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Improve documentation strings.
author Rik <octave@nomad.inbox5.com>
date Tue, 08 Jun 2010 20:22:38 -0700
parents 95c3e38098bf
children be55736a0783
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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)
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394 ## Get the dogleg path minimizer.
9899
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395 bn = norm (b);
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396 dxn = delta/xn; snmd = snm/delta;
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diff changeset
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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parents: 9758
diff changeset
401 alpha = 0;
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John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
402 endif
9f25290a35e8 more private function and subfunction changes
John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
403 else
9f25290a35e8 more private function and subfunction changes
John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
404 alpha = delta / xn;
9f25290a35e8 more private function and subfunction changes
John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
405 snm = 0;
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John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
406 endif
9f25290a35e8 more private function and subfunction changes
John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
407 ## Form the appropriate convex combination.
9f25290a35e8 more private function and subfunction changes
John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
408 x = alpha * x + ((1-alpha) * min (snm, delta)) * s;
9f25290a35e8 more private function and subfunction changes
John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
409 endif
9f25290a35e8 more private function and subfunction changes
John W. Eaton <jwe@octave.org>
parents: 9758
diff changeset
410 endfunction