Mercurial > hg > octave-lyh
annotate scripts/optimization/fsolve.m @ 17392:8c5878260636
doc: Fix typo in fieldnames docstring.
* scripts/general/fieldnames.m: Eliminate stray parenthesis in docstring.
| author | Rik <rik@octave.org> |
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| date | Fri, 06 Sep 2013 08:35:59 -0700 |
| parents | 1c89599167a6 |
| children |
| rev | line source |
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1 ## Copyright (C) 2008-2012 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 |
| 8466 | 21 ## -*- texinfo -*- |
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22 ## @deftypefn {Function File} {} fsolve (@var{fcn}, @var{x0}, @var{options}) |
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23 ## @deftypefnx {Function File} {[@var{x}, @var{fvec}, @var{info}, @var{output}, @var{fjac}] =} fsolve (@var{fcn}, @dots{}) |
| 8513 | 24 ## Solve a system of nonlinear equations defined by the function @var{fcn}. |
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25 ## @var{fcn} should accept a vector (array) defining the unknown variables, |
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26 ## and return a vector of left-hand sides of the equations. Right-hand sides |
| 8466 | 27 ## are defined to be zeros. |
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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})} gives (approximately) all zeros. |
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30 ## @var{x0} determines a starting guess. The shape of @var{x0} is preserved |
| 8466 | 31 ## in all calls to @var{fcn}, but otherwise it 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{fsolve} recognizes these options: |
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34 ## @qcode{"FunValCheck"}, @qcode{"OutputFcn"}, @qcode{"TolX"}, |
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35 ## @qcode{"TolFun"}, @qcode{"MaxIter"}, @qcode{"MaxFunEvals"}, |
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36 ## @qcode{"Jacobian"}, @qcode{"Updating"}, @qcode{"ComplexEqn"} |
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37 ## @qcode{"TypicalX"}, @qcode{"AutoScaling"} and @qcode{"FinDiffType"}. |
| 8466 | 38 ## |
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39 ## If @qcode{"Jacobian"} is @qcode{"on"}, it specifies that @var{fcn}, |
| 8513 | 40 ## called with 2 output arguments, also returns the Jacobian matrix |
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41 ## of right-hand sides at the requested point. @qcode{"TolX"} specifies |
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42 ## the termination tolerance in the unknown variables, while |
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43 ## @qcode{"TolFun"} is a tolerance for equations. Default is @code{1e-7} |
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44 ## for both @qcode{"TolX"} and @qcode{"TolFun"}. |
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45 ## |
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46 ## If @qcode{"AutoScaling"} is on, the variables will be automatically scaled |
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47 ## according to the column norms of the (estimated) Jacobian. As a result, |
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48 ## TolF becomes scaling-independent. By default, this option is off, because |
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49 ## it may sometimes deliver unexpected (though mathematically correct) results. |
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50 ## |
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51 ## If @qcode{"Updating"} is @qcode{"on"}, the function will attempt to use |
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52 ## @nospell{Broyden} updates to update the Jacobian, in order to reduce the |
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53 ## amount of Jacobian calculations. If your user function always calculates the |
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54 ## Jacobian (regardless of number of output arguments), this option provides |
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55 ## no advantage and should be set to false. |
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56 ## |
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57 ## @qcode{"ComplexEqn"} is @qcode{"on"}, @code{fsolve} will attempt to solve |
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58 ## complex equations in complex variables, assuming that the equations possess a |
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59 ## complex derivative (i.e., are holomorphic). If this is not what you want, |
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60 ## should unpack the real and imaginary parts of the system to get a real |
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61 ## system. |
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62 ## |
| 8466 | 63 ## For description of the other options, see @code{optimset}. |
| 64 ## | |
| 65 ## On return, @var{fval} contains the value of the function @var{fcn} | |
| 66 ## evaluated at @var{x}, and @var{info} may be one of the following values: | |
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67 ## |
| 8466 | 68 ## @table @asis |
| 69 ## @item 1 | |
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70 ## Converged to a solution point. Relative residual error is less than |
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71 ## specified by TolFun. |
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72 ## |
| 8466 | 73 ## @item 2 |
| 74 ## Last relative step size was less that TolX. | |
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75 ## |
| 8466 | 76 ## @item 3 |
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77 ## Last relative decrease in residual was less than TolF. |
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78 ## |
| 8466 | 79 ## @item 0 |
| 80 ## Iteration limit exceeded. | |
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81 ## |
| 8466 | 82 ## @item -3 |
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83 ## The trust region radius became excessively small. |
| 8466 | 84 ## @end table |
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85 ## |
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86 ## Note: If you only have a single nonlinear equation of one variable, using |
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88 ## |
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89 ## Note about user-supplied Jacobians: |
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90 ## As an inherent property of the algorithm, Jacobian is always requested for a |
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91 ## solution vector whose residual vector is already known, and it is the last |
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92 ## accepted successful step. Often this will be one of the last two calls, but |
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93 ## not always. If the savings by reusing intermediate results from residual |
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94 ## calculation in Jacobian calculation are significant, the best strategy is to |
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95 ## employ OutputFcn: After a vector is evaluated for residuals, if OutputFcn is |
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96 ## called with that vector, then the intermediate results should be saved for |
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97 ## future Jacobian evaluation, and should be kept until a Jacobian evaluation |
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98 ## is requested or until OutputFcn is called with a different vector, in which |
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99 ## case they should be dropped in favor of this most recent vector. A short |
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100 ## example how this can be achieved follows: |
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101 ## |
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102 ## @example |
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103 ## function [fvec, fjac] = user_func (x, optimvalues, state) |
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104 ## persistent sav = [], sav0 = []; |
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105 ## if (nargin == 1) |
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106 ## ## evaluation call |
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107 ## if (nargout == 1) |
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108 ## sav0.x = x; # mark saved vector |
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109 ## ## calculate fvec, save results to sav0. |
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110 ## elseif (nargout == 2) |
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111 ## ## calculate fjac using sav. |
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112 ## endif |
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113 ## else |
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114 ## ## outputfcn call. |
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115 ## if (all (x == sav0.x)) |
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116 ## sav = sav0; |
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117 ## endif |
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118 ## ## maybe output iteration status, etc. |
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119 ## endif |
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120 ## endfunction |
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121 ## |
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122 ## ## @dots{} |
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123 ## |
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124 ## fsolve (@@user_func, x0, optimset ("OutputFcn", @@user_func, @dots{})) |
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125 ## @end example |
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126 ## @seealso{fzero, optimset} |
| 8466 | 127 ## @end deftypefn |
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128 |
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129 ## PKG_ADD: ## Discard result to avoid polluting workspace with ans at startup. |
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130 ## PKG_ADD: [~] = __all_opts__ ("fsolve"); |
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131 |
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132 function [x, fvec, info, output, fjac] = fsolve (fcn, x0, options = struct ()) |
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133 |
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134 ## Get default options if requested. |
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135 if (nargin == 1 && ischar (fcn) && strcmp (fcn, 'defaults')) |
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136 x = optimset ("MaxIter", 400, "MaxFunEvals", Inf, ... |
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137 "Jacobian", "off", "TolX", 1e-7, "TolFun", 1e-7, |
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138 "OutputFcn", [], "Updating", "on", "FunValCheck", "off", |
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139 "ComplexEqn", "off", "FinDiffType", "central", |
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140 "TypicalX", [], "AutoScaling", "off"); |
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141 return; |
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142 endif |
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143 |
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144 if (nargin < 2 || nargin > 3 || ! ismatrix (x0)) |
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145 print_usage (); |
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146 endif |
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147 |
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148 if (ischar (fcn)) |
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149 fcn = str2func (fcn, "global"); |
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150 elseif (iscell (fcn)) |
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151 fcn = @(x) make_fcn_jac (x, fcn{1}, fcn{2}); |
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152 endif |
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153 |
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154 xsiz = size (x0); |
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155 n = numel (x0); |
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156 |
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157 has_jac = strcmpi (optimget (options, "Jacobian", "off"), "on"); |
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158 cdif = strcmpi (optimget (options, "FinDiffType", "central"), "central"); |
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159 maxiter = optimget (options, "MaxIter", 400); |
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160 maxfev = optimget (options, "MaxFunEvals", Inf); |
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161 outfcn = optimget (options, "OutputFcn"); |
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162 updating = strcmpi (optimget (options, "Updating", "on"), "on"); |
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163 complexeqn = strcmpi (optimget (options, "ComplexEqn", "off"), "on"); |
| 8590 | 164 |
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165 ## Get scaling matrix using the TypicalX option. If set to "auto", the |
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166 ## scaling matrix is estimated using the Jacobian. |
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167 typicalx = optimget (options, "TypicalX"); |
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168 if (isempty (typicalx)) |
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169 typicalx = ones (n, 1); |
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170 endif |
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171 autoscale = strcmpi (optimget (options, "AutoScaling", "off"), "on"); |
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172 if (! autoscale) |
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173 dg = 1 ./ typicalx; |
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174 endif |
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175 |
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176 funvalchk = strcmpi (optimget (options, "FunValCheck", "off"), "on"); |
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177 |
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178 if (funvalchk) |
| 8604 | 179 ## Replace fcn with a guarded version. |
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180 fcn = @(x) guarded_eval (fcn, x, complexeqn); |
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181 endif |
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182 |
| 8466 | 183 ## These defaults are rather stringent. I think that normally, user |
| 184 ## prefers accuracy to performance. | |
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185 |
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186 macheps = eps (class (x0)); |
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187 |
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188 tolx = optimget (options, "TolX", 1e-7); |
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189 tolf = optimget (options, "TolFun", 1e-7); |
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190 |
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191 factor = 1; |
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192 |
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193 niter = 1; |
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194 nfev = 1; |
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195 |
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196 x = x0(:); |
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197 info = 0; |
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198 |
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199 ## Initial evaluation. |
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200 ## Handle arbitrary shapes of x and f and remember them. |
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201 fvec = fcn (reshape (x, xsiz)); |
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202 fsiz = size (fvec); |
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203 fvec = fvec(:); |
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204 fn = norm (fvec); |
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205 m = length (fvec); |
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206 n = length (x); |
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207 |
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208 if (! isempty (outfcn)) |
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209 optimvalues.iter = niter; |
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210 optimvalues.funccount = nfev; |
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211 optimvalues.fval = fn; |
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212 optimvalues.searchdirection = zeros (n, 1); |
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213 state = 'init'; |
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214 stop = outfcn (x, optimvalues, state); |
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215 if (stop) |
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216 info = -1; |
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217 break; |
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218 endif |
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219 endif |
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220 |
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221 nsuciter = 0; |
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222 |
| 8507 | 223 ## Outer loop. |
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224 while (niter < maxiter && nfev < maxfev && ! info) |
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225 |
| 8507 | 226 ## Calculate function value and Jacobian (possibly via FD). |
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227 if (has_jac) |
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228 [fvec, fjac] = fcn (reshape (x, xsiz)); |
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229 ## If the Jacobian is sparse, disable Broyden updating. |
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230 if (issparse (fjac)) |
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231 updating = false; |
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232 endif |
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233 fvec = fvec(:); |
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234 nfev ++; |
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235 else |
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236 fjac = __fdjac__ (fcn, reshape (x, xsiz), fvec, typicalx, cdif); |
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237 nfev += (1 + cdif) * length (x); |
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238 endif |
| 8590 | 239 |
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240 ## For square and overdetermined systems, we update a QR |
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241 ## factorization of the Jacobian to avoid solving a full system in each |
| 8590 | 242 ## step. In this case, we pass a triangular matrix to __dogleg__. |
| 243 useqr = updating && m >= n && n > 10; | |
| 244 | |
| 245 if (useqr) | |
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246 ## FIXME: Currently, pivoting is mostly useless because the \ operator |
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247 ## cannot exploit the resulting props of the triangular factor. |
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248 ## Unpivoted QR is significantly faster so it doesn't seem right to pivot |
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249 ## just to get invariance. Original MINPACK didn't pivot either, at least |
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250 ## when qr updating was used. |
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251 [q, r] = qr (fjac, 0); |
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252 endif |
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253 |
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254 if (autoscale) |
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255 ## Get column norms, use them as scaling factors. |
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256 jcn = norm (fjac, 'columns').'; |
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257 if (niter == 1) |
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258 dg = jcn; |
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259 dg(dg == 0) = 1; |
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260 else |
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261 ## Rescale adaptively. |
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262 ## FIXME: the original minpack used the following rescaling strategy: |
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263 ## dg = max (dg, jcn); |
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264 ## but it seems not good if we start with a bad guess yielding Jacobian |
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265 ## columns with large norms that later decrease, because the corresponding |
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266 ## variable will still be overscaled. So instead, we only give the old |
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267 ## scaling a small momentum, but do not honor it. |
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268 |
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269 dg = max (0.1*dg, jcn); |
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270 endif |
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271 endif |
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272 |
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273 if (niter == 1) |
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274 xn = norm (dg .* x); |
| 8590 | 275 ## FIXME: something better? |
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276 delta = factor * max (xn, 1); |
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277 endif |
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278 |
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279 ## It also seems that in the case of fast (and inhomogeneously) changing |
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280 ## Jacobian, the Broyden updates are of little use, so maybe we could |
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281 ## skip them if a big disproportional change is expected. The question is, |
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282 ## of course, how to define the above terms :) |
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283 |
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284 lastratio = 0; |
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285 nfail = 0; |
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286 nsuc = 0; |
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287 decfac = 0.5; |
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288 |
| 8507 | 289 ## Inner loop. |
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290 while (niter <= maxiter && nfev < maxfev && ! info) |
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291 |
| 8590 | 292 ## Get trust-region model (dogleg) minimizer. |
| 293 if (useqr) | |
| 294 qtf = q'*fvec; | |
| 295 s = - __dogleg__ (r, qtf, dg, delta); | |
| 296 w = qtf + r * s; | |
| 297 else | |
| 298 s = - __dogleg__ (fjac, fvec, dg, delta); | |
| 299 w = fvec + fjac * s; | |
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300 endif |
| 8590 | 301 |
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302 sn = norm (dg .* s); |
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303 if (niter == 1) |
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304 delta = min (delta, sn); |
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305 endif |
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306 |
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307 fvec1 = fcn (reshape (x + s, xsiz)) (:); |
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308 fn1 = norm (fvec1); |
| 8590 | 309 nfev ++; |
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310 |
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311 if (fn1 < fn) |
| 8507 | 312 ## Scaled actual reduction. |
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313 actred = 1 - (fn1/fn)^2; |
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314 else |
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315 actred = -1; |
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316 endif |
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317 |
| 8507 | 318 ## Scaled predicted reduction, and ratio. |
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319 t = norm (w); |
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320 if (t < fn) |
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321 prered = 1 - (t/fn)^2; |
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322 ratio = actred / prered; |
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323 else |
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324 prered = 0; |
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325 ratio = 0; |
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326 endif |
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327 |
| 8507 | 328 ## Update delta. |
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329 if (ratio < min (max (0.1, 0.8*lastratio), 0.9)) |
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330 nsuc = 0; |
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331 nfail ++; |
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332 delta *= decfac; |
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333 decfac ^= 1.4142; |
| 8590 | 334 if (delta <= 1e1*macheps*xn) |
| 8507 | 335 ## Trust region became uselessly small. |
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336 info = -3; |
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337 break; |
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338 endif |
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339 else |
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340 lastratio = ratio; |
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341 decfac = 0.5; |
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342 nfail = 0; |
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343 nsuc ++; |
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344 if (abs (1-ratio) <= 0.1) |
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345 delta = 1.4142*sn; |
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346 elseif (ratio >= 0.5 || nsuc > 1) |
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347 delta = max (delta, 1.4142*sn); |
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348 endif |
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349 endif |
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350 |
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351 if (ratio >= 1e-4) |
| 8507 | 352 ## Successful iteration. |
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353 x += s; |
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354 xn = norm (dg .* x); |
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355 fvec = fvec1; |
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356 fn = fn1; |
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357 nsuciter ++; |
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358 endif |
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359 |
| 8857 | 360 niter ++; |
| 361 | |
| 8590 | 362 ## FIXME: should outputfcn be only called after a successful iteration? |
| 8615 | 363 if (! isempty (outfcn)) |
| 8590 | 364 optimvalues.iter = niter; |
| 365 optimvalues.funccount = nfev; | |
| 366 optimvalues.fval = fn; | |
| 367 optimvalues.searchdirection = s; | |
| 368 state = 'iter'; | |
| 369 stop = outfcn (x, optimvalues, state); | |
| 370 if (stop) | |
| 371 info = -1; | |
| 372 break; | |
| 373 endif | |
| 374 endif | |
| 375 | |
| 8466 | 376 ## Tests for termination conditions. A mysterious place, anything |
| 377 ## can happen if you change something here... | |
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378 |
| 8466 | 379 ## The rule of thumb (which I'm not sure M*b is quite following) |
| 380 ## is that for a tolerance that depends on scaling, only 0 makes | |
| 381 ## sense as a default value. But 0 usually means uselessly long | |
| 382 ## iterations, so we need scaling-independent tolerances wherever | |
| 383 ## possible. | |
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384 |
| 8466 | 385 ## FIXME -- why tolf*n*xn? If abs (e) ~ abs(x) * eps is a vector |
| 386 ## of perturbations of x, then norm (fjac*e) <= eps*n*xn, i.e. by | |
| 387 ## tolf ~ eps we demand as much accuracy as we can expect. | |
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388 if (fn <= tolf*n*xn) |
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389 info = 1; |
| 10549 | 390 ## The following tests done only after successful step. |
| 9075 | 391 elseif (ratio >= 1e-4) |
| 8466 | 392 ## This one is classic. Note that we use scaled variables again, |
| 10549 | 393 ## but compare to scaled step, so nothing bad. |
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394 if (sn <= tolx*xn) |
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395 info = 2; |
| 8466 | 396 ## Again a classic one. It seems weird to use the same tolf |
| 10549 | 397 ## for two different tests, but that's what M*b manual appears |
| 398 ## to say. | |
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399 elseif (actred < tolf) |
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400 info = 3; |
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401 endif |
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402 endif |
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403 |
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404 ## Criterion for recalculating Jacobian. |
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405 if (! updating || nfail == 2 || nsuciter < 2) |
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406 break; |
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407 endif |
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408 |
| 8507 | 409 ## Compute the scaled Broyden update. |
| 8590 | 410 if (useqr) |
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411 u = (fvec1 - q*w) / sn; |
| 8590 | 412 v = dg .* ((dg .* s) / sn); |
| 413 | |
| 414 ## Update the QR factorization. | |
| 415 [q, r] = qrupdate (q, r, u, v); | |
| 416 else | |
| 417 u = (fvec1 - w); | |
| 418 v = dg .* ((dg .* s) / sn); | |
| 419 | |
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420 ## update the Jacobian |
| 8590 | 421 fjac += u * v'; |
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422 endif |
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423 endwhile |
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424 endwhile |
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425 |
| 8507 | 426 ## Restore original shapes. |
|
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427 x = reshape (x, xsiz); |
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428 fvec = reshape (fvec, fsiz); |
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429 |
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430 output.iterations = niter; |
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431 output.successful = nsuciter; |
| 8590 | 432 output.funcCount = nfev; |
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433 |
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434 endfunction |
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435 |
| 8466 | 436 ## An assistant function that evaluates a function handle and checks for |
| 437 ## bad results. | |
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438 function [fx, jx] = guarded_eval (fun, x, complexeqn) |
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439 if (nargout > 1) |
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440 [fx, jx] = fun (x); |
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441 else |
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442 fx = fun (x); |
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443 jx = []; |
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444 endif |
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445 |
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446 if (! complexeqn && ! (isreal (fx) && isreal (jx))) |
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447 error ("fsolve:notreal", "fsolve: non-real value encountered"); |
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448 elseif (complexeqn && ! (isnumeric (fx) && isnumeric (jx))) |
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449 error ("fsolve:notnum", "fsolve: non-numeric value encountered"); |
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450 elseif (any (isnan (fx(:)))) |
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451 error ("fsolve:isnan", "fsolve: NaN value encountered"); |
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452 elseif (any (isinf (fx(:)))) |
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453 error ("fsolve:isinf", "fsolve: Inf value encountered"); |
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454 endif |
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455 endfunction |
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456 |
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457 function [fx, jx] = make_fcn_jac (x, fcn, fjac) |
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458 fx = fcn (x); |
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459 if (nargout == 2) |
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460 jx = fjac (x); |
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461 endif |
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462 endfunction |
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463 |
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464 |
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465 %!function retval = __f (p) |
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466 %! x = p(1); |
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467 %! y = p(2); |
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468 %! z = p(3); |
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469 %! retval = zeros (3, 1); |
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470 %! retval(1) = sin (x) + y^2 + log (z) - 7; |
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471 %! retval(2) = 3*x + 2^y -z^3 + 1; |
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472 %! retval(3) = x + y + z - 5; |
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473 %!endfunction |
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474 %!test |
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475 %! x_opt = [ 0.599054; |
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476 %! 2.395931; |
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477 %! 2.005014 ]; |
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478 %! tol = 1.0e-5; |
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479 %! [x, fval, info] = fsolve (@__f, [ 0.5; 2.0; 2.5 ]); |
|
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480 %! assert (info > 0); |
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481 %! assert (norm (x - x_opt, Inf) < tol); |
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482 %! assert (norm (fval) < tol); |
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483 |
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484 %!function retval = __f (p) |
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485 %! x = p(1); |
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486 %! y = p(2); |
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487 %! z = p(3); |
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488 %! w = p(4); |
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489 %! retval = zeros (4, 1); |
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490 %! retval(1) = 3*x + 4*y + exp (z + w) - 1.007; |
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491 %! retval(2) = 6*x - 4*y + exp (3*z + w) - 11; |
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492 %! retval(3) = x^4 - 4*y^2 + 6*z - 8*w - 20; |
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493 %! retval(4) = x^2 + 2*y^3 + z - w - 4; |
|
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494 %!endfunction |
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495 %!test |
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496 %! x_opt = [ -0.767297326653401, 0.590671081117440, ... |
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497 %! 1.47190018629642, -1.52719341133957 ]; |
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498 %! tol = 1.0e-5; |
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499 %! [x, fval, info] = fsolve (@__f, [-1, 1, 2, -1]); |
|
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500 %! assert (info > 0); |
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501 %! assert (norm (x - x_opt, Inf) < tol); |
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502 %! assert (norm (fval) < tol); |
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503 |
|
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504 %!function retval = __f (p) |
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505 %! x = p(1); |
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506 %! y = p(2); |
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507 %! z = p(3); |
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508 %! retval = zeros (3, 1); |
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509 %! retval(1) = sin (x) + y^2 + log (z) - 7; |
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510 %! retval(2) = 3*x + 2^y -z^3 + 1; |
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511 %! retval(3) = x + y + z - 5; |
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512 %! retval(4) = x*x + y - z*log (z) - 1.36; |
|
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513 %!endfunction |
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514 %!test |
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515 %! x_opt = [ 0.599054; |
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516 %! 2.395931; |
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517 %! 2.005014 ]; |
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518 %! tol = 1.0e-5; |
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519 %! [x, fval, info] = fsolve (@__f, [ 0.5; 2.0; 2.5 ]); |
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520 %! assert (info > 0); |
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521 %! assert (norm (x - x_opt, Inf) < tol); |
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522 %! assert (norm (fval) < tol); |
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523 |
|
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524 %!function retval = __f (p) |
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525 %! x = p(1); |
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526 %! y = p(2); |
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527 %! z = p(3); |
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528 %! retval = zeros (3, 1); |
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529 %! retval(1) = sin (x) + y^2 + log (z) - 7; |
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530 %! retval(2) = 3*x + 2^y -z^3 + 1; |
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531 %! retval(3) = x + y + z - 5; |
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532 %!endfunction |
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533 %!test |
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534 %! x_opt = [ 0.599054; |
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535 %! 2.395931; |
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536 %! 2.005014 ]; |
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537 %! tol = 1.0e-5; |
| 8590 | 538 %! opt = optimset ("Updating", "qrp"); |
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539 %! [x, fval, info] = fsolve (@__f, [ 0.5; 2.0; 2.5 ], opt); |
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540 %! assert (info > 0); |
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541 %! assert (norm (x - x_opt, Inf) < tol); |
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542 %! assert (norm (fval) < tol); |
| 8590 | 543 |
| 544 %!test | |
| 545 %! b0 = 3; | |
| 546 %! a0 = 0.2; | |
| 547 %! x = 0:.5:5; | |
| 548 %! noise = 1e-5 * sin (100*x); | |
| 549 %! y = exp (-a0*x) + b0 + noise; | |
| 550 %! c_opt = [a0, b0]; | |
| 551 %! tol = 1e-5; | |
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552 %! |
| 8590 | 553 %! [c, fval, info, output] = fsolve (@(c) (exp(-c(1)*x) + c(2) - y), [0, 0]); |
| 554 %! assert (info > 0); | |
| 555 %! assert (norm (c - c_opt, Inf) < tol); | |
| 556 %! assert (norm (fval) < norm (noise)); | |
| 557 | |
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558 %!function y = cfun (x) |
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559 %! y(1) = (1+i)*x(1)^2 - (1-i)*x(2) - 2; |
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560 %! y(2) = sqrt (x(1)*x(2)) - (1-2i)*x(3) + (3-4i); |
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561 %! y(3) = x(1) * x(2) - x(3)^2 + (3+2i); |
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562 %!endfunction |
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563 |
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564 %!test |
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565 %! x_opt = [-1+i, 1-i, 2+i]; |
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566 %! x = [i, 1, 1+i]; |
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567 %! |
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568 %! [x, f, info] = fsolve (@cfun, x, optimset ("ComplexEqn", "on")); |
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569 %! tol = 1e-5; |
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570 %! assert (norm (f) < tol); |
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571 %! assert (norm (x - x_opt, Inf) < tol); |
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572 |
|
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573 ## Solve the double dogleg trust-region least-squares problem: |
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574 ## Minimize norm(r*x-b) subject to the constraint norm(d.*x) <= delta, |
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575 ## x being a convex combination of the gauss-newton and scaled gradient. |
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576 |
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577 ## TODO: error checks |
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578 ## TODO: handle singularity, or leave it up to mldivide? |
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579 |
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580 function x = __dogleg__ (r, b, d, delta) |
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581 ## Get Gauss-Newton direction. |
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582 x = r \ b; |
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583 xn = norm (d .* x); |
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584 if (xn > delta) |
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585 ## GN is too big, get scaled gradient. |
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586 s = (r' * b) ./ d; |
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587 sn = norm (s); |
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588 if (sn > 0) |
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589 ## Normalize and rescale. |
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590 s = (s / sn) ./ d; |
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591 ## Get the line minimizer in s direction. |
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592 tn = norm (r*s); |
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593 snm = (sn / tn) / tn; |
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594 if (snm < delta) |
| 10549 | 595 ## Get the dogleg path minimizer. |
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596 bn = norm (b); |
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597 dxn = delta/xn; snmd = snm/delta; |
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598 t = (bn/sn) * (bn/xn) * snmd; |
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599 t -= dxn * snmd^2 - sqrt ((t-dxn)^2 + (1-dxn^2)*(1-snmd^2)); |
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600 alpha = dxn*(1-snmd^2) / t; |
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601 else |
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602 alpha = 0; |
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603 endif |
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604 else |
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605 alpha = delta / xn; |
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606 snm = 0; |
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607 endif |
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608 ## Form the appropriate convex combination. |
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609 x = alpha * x + ((1-alpha) * min (snm, delta)) * s; |
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610 endif |
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611 endfunction |
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612 |