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1 ## Copyright (C) 2004, 2006, 2007 Piotr Krzyzanowski |
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2 ## |
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3 ## This file is part of Octave. |
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4 ## |
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5 ## Octave is free software; you can redistribute it and/or modify it |
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6 ## under the terms of the GNU General Public License as published by |
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7 ## the Free Software Foundation; either version 3 of the License, or (at |
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8 ## your option) any later version. |
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9 ## |
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10 ## Octave is distributed in the hope that it will be useful, but |
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11 ## WITHOUT ANY WARRANTY; without even the implied warranty of |
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12 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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13 ## General Public License for more details. |
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14 ## |
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15 ## You should have received a copy of the GNU General Public License |
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16 ## along with Octave; see the file COPYING. If not, see |
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17 ## <http://www.gnu.org/licenses/>. |
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18 |
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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{}) |
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21 ## @deftypefnx {Function File} {[@var{x}, @var{flag}, @var{relres}, @var{iter}, @var{resvec}, @var{eigest}] =} pcg (@dots{}) |
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22 ## |
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23 ## Solves the linear system of equations @code{@var{a} * @var{x} = |
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24 ## @var{b}} by means of the Preconditioned Conjugate Gradient iterative |
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25 ## method. The input arguments are |
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26 ## |
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27 ## @itemize |
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28 ## @item |
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29 ## @var{a} can be either a square (preferably sparse) matrix or a |
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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 |
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34 ## message and the @var{flag} output parameter will be set. |
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35 ## |
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36 ## @item |
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37 ## @var{b} is the right hand side vector. |
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38 ## |
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39 ## @item |
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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 @code{norm |
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42 ## (@var{b} - @var{a} * @var{x}) <= @var{tol} * norm (@var{b} - @var{a} * |
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43 ## @var{x0})}. If @var{tol} is empty or is omitted, the function sets |
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44 ## @code{@var{tol} = 1e-6} by default. |
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45 ## |
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46 ## @item |
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47 ## @var{maxit} is the maximum allowable number of iterations; if |
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48 ## @code{[]} is supplied for @code{maxit}, or @code{pcg} has less |
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49 ## arguments, a default value equal to 20 is used. |
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50 ## |
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51 ## @item |
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52 ## @var{m} = @var{m1} * @var{m2} is the (left) preconditioning matrix, so that the iteration is |
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53 ## (theoretically) equivalent to solving by @code{pcg} @code{@var{P} * |
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54 ## @var{x} = @var{m} \ @var{b}}, with @code{@var{P} = @var{m} \ @var{a}}. |
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55 ## Note that a proper choice of the preconditioner may dramatically |
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56 ## improve the overall performance of the method. Instead of matrices |
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57 ## @var{m1} and @var{m2}, the user may pass two functions which return |
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58 ## the results of applying the inverse of @var{m1} and @var{m2} to |
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59 ## a vector (usually this is the preferred way of using the preconditioner). |
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60 ## If @code{[]} is supplied for @var{m1}, or @var{m1} is omitted, no |
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61 ## preconditioning is applied. If @var{m2} is omitted, @var{m} = @var{m1} |
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62 ## will be used as preconditioner. |
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63 ## |
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64 ## @item |
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65 ## @var{x0} is the initial guess. If @var{x0} is empty or omitted, the |
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66 ## function sets @var{x0} to a zero vector by default. |
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67 ## @end itemize |
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68 ## |
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69 ## The arguments which follow @var{x0} are treated as parameters, and |
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70 ## passed in a proper way to any of the functions (@var{a} or @var{m}) |
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71 ## which are passed to @code{pcg}. See the examples below for further |
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72 ## details. The output arguments are |
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73 ## |
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74 ## @itemize |
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75 ## @item |
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76 ## @var{x} is the computed approximation to the solution of |
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77 ## @code{@var{a} * @var{x} = @var{b}}. |
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78 ## |
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79 ## @item |
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80 ## @var{flag} reports on the convergence. @code{@var{flag} = 0} means |
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81 ## the solution converged and the tolerance criterion given by @var{tol} |
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82 ## is satisfied. @code{@var{flag} = 1} means that the @var{maxit} limit |
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83 ## for the iteration count was reached. @code{@var{flag} = 3} reports that |
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84 ## the (preconditioned) matrix was found not positive definite. |
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85 ## |
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86 ## @item |
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87 ## @var{relres} is the ratio of the final residual to its initial value, |
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88 ## measured in the Euclidean norm. |
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89 ## |
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90 ## @item |
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91 ## @var{iter} is the actual number of iterations performed. |
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92 ## |
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93 ## @item |
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94 ## @var{resvec} describes the convergence history of the method. |
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95 ## @code{@var{resvec} (i,1)} is the Euclidean norm of the residual, and |
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96 ## @code{@var{resvec} (i,2)} is the preconditioned residual norm, |
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97 ## after the (@var{i}-1)-th iteration, @code{@var{i} = |
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98 ## 1, 2, @dots{}, @var{iter}+1}. The preconditioned residual norm |
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99 ## is defined as |
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100 ## @code{norm (@var{r}) ^ 2 = @var{r}' * (@var{m} \ @var{r})} where |
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101 ## @code{@var{r} = @var{b} - @var{a} * @var{x}}, see also the |
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102 ## description of @var{m}. If @var{eigest} is not required, only |
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103 ## @code{@var{resvec} (:,1)} is returned. |
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104 ## |
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105 ## @item |
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106 ## @var{eigest} returns the estimate for the smallest @code{@var{eigest} |
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107 ## (1)} and largest @code{@var{eigest} (2)} eigenvalues of the |
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108 ## preconditioned matrix @code{@var{P} = @var{m} \ @var{a}}. In |
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109 ## particular, if no preconditioning is used, the estimates for the |
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110 ## extreme eigenvalues of @var{a} are returned. @code{@var{eigest} (1)} |
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111 ## is an overestimate and @code{@var{eigest} (2)} is an underestimate, |
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112 ## so that @code{@var{eigest} (2) / @var{eigest} (1)} is a lower bound |
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113 ## for @code{cond (@var{P}, 2)}, which nevertheless in the limit should |
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114 ## theoretically be equal to the actual value of the condition number. |
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115 ## The method which computes @var{eigest} works only for symmetric positive |
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116 ## definite @var{a} and @var{m}, and the user is responsible for |
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117 ## verifying this assumption. |
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118 ## @end itemize |
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119 ## |
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120 ## Let us consider a trivial problem with a diagonal matrix (we exploit the |
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121 ## sparsity of A) |
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122 ## |
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123 ## @example |
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124 ## @group |
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125 ## N = 10; |
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126 ## A = spdiag ([1:N]); |
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127 ## b = rand (N, 1); |
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128 ## [L, U, P, Q] = luinc (A,1.e-3); |
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129 ## @end group |
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130 ## @end example |
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131 ## |
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132 ## @sc{Example 1:} Simplest use of @code{pcg} |
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133 ## |
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134 ## @example |
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135 ## x = pcg(A,b) |
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136 ## @end example |
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137 ## |
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138 ## @sc{Example 2:} @code{pcg} with a function which computes |
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139 ## @code{@var{a} * @var{x}} |
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140 ## |
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141 ## @example |
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142 ## @group |
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143 ## function y = applyA (x) |
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144 ## y = [1:N]'.*x; |
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145 ## endfunction |
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146 ## |
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147 ## x = pcg ("applyA", b) |
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148 ## @end group |
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149 ## @end example |
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150 ## |
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151 ## @sc{Example 3:} @code{pcg} with a preconditioner: @var{l} * @var{u} |
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152 ## |
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153 ## @example |
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154 ## x=pcg(A,b,1.e-6,500,L*U); |
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155 ## @end example |
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156 ## |
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157 ## @sc{Example 4:} @code{pcg} with a preconditioner: @var{l} * @var{u}. |
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158 ## Faster than @sc{Example 3} since lower and upper triangular matrices |
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159 ## are easier to invert |
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160 ## |
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161 ## @example |
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162 ## x=pcg(A,b,1.e-6,500,L,U); |
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163 ## @end example |
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164 ## |
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165 ## @sc{Example 5:} Preconditioned iteration, with full diagnostics. The |
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166 ## preconditioner (quite strange, because even the original matrix |
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167 ## @var{a} is trivial) is defined as a function |
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168 ## |
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169 ## @example |
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170 ## @group |
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171 ## function y = applyM(x) |
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172 ## K = floor (length (x) - 2); |
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173 ## y = x; |
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174 ## y(1:K) = x(1:K)./[1:K]'; |
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175 ## endfunction |
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176 ## |
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177 ## [x, flag, relres, iter, resvec, eigest] = ... |
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178 ## pcg (A, b, [], [], "applyM"); |
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179 ## semilogy (1:iter+1, resvec); |
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180 ## @end group |
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181 ## @end example |
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182 ## |
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183 ## @sc{Example 6:} Finally, a preconditioner which depends on a |
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184 ## parameter @var{k}. |
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185 ## |
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186 ## @example |
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187 ## @group |
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188 ## function y = applyM (x, varargin) |
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189 ## K = varargin@{1@}; |
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190 ## y = x; |
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191 ## y(1:K) = x(1:K)./[1:K]'; |
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192 ## endfunction |
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193 ## |
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194 ## [x, flag, relres, iter, resvec, eigest] = ... |
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195 ## pcg (A, b, [], [], "applyM", [], [], 3) |
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196 ## @end group |
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197 ## @end example |
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198 ## |
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199 ## @sc{References} |
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200 ## |
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201 ## [1] C.T.Kelley, 'Iterative methods for linear and nonlinear equations', |
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202 ## SIAM, 1995 (the base PCG algorithm) |
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203 ## |
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204 ## [2] Y.Saad, 'Iterative methods for sparse linear systems', PWS 1996 |
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205 ## (condition number estimate from PCG) Revised version of this book is |
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206 ## available online at http://www-users.cs.umn.edu/~saad/books.html |
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207 ## |
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208 ## |
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209 ## @seealso{sparse, pcr} |
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210 ## @end deftypefn |
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211 |
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212 ## Author: Piotr Krzyzanowski <piotr.krzyzanowski@mimuw.edu.pl> |
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213 ## Modified by: Vittoria Rezzonico <vittoria.rezzonico@epfl.ch> |
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214 ## - Add the ability to provide the pre-conditioner as two separate |
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215 ## matrices |
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216 |
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217 function [x, flag, relres, iter, resvec, eigest] = pcg (A, b, tol, maxit, M1, M2, x0, varargin) |
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218 |
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219 ## M = M1*M2 |
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220 |
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221 if (nargin < 7 || isempty (x0)) |
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222 x = zeros (size (b)); |
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223 else |
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224 x = x0; |
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225 endif |
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226 |
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227 if ((nargin < 5) || isempty (M1)) |
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228 existM1 = 0; |
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229 else |
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230 existM1 = 1; |
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231 endif |
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232 |
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233 if ((nargin < 6) || isempty (M2)) |
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234 existM2 = 0; |
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235 else |
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236 existM2 = 1; |
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237 endif |
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238 |
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239 if (nargin < 4 || isempty (maxit)) |
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240 maxit = min (size (b, 1), 20); |
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241 endif |
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242 |
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243 maxit += 2; |
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244 |
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245 if (nargin < 3 || isempty (tol)) |
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246 tol = 1e-6; |
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247 endif |
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248 |
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249 preconditioned_residual_out = false; |
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250 if (nargout > 5) |
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251 T = zeros (maxit, maxit); |
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252 preconditioned_residual_out = true; |
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253 endif |
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254 |
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255 matrix_positive_definite = true; # assume A is positive definite |
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256 |
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257 p = zeros (size (b)); |
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258 oldtau = 1; |
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259 if (isnumeric (A)) # is A a matrix? |
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260 r = b - A*x; |
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261 else # then A should be a function! |
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262 r = b - feval (A, x, varargin{:}); |
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263 endif |
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264 |
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265 resvec(1,1) = norm (r); |
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266 alpha = 1; |
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267 iter = 2; |
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268 |
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269 while (resvec (iter-1,1) > tol * resvec (1,1) && iter < maxit) |
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270 if (existM1) |
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271 if(isnumeric (M1)) |
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272 y = M1 \ r; |
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273 else |
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274 y = feval (M1, r, varargin{:}); |
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275 endif |
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276 else |
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277 y = r; |
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278 endif |
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279 if (existM2) |
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280 if (isnumeric (M2)) |
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281 z = M2 \ y; |
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282 else |
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283 z = feval (M2, y, varargin{:}); |
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284 endif |
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285 else |
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286 z = y; |
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287 endif |
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288 tau = z' * r; |
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289 resvec (iter-1,2) = sqrt (tau); |
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290 beta = tau / oldtau; |
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291 oldtau = tau; |
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292 p = z + beta * p; |
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293 if (isnumeric (A)) # is A a matrix? |
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294 w = A * p; |
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295 else # then A should be a function! |
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296 w = feval (A, p, varargin{:}); |
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297 endif |
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298 oldalpha = alpha; # needed only for eigest |
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299 alpha = tau / (p'*w); |
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300 if (alpha <= 0.0) # negative matrix? |
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301 matrix_positive_definite = false; |
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302 endif |
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303 x += alpha * p; |
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304 r -= alpha * w; |
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305 if (nargout > 5 && iter > 2) |
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306 T(iter-1:iter, iter-1:iter) = T(iter-1:iter, iter-1:iter) + ... |
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307 [1 sqrt(beta); sqrt(beta) beta]./oldalpha; |
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308 ## EVS = eig(T(2:iter-1,2:iter-1)); |
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309 ## fprintf(stderr,"PCG condest: %g (iteration: %d)\n", max(EVS)/min(EVS),iter); |
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310 endif |
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311 resvec (iter,1) = norm (r); |
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312 iter++; |
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313 endwhile |
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314 |
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315 if (nargout > 5) |
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316 if (matrix_positive_definite) |
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317 if (iter > 3) |
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318 T = T(2:iter-2,2:iter-2); |
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319 l = eig (T); |
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320 eigest = [min(l), max(l)]; |
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321 ## fprintf (stderr, "pcg condest: %g\n", eigest(2)/eigest(1)); |
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322 else |
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323 eigest = [NaN, NaN]; |
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324 warning ("pcg: eigenvalue estimate failed: iteration converged too fast."); |
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325 endif |
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326 else |
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327 eigest = [NaN, NaN]; |
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328 endif |
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329 |
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330 ## apply the preconditioner once more and finish with the precond |
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331 ## residual |
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332 if (existM1) |
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333 if(isnumeric (M1)) |
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334 y = M1 \ r; |
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335 else |
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336 y = feval (M1, r, varargin{:}); |
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337 endif |
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338 else |
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339 y = r; |
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340 endif |
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341 if (existM2) |
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342 if (isnumeric (M2)) |
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343 z = M2 \ y; |
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344 else |
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345 z = feval (M2, y, varargin{:}); |
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346 endif |
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347 else |
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348 z = y; |
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349 endif |
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350 |
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351 resvec (iter-1,2) = sqrt (r' * z); |
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352 else |
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353 resvec = resvec(:,1); |
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354 endif |
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355 |
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356 flag = 0; |
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357 relres = resvec (iter-1,1) ./ resvec(1,1); |
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358 iter -= 2; |
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359 if (iter >= maxit - 2) |
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360 flag = 1; |
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361 if (nargout < 2) |
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362 warning ("pcg: maximum number of iterations (%d) reached\n", iter); |
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363 warning ("the initial residual norm was reduced %g times.\n", ... |
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364 1.0 / relres); |
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365 endif |
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366 elseif (nargout < 2) |
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367 fprintf (stderr, "pcg: converged in %d iterations. ", iter); |
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368 fprintf (stderr, "the initial residual norm was reduced %g times.\n",... |
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369 1.0/relres); |
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370 endif |
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371 |
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372 if (! matrix_positive_definite) |
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373 flag = 3; |
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374 if (nargout < 2) |
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375 warning ("pcg: matrix not positive definite?\n"); |
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376 endif |
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377 endif |
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378 endfunction |
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379 |
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380 %!demo |
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381 %! |
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382 %! # Simplest usage of pcg (see also 'help pcg') |
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383 %! |
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384 %! N = 10; |
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385 %! A = diag ([1:N]); b = rand (N, 1); y = A \ b; #y is the true solution |
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386 %! x = pcg (A, b); |
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387 %! printf('The solution relative error is %g\n', norm (x - y) / norm (y)); |
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388 %! |
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389 %! # You shouldn't be afraid if pcg issues some warning messages in this |
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390 %! # example: watch out in the second example, why it takes N iterations |
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391 %! # of pcg to converge to (a very accurate, by the way) solution |
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392 %!demo |
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393 %! |
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394 %! # Full output from pcg, except for the eigenvalue estimates |
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395 %! # We use this output to plot the convergence history |
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396 %! |
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397 %! N = 10; |
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398 %! A = diag ([1:N]); b = rand (N, 1); X = A \ b; #X is the true solution |
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399 %! [x, flag, relres, iter, resvec] = pcg (A, b); |
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400 %! printf('The solution relative error is %g\n', norm (x - X) / norm (X)); |
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401 %! title('Convergence history'); xlabel('Iteration'); ylabel('log(||b-Ax||/||b||)'); |
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402 %! semilogy([0:iter], resvec / resvec(1),'o-g'); |
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403 %! legend('relative residual'); |
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404 %!demo |
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405 %! |
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406 %! # Full output from pcg, including the eigenvalue estimates |
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407 %! # Hilbert matrix is extremely ill conditioned, so pcg WILL have problems |
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408 %! |
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409 %! N = 10; |
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410 %! A = hilb (N); b = rand (N, 1); X = A \ b; #X is the true solution |
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411 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], 200); |
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412 %! printf('The solution relative error is %g\n', norm (x - X) / norm (X)); |
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413 %! printf('Condition number estimate is %g\n', eigest(2) / eigest (1)); |
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414 %! printf('Actual condition number is %g\n', cond (A)); |
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415 %! title('Convergence history'); xlabel('Iteration'); ylabel('log(||b-Ax||)'); |
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416 %! semilogy([0:iter], resvec,['o-g';'+-r']); |
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417 %! legend('absolute residual','absolute preconditioned residual'); |
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418 %!demo |
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419 %! |
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420 %! # Full output from pcg, including the eigenvalue estimates |
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421 %! # We use the 1-D Laplacian matrix for A, and cond(A) = O(N^2) |
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422 %! # and that's the reasone we need some preconditioner; here we take |
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423 %! # a very simple and not powerful Jacobi preconditioner, |
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424 %! # which is the diagonal of A |
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425 %! |
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426 %! N = 100; |
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427 %! A = zeros (N, N); |
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428 %! for i=1 : N - 1 # form 1-D Laplacian matrix |
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429 %! A (i:i+1, i:i+1) = [2 -1; -1 2]; |
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430 %! endfor |
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431 %! b = rand (N, 1); X = A \ b; #X is the true solution |
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432 %! maxit = 80; |
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433 %! printf('System condition number is %g\n', cond (A)); |
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434 %! # No preconditioner: the convergence is very slow! |
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435 %! |
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436 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit); |
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437 %! printf('System condition number estimate is %g\n', eigest(2) / eigest(1)); |
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438 %! title('Convergence history'); xlabel('Iteration'); ylabel('log(||b-Ax||)'); |
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439 %! semilogy([0:iter], resvec(:,1), 'o-g'); |
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440 %! legend('NO preconditioning: absolute residual'); |
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441 %! |
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442 %! pause(1); |
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443 %! # Test Jacobi preconditioner: it will not help much!!! |
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444 %! |
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445 %! M = diag (diag (A)); # Jacobi preconditioner |
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446 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit, M); |
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447 %! printf('JACOBI preconditioned system condition number estimate is %g\n', eigest(2) / eigest(1)); |
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448 %! hold on; |
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449 %! semilogy([0:iter], resvec(:,1), 'o-r'); |
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450 %! legend('NO preconditioning: absolute residual', ... |
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451 %! 'JACOBI preconditioner: absolute residual'); |
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452 %! |
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453 %! pause(1); |
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454 %! # Test nonoverlapping block Jacobi preconditioner: it will help much! |
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455 %! |
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456 %! M = zeros (N, N); k = 4; |
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457 %! for i = 1 : k : N # form 1-D Laplacian matrix |
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458 %! M (i:i+k-1, i:i+k-1) = A (i:i+k-1, i:i+k-1); |
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459 %! endfor |
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460 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], maxit, M); |
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461 %! printf('BLOCK JACOBI preconditioned system condition number estimate is %g\n', eigest(2) / eigest(1)); |
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462 %! semilogy ([0:iter], resvec(:,1),'o-b'); |
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463 %! legend('NO preconditioning: absolute residual', ... |
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464 %! 'JACOBI preconditioner: absolute residual', ... |
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465 %! 'BLOCK JACOBI preconditioner: absolute residual'); |
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466 %! hold off; |
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467 %!test |
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468 %! |
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469 %! #solve small diagonal system |
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470 %! |
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471 %! N = 10; |
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472 %! A = diag ([1:N]); b = rand (N, 1); X = A \ b; #X is the true solution |
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473 %! [x, flag] = pcg (A, b, [], N+1); |
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474 %! assert(norm (x - X) / norm (X), 0, 1e-10); |
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475 %! assert(flag, 0); |
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476 %! |
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477 %!test |
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478 %! |
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479 %! #solve small indefinite diagonal system |
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480 %! #despite A is indefinite, the iteration continues and converges |
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481 %! #indefiniteness of A is detected |
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482 %! |
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483 %! N = 10; |
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484 %! A = diag([1:N] .* (-ones(1, N) .^ 2)); b = rand (N, 1); X = A \ b; #X is the true solution |
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485 %! [x, flag] = pcg (A, b, [], N+1); |
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486 %! assert(norm (x - X) / norm (X), 0, 1e-10); |
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487 %! assert(flag, 3); |
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488 %! |
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489 %!test |
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490 %! |
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491 %! #solve tridiagonal system, do not converge in default 20 iterations |
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492 %! |
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493 %! N = 100; |
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494 %! A = zeros (N, N); |
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495 %! for i = 1 : N - 1 # form 1-D Laplacian matrix |
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496 %! A (i:i+1, i:i+1) = [2 -1; -1 2]; |
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497 %! endfor |
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498 %! b = ones (N, 1); X = A \ b; #X is the true solution |
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499 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, 1e-12); |
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500 %! assert(flag); |
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501 %! assert(relres > 1.0); |
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502 %! assert(iter, 20); #should perform max allowable default number of iterations |
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503 %! |
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504 %!test |
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505 %! |
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506 %! #solve tridiagonal system with 'prefect' preconditioner |
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507 %! #converges in one iteration, so the eigest does not work |
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508 %! #and issues a warning |
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509 %! |
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510 %! N = 100; |
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511 %! A = zeros (N, N); |
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512 %! for i = 1 : N - 1 # form 1-D Laplacian matrix |
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513 %! A (i:i+1, i:i+1) = [2 -1; -1 2]; |
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514 %! endfor |
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515 %! b = ones (N, 1); X = A \ b; #X is the true solution |
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516 %! [x, flag, relres, iter, resvec, eigest] = pcg (A, b, [], [], A, [], b); |
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517 %! assert(norm (x - X) / norm (X), 0, 1e-6); |
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518 %! assert(flag, 0); |
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519 %! assert(iter, 1); #should converge in one iteration |
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520 %! assert(isnan (eigest), isnan ([NaN, NaN])); |
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521 %! |
