Mercurial > hg > octave-lyh
annotate scripts/optimization/fminunc.m @ 10687:a8ce6bdecce5
Improve documentation strings.
author | Rik <octave@nomad.inbox5.com> |
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date | Tue, 08 Jun 2010 20:22:38 -0700 |
parents | 95c3e38098bf |
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1 ## Copyright (C) 2008, 2009 VZLU Prague, a.s. |
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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 ## Author: Jaroslav Hajek <highegg@gmail.com> |
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20 |
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21 ## -*- texinfo -*- |
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22 ## @deftypefn {Function File} {} fminunc (@var{fcn}, @var{x0}) |
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23 ## @deftypefnx {Function File} {} fminunc (@var{fcn}, @var{x0}, @var{options}) |
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24 ## @deftypefnx {Function File} {[@var{x}, @var{fvec}, @var{info}, @var{output}, @var{grad}, @var{hess}]} = fminunc (@var{fcn}, @dots{}) |
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25 ## Solve an unconstrained optimization problem defined by the function @var{fcn}. |
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26 ## @var{fcn} should accepts a vector (array) defining the unknown variables, |
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27 ## and return the objective function value, optionally with gradient. |
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28 ## In other words, this function attempts to determine a vector @var{x} such |
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29 ## that @code{@var{fcn} (@var{x})} is a local minimum. |
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30 ## @var{x0} determines a starting guess. The shape of @var{x0} is preserved |
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31 ## in all calls to @var{fcn}, but otherwise is treated as a column vector. |
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32 ## @var{options} is a structure specifying additional options. |
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33 ## Currently, @code{fminunc} recognizes these options: |
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34 ## @code{"FunValCheck"}, @code{"OutputFcn"}, @code{"TolX"}, |
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35 ## @code{"TolFun"}, @code{"MaxIter"}, @code{"MaxFunEvals"}, |
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36 ## @code{"GradObj"}, @code{"FinDiffType"}, |
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37 ## @code{"TypicalX"}, @code{"AutoScaling"}. |
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38 ## |
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39 ## If @code{"GradObj"} is @code{"on"}, it specifies that @var{fcn}, |
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40 ## called with 2 output arguments, also returns the Jacobian matrix |
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41 ## of right-hand sides at the requested point. @code{"TolX"} specifies |
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42 ## the termination tolerance in the unknown variables, while |
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43 ## @code{"TolFun"} is a tolerance for equations. Default is @code{1e-7} |
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44 ## for both @code{"TolX"} and @code{"TolFun"}. |
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45 ## |
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46 ## For description of the other options, see @code{optimset}. |
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47 ## |
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48 ## On return, @var{fval} contains the value of the function @var{fcn} |
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49 ## evaluated at @var{x}, and @var{info} may be one of the following values: |
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50 ## |
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51 ## @table @asis |
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52 ## @item 1 |
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53 ## Converged to a solution point. Relative gradient error is less than specified |
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54 ## by TolFun. |
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55 ## @item 2 |
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56 ## Last relative step size was less that TolX. |
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57 ## @item 3 |
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58 ## Last relative decrease in func value was less than TolF. |
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59 ## @item 0 |
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60 ## Iteration limit exceeded. |
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61 ## @item -3 |
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62 ## The trust region radius became excessively small. |
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63 ## @end table |
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64 ## |
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65 ## Optionally, fminunc can also yield a structure with convergence statistics |
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66 ## (@var{output}), the output gradient (@var{grad}) and approximate hessian |
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67 ## (@var{hess}). |
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68 ## |
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69 ## Note: If you only have a single nonlinear equation of one variable, using |
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70 ## @code{fminbnd} is usually a much better idea. |
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71 ## @seealso{fminbnd, optimset} |
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72 ## @end deftypefn |
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73 |
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74 ## PKG_ADD: __all_opts__ ("fminunc"); |
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75 |
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76 function [x, fval, info, output, grad, hess] = fminunc (fcn, x0, options = struct ()) |
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77 |
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78 ## Get default options if requested. |
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79 if (nargin == 1 && ischar (fcn) && strcmp (fcn, 'defaults')) |
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80 x = optimset ("MaxIter", 400, "MaxFunEvals", Inf, \ |
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81 "GradObj", "off", "TolX", 1e-7, "TolFun", 1e-7, |
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82 "OutputFcn", [], "FunValCheck", "off", |
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83 "FinDiffType", "central", |
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84 "TypicalX", [], "AutoScaling", "off"); |
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85 return; |
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86 endif |
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87 |
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88 if (nargin < 2 || nargin > 3 || ! ismatrix (x0)) |
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89 print_usage (); |
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90 endif |
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91 |
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92 if (ischar (fcn)) |
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93 fcn = str2func (fcn, "global"); |
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94 endif |
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95 |
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96 xsiz = size (x0); |
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97 n = numel (x0); |
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98 |
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99 has_grad = strcmpi (optimget (options, "GradObj", "off"), "on"); |
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100 cdif = strcmpi (optimget (options, "FinDiffType", "central"), "central"); |
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101 maxiter = optimget (options, "MaxIter", 400); |
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102 maxfev = optimget (options, "MaxFunEvals", Inf); |
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103 outfcn = optimget (options, "OutputFcn"); |
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104 |
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105 ## Get scaling matrix using the TypicalX option. If set to "auto", the |
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106 ## scaling matrix is estimated using the jacobian. |
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107 typicalx = optimget (options, "TypicalX"); |
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108 if (isempty (typicalx)) |
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109 typicalx = ones (n, 1); |
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110 endif |
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111 autoscale = strcmpi (optimget (options, "AutoScaling", "off"), "on"); |
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112 if (! autoscale) |
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113 dg = 1 ./ typicalx; |
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114 endif |
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115 |
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116 funvalchk = strcmpi (optimget (options, "FunValCheck", "off"), "on"); |
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117 |
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118 if (funvalchk) |
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119 ## Replace fcn with a guarded version. |
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120 fcn = @(x) guarded_eval (fcn, x); |
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121 endif |
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122 |
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123 ## These defaults are rather stringent. I think that normally, user |
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124 ## prefers accuracy to performance. |
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125 |
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126 macheps = eps (class (x0)); |
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127 |
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128 tolx = optimget (options, "TolX", 1e-7); |
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129 tolf = optimget (options, "TolFun", 1e-7); |
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130 |
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131 factor = 0.1; |
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132 ## FIXME: TypicalX corresponds to user scaling (???) |
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133 autodg = true; |
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134 |
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135 niter = 1; |
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136 nfev = 0; |
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137 |
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138 x = x0(:); |
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139 info = 0; |
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140 |
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141 ## Initial evaluation. |
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142 fval = fcn (reshape (x, xsiz)); |
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143 n = length (x); |
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144 |
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145 if (! isempty (outfcn)) |
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146 optimvalues.iter = niter; |
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147 optimvalues.funccount = nfev; |
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148 optimvalues.fval = fval; |
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149 optimvalues.searchdirection = zeros (n, 1); |
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150 state = 'init'; |
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151 stop = outfcn (x, optimvalues, state); |
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152 if (stop) |
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153 info = -1; |
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154 break; |
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155 endif |
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156 endif |
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157 |
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158 nsuciter = 0; |
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159 lastratio = 0; |
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160 |
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161 grad = []; |
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162 |
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163 ## Outer loop. |
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164 while (niter < maxiter && nfev < maxfev && ! info) |
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165 |
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166 grad0 = grad; |
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167 |
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168 ## Calculate function value and gradient (possibly via FD). |
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169 if (has_grad) |
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170 [fval, grad] = fcn (reshape (x, xsiz)); |
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171 grad = grad(:); |
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172 nfev ++; |
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173 else |
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174 grad = __fdjac__ (fcn, reshape (x, xsiz), fval, typicalx, cdif)(:); |
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175 nfev += (1 + cdif) * length (x); |
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176 endif |
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177 |
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178 if (niter == 1) |
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179 ## Initialize by identity matrix. |
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180 hesr = eye (n); |
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181 else |
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182 ## Use the damped BFGS formula. |
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183 y = grad - grad0; |
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184 sBs = sumsq (w); |
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185 Bs = hesr'*w; |
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186 sy = y'*s; |
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187 theta = 0.8 / max (1 - sy / sBs, 0.8); |
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188 r = theta * y + (1-theta) * Bs; |
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189 hesr = cholupdate (hesr, r / sqrt (s'*r), "+"); |
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190 [hesr, info] = cholupdate (hesr, Bs / sqrt (sBs), "-"); |
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191 if (info) |
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192 hesr = eye (n); |
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193 endif |
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194 endif |
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195 |
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196 if (autoscale) |
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197 ## Second derivatives approximate the hessian. |
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198 d2f = norm (hesr, 'columns').'; |
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199 if (niter == 1) |
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200 dg = d2f; |
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201 else |
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202 ## FIXME: maybe fixed lower and upper bounds? |
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203 dg = max (0.1*dg, d2f); |
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204 endif |
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205 endif |
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206 |
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207 if (niter == 1) |
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208 xn = norm (dg .* x); |
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209 ## FIXME: something better? |
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210 delta = factor * max (xn, 1); |
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211 endif |
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212 |
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213 ## FIXME -- why tolf*n*xn? If abs (e) ~ abs(x) * eps is a vector |
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214 ## of perturbations of x, then norm (hesr*e) <= eps*xn, i.e. by |
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215 ## tolf ~ eps we demand as much accuracy as we can expect. |
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216 if (norm (grad) <= tolf*n*xn) |
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217 info = 1; |
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218 break; |
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219 endif |
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220 |
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221 suc = false; |
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222 decfac = 0.5; |
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223 |
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224 ## Inner loop. |
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225 while (! suc && niter <= maxiter && nfev < maxfev && ! info) |
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226 |
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227 s = - __doglegm__ (hesr, grad, dg, delta); |
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228 |
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229 sn = norm (dg .* s); |
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230 if (niter == 1) |
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231 delta = min (delta, sn); |
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232 endif |
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233 |
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234 fval1 = fcn (reshape (x + s, xsiz)) (:); |
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235 nfev ++; |
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236 |
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237 if (fval1 < fval) |
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238 ## Scaled actual reduction. |
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239 actred = (fval - fval1) / (abs (fval1) + abs (fval)); |
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240 else |
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241 actred = -1; |
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242 endif |
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243 |
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244 w = hesr*s; |
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245 ## Scaled predicted reduction, and ratio. |
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246 t = 1/2 * sumsq (w) + grad'*s; |
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247 if (t < 0) |
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248 prered = -t/(abs (fval) + abs (fval + t)); |
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249 ratio = actred / prered; |
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250 else |
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251 prered = 0; |
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252 ratio = 0; |
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253 endif |
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254 |
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255 ## Update delta. |
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256 if (ratio < min(max(0.1, 0.8*lastratio), 0.9)) |
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257 delta *= decfac; |
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258 decfac ^= 1.4142; |
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259 if (delta <= 1e1*macheps*xn) |
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260 ## Trust region became uselessly small. |
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261 info = -3; |
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262 break; |
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263 endif |
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264 else |
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265 lastratio = ratio; |
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266 decfac = 0.5; |
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267 if (abs (1-ratio) <= 0.1) |
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268 delta = 1.4142*sn; |
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269 elseif (ratio >= 0.5) |
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270 delta = max (delta, 1.4142*sn); |
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271 endif |
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272 endif |
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273 |
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274 if (ratio >= 1e-4) |
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275 ## Successful iteration. |
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276 x += s; |
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277 xn = norm (dg .* x); |
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278 fval = fval1; |
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279 nsuciter ++; |
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280 suc = true; |
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281 endif |
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282 |
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283 niter ++; |
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284 |
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285 ## FIXME: should outputfcn be only called after a successful iteration? |
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286 if (! isempty (outfcn)) |
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287 optimvalues.iter = niter; |
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288 optimvalues.funccount = nfev; |
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289 optimvalues.fval = fval; |
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290 optimvalues.searchdirection = s; |
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291 state = 'iter'; |
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292 stop = outfcn (x, optimvalues, state); |
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293 if (stop) |
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294 info = -1; |
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295 break; |
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296 endif |
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297 endif |
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298 |
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299 ## Tests for termination conditions. A mysterious place, anything |
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300 ## can happen if you change something here... |
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301 |
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302 ## The rule of thumb (which I'm not sure M*b is quite following) |
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303 ## is that for a tolerance that depends on scaling, only 0 makes |
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304 ## sense as a default value. But 0 usually means uselessly long |
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305 ## iterations, so we need scaling-independent tolerances wherever |
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306 ## possible. |
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307 |
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308 ## The following tests done only after successful step. |
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309 if (ratio >= 1e-4) |
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310 ## This one is classic. Note that we use scaled variables again, |
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311 ## but compare to scaled step, so nothing bad. |
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312 if (sn <= tolx*xn) |
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313 info = 2; |
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314 ## Again a classic one. |
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315 elseif (actred < tolf) |
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316 info = 3; |
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317 endif |
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318 endif |
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319 |
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320 endwhile |
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321 endwhile |
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322 |
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323 ## Restore original shapes. |
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324 x = reshape (x, xsiz); |
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325 |
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326 output.iterations = niter; |
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327 output.successful = nsuciter; |
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328 output.funcCount = nfev; |
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329 |
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330 if (nargout > 5) |
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331 hess = hesr'*hesr; |
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332 endif |
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333 |
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334 endfunction |
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335 |
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336 ## An assistant function that evaluates a function handle and checks for |
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337 ## bad results. |
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338 function [fx, gx] = guarded_eval (fun, x) |
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339 if (nargout > 1) |
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340 [fx, gx] = fun (x); |
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341 else |
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342 fx = fun (x); |
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343 gx = []; |
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344 endif |
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345 |
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346 if (! (isreal (fx) && isreal (jx))) |
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347 error ("fminunc:notreal", "fminunc: non-real value encountered"); |
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348 elseif (complexeqn && ! (isnumeric (fx) && isnumeric(jx))) |
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349 error ("fminunc:notnum", "fminunc: non-numeric value encountered"); |
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350 elseif (any (isnan (fx(:)))) |
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351 error ("fminunc:isnan", "fminunc: NaN value encountered"); |
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352 endif |
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353 endfunction |
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354 |
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355 %!function f = rosenb (x) |
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356 %! n = length (x); |
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357 %! f = sumsq (1 - x(1:n-1)) + 100 * sumsq (x(2:n) - x(1:n-1).^2); |
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358 %!test |
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359 %! [x, fval, info, out] = fminunc (@rosenb, [5, -5]); |
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360 %! tol = 2e-5; |
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361 %! assert (info > 0); |
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362 %! assert (x, ones (1, 2), tol); |
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363 %! assert (fval, 0, tol); |
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364 %!test |
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365 %! [x, fval, info, out] = fminunc (@rosenb, zeros (1, 4)); |
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366 %! tol = 2e-5; |
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367 %! assert (info > 0); |
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368 %! assert (x, ones (1, 4), tol); |
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369 %! assert (fval, 0, tol); |
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370 |
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371 ## Solve the double dogleg trust-region minimization problem: |
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372 ## Minimize 1/2*norm(r*x)^2 subject to the constraint norm(d.*x) <= delta, |
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373 ## x being a convex combination of the gauss-newton and scaled gradient. |
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374 |
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375 ## TODO: error checks |
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376 ## TODO: handle singularity, or leave it up to mldivide? |
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377 |
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378 function x = __doglegm__ (r, g, d, delta) |
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379 ## Get Gauss-Newton direction. |
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380 b = r' \ g; |
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381 x = r \ b; |
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382 xn = norm (d .* x); |
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383 if (xn > delta) |
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384 ## GN is too big, get scaled gradient. |
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385 s = g ./ d; |
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386 sn = norm (s); |
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387 if (sn > 0) |
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388 ## Normalize and rescale. |
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389 s = (s / sn) ./ d; |
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390 ## Get the line minimizer in s direction. |
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391 tn = norm (r*s); |
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392 snm = (sn / tn) / tn; |
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393 if (snm < delta) |
10549 | 394 ## Get the dogleg path minimizer. |
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395 bn = norm (b); |
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396 dxn = delta/xn; snmd = snm/delta; |
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397 t = (bn/sn) * (bn/xn) * snmd; |
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398 t -= dxn * snmd^2 - sqrt ((t-dxn)^2 + (1-dxn^2)*(1-snmd^2)); |
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399 alpha = dxn*(1-snmd^2) / t; |
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400 else |
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401 alpha = 0; |
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402 endif |
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403 else |
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404 alpha = delta / xn; |
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405 snm = 0; |
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406 endif |
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407 ## Form the appropriate convex combination. |
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408 x = alpha * x + ((1-alpha) * min (snm, delta)) * s; |
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409 endif |
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410 endfunction |