p; |
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300 if (isnumeric (A)) |
8506 | 301 ## A is a matrix. |
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302 w = A * p; |
8506 | 303 else |
304 ## A should be a function. | |
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305 w = feval (A, p, varargin{:}); |
5837 | 306 endif |
8506 | 307 ## Needed only for eigest. |
308 oldalpha = alpha; | |
5838 | 309 alpha = tau / (p'*w); |
8506 | 310 if (alpha <= 0.0) |
311 ## Negative matrix. | |
5837 | 312 matrix_positive_definite = false; |
313 endif | |
6747 | 314 x += alpha * p; |
315 r -= alpha * w; | |
5838 | 316 if (nargout > 5 && iter > 2) |
5837 | 317 T(iter-1:iter, iter-1:iter) = T(iter-1:iter, iter-1:iter) + ... |
10549 | 318 [1 sqrt(beta); sqrt(beta) beta]./oldalpha; |
5837 | 319 ## EVS = eig(T(2:iter-1,2:iter-1)); |
320 ## fprintf(stderr,"PCG condest: %g (iteration: %d)\n", max(EVS)/min(EVS),iter); | |
321 endif | |
6747 | 322 resvec (iter,1) = norm (r); |
5838 | 323 iter++; |
5837 | 324 endwhile |
325 | |
326 if (nargout > 5) | |
5838 | 327 if (matrix_positive_definite) |
5837 | 328 if (iter > 3) |
10549 | 329 T = T(2:iter-2,2:iter-2); |
330 l = eig (T); | |
331 eigest = [min(l), max(l)]; | |
332 ## fprintf (stderr, "pcg condest: %g\n", eigest(2)/eigest(1)); | |
5837 | 333 else |
10549 | 334 eigest = [NaN, NaN]; |
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335 warning ("pcg: eigenvalue estimate failed: iteration converged too fast"); |
5837 | 336 endif |
337 else | |
5838 | 338 eigest = [NaN, NaN]; |
5837 | 339 endif |
340 | |
8506 | 341 ## Apply the preconditioner once more and finish with the precond |
342 ## residual. | |
8507 | 343 if (exist_m1) |
344 if (isnumeric (m1)) | |
10549 | 345 y = m1 \ r; |
6747 | 346 else |
10549 | 347 y = feval (m1, r, varargin{:}); |
5837 | 348 endif |
6747 | 349 else |
350 y = r; | |
5837 | 351 endif |
8507 | 352 if (exist_m2) |
353 if (isnumeric (m2)) | |
10549 | 354 z = m2 \ y; |
6747 | 355 else |
10549 | 356 z = feval (m2, y, varargin{:}); |
6747 | 357 endif |
358 else | |
359 z = y; | |
360 endif | |
361 | |
362 resvec (iter-1,2) = sqrt (r' * z); | |
5837 | 363 else |
5838 | 364 resvec = resvec(:,1); |
5837 | 365 endif |
366 | |
367 flag = 0; | |
6747 | 368 relres = resvec (iter-1,1) ./ resvec(1,1); |
5838 | 369 iter -= 2; |
6747 | 370 if (iter >= maxit - 2) |
5837 | 371 flag = 1; |
372 if (nargout < 2) | |
6498 | 373 warning ("pcg: maximum number of iterations (%d) reached\n", iter); |
6747 | 374 warning ("the initial residual norm was reduced %g times.\n", ... |
10549 | 375 1.0 / relres); |
5837 | 376 endif |
5838 | 377 elseif (nargout < 2) |
6498 | 378 fprintf (stderr, "pcg: converged in %d iterations. ", iter); |
379 fprintf (stderr, "the initial residual norm was reduced %g times.\n",... | |
10549 | 380 1.0/relres); |
5837 | 381 endif |
382 | |
5838 | 383 if (! matrix_positive_definite) |
5837 | 384 flag = 3; |
385 if (nargout < 2) | |
6498 | 386 warning ("pcg: matrix not positive definite?\n"); |
5837 | 387 endif |
388 endif | |
389 endfunction | |
390 | |
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391 |
5837 | 392 %!demo |
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393 %! # Simplest usage of pcg (see also 'help pcg') |
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394 %! |
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395 %! N = 10; |
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396 %! A = diag ([1:N]); b = rand (N, 1); |
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397 %! y = A \ b; # y is the true solution |
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398 %! x = pcg (A, b); |
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399 %! printf ("The solution relative error is %g\n", norm (x - y) / norm (y)); |
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400 %! |
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401 %! # You shouldn't be afraid if pcg issues some warning messages in this |
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402 %! # example: watch out in the second example, why it takes N iterations |
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403 %! # of pcg to converge to (a very accurate, by the way) solution |
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404 |
5837 | 405 %!demo |
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406 %! # Full output from pcg, except for the eigenvalue estimates |
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407 %! # We use this output to plot the convergence history |
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408 %! |
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409 %! N = 10; |
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410 %! A = diag ([1:N]); b = rand (N, 1); |
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411 %! X = A \ b; # X is the true solution |
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412 %! [x, flag, relres, iter, resvec] = pcg (A, b); |
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413 %! printf ("The solution relative error is %g\n", norm (x - X) / norm (X)); |
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414 %! title ("Convergence history"); |
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415 %! semilogy ([0:iter], resvec / resvec(1), "o-g"); |
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416 %! xlabel ("Iteration"); ylabel ("log(||b-Ax||/||b||)"); |
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417 %! legend ("relative residual"); |
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418 |
5837 | 419 %!demo |
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420 %! # Full output from pcg, including the eigenvalue estimates |
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421 %! # Hilbert matrix is extremely ill-conditioned, so pcg WILL have problems |
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422 %! |
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423 %! N = 10; |
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424 %! A = hilb (N); b = rand (N, 1); |
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425 %! X = A \ b; # X is the true solution |
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426 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], 200); |
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427 %! printf ("The solution relative error is %g\n", norm (x - X) / norm (X)); |
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428 %! printf ("Condition number estimate is %g\n", eigest(2) / eigest(1)); |
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429 %! printf ("Actual condition number is %g\n", cond (A)); |
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430 %! title ("Convergence history"); |
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431 %! semilogy ([0:iter], resvec, ["o-g";"+-r"]); |
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432 %! xlabel ("Iteration"); ylabel ("log(||b-Ax||)"); |
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433 %! legend ("absolute residual", "absolute preconditioned residual"); |
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434 |
5837 | 435 %!demo |
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436 %! # Full output from pcg, including the eigenvalue estimates |
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437 %! # We use the 1-D Laplacian matrix for A, and cond(A) = O(N^2) |
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438 %! # and that's the reason we need some preconditioner; here we take |
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439 %! # a very simple and not powerful Jacobi preconditioner, |
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440 %! # which is the diagonal of A |
5837 | 441 %! |
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442 %! N = 100; |
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443 %! A = zeros (N, N); |
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444 %! for i = 1 : N - 1 # form 1-D Laplacian matrix |
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445 %! A(i:i+1, i:i+1) = [2 -1; -1 2]; |
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446 %! endfor |
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447 %! b = rand (N, 1); |
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448 %! X = A \ b; # X is the true solution |
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449 %! maxit = 80; |
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450 %! printf ("System condition number is %g\n", cond (A)); |
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451 %! # No preconditioner: the convergence is very slow! |
5837 | 452 %! |
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453 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit); |
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454 %! printf ("System condition number estimate is %g\n", eigest(2) / eigest(1)); |
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455 %! title ("Convergence history"); |
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456 %! semilogy ([0:iter], resvec(:,1), "o-g"); |
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457 %! xlabel ("Iteration"); ylabel ("log(||b-Ax||)"); |
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458 %! legend ("NO preconditioning: absolute residual"); |
5837 | 459 %! |
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460 %! pause (1); |
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461 %! # Test Jacobi preconditioner: it will not help much!!! |
5837 | 462 %! |
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463 %! M = diag (diag (A)); # Jacobi preconditioner |
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464 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit, M); |
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465 %! printf ("JACOBI preconditioned system condition number estimate is %g\n", eigest(2) / eigest(1)); |
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466 %! hold on; |
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467 %! semilogy ([0:iter], resvec(:,1), "o-r"); |
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468 %! legend ("NO preconditioning: absolute residual", ... |
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469 %! "JACOBI preconditioner: absolute residual"); |
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470 %! |
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471 %! pause (1); |
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472 %! # Test nonoverlapping block Jacobi preconditioner: it will help much! |
5837 | 473 %! |
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474 %! M = zeros (N, N); k = 4; |
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475 %! for i = 1 : k : N # form 1-D Laplacian matrix |
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476 %! M(i:i+k-1, i:i+k-1) = A(i:i+k-1, i:i+k-1); |
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477 %! endfor |
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478 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit, M); |
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479 %! printf ("BLOCK JACOBI preconditioned system condition number estimate is %g\n", eigest(2) / eigest(1)); |
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480 %! semilogy ([0:iter], resvec(:,1), "o-b"); |
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481 %! legend ("NO preconditioning: absolute residual", ... |
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482 %! "JACOBI preconditioner: absolute residual", ... |
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483 %! "BLOCK JACOBI preconditioner: absolute residual"); |
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484 %! hold off; |
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485 |
5837 | 486 %!test |
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487 %! # solve small diagonal system |
5837 | 488 %! |
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489 %! N = 10; |
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490 %! A = diag ([1:N]); b = rand (N, 1); |
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491 %! X = A \ b; # X is the true solution |
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492 %! [x, flag] = pcg (A, b, [], N+1); |
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493 %! assert (norm (x - X) / norm (X), 0, 1e-10); |
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494 %! assert (flag, 0); |
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495 |
5837 | 496 %!test |
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497 %! # solve small indefinite diagonal system |
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498 %! # despite A is indefinite, the iteration continues and converges |
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499 %! # indefiniteness of A is detected |
5837 | 500 %! |
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501 %! N = 10; |
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502 %! A = diag([1:N] .* (-ones(1, N) .^ 2)); b = rand (N, 1); |
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503 %! X = A \ b; # X is the true solution |
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504 %! [x, flag] = pcg (A, b, [], N+1); |
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505 %! assert (norm (x - X) / norm (X), 0, 1e-10); |
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506 %! assert (flag, 3); |
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507 |
5837 | 508 %!test |
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509 %! # solve tridiagonal system, do not converge in default 20 iterations |
5837 | 510 %! |
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511 %! N = 100; |
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512 %! A = zeros (N, N); |
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513 %! for i = 1 : N - 1 # form 1-D Laplacian matrix |
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514 %! A(i:i+1, i:i+1) = [2 -1; -1 2]; |
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515 %! endfor |
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516 %! b = ones (N, 1); |
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517 %! X = A \ b; # X is the true solution |
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518 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, 1e-12); |
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519 %! assert (flag); |
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520 %! assert (relres > 1.0); |
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521 %! assert (iter, 20); # should perform max allowable default number of iterations |
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522 |
5837 | 523 %!test |
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524 %! # solve tridiagonal system with 'perfect' preconditioner |
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525 %! # which converges in one iteration, so the eigest does not |
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526 %! # work and issues a warning |
5837 | 527 %! |
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528 %! N = 100; |
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529 %! A = zeros (N, N); |
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530 %! for i = 1 : N - 1 # form 1-D Laplacian matrix |
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531 %! A (i:i+1, i:i+1) = [2 -1; -1 2]; |
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532 %! endfor |
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533 %! b = ones (N, 1); |
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534 %! X = A \ b; # X is the true solution |
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535 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], [], A, [], b); |
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536 %! assert (norm (x - X) / norm (X), 0, 1e-6); |
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537 %! assert (flag, 0); |
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538 %! assert (iter, 1); # should converge in one iteration |
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539 %! assert (isnan (eigest), isnan ([NaN, NaN])); |
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540 |