annotate scripts/optimization/fsolve.m @ 8648:ff61b53eb294

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author John W. Eaton <jwe@octave.org>
date Fri, 30 Jan 2009 15:42:51 -0500
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children 9c092b111b1d
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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} {} 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{})
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24 ## Solve a system of nonlinear equations defined by the function @var{fcn}.
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25 ## @var{fcn} should accepts 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
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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
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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 ## @code{"FunValCheck"}, @code{"OutputFcn"}, @code{"TolX"},
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35 ## @code{"TolFun"}, @code{"MaxIter"}, @code{"MaxFunEvals"},
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36 ## @code{"Jacobian"} and @code{"Updating"}.
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37 ##
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38 ## If @code{"Jacobian"} is @code{"on"}, it specifies that @var{fcn},
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39 ## called with 2 output arguments, also returns the Jacobian matrix
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40 ## of right-hand sides at the requested point. @code{"TolX"} specifies
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41 ## the termination tolerance in the unknown variables, while
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42 ## @code{"TolFun"} is a tolerance for equations. Default is @code{1e-7}
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43 ## for both @code{"TolX"} and @code{"TolFun"}.
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44 ## If @code{"Updating"} is "on", the function will attempt to use Broyden
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45 ## updates to update the Jacobian, in order to reduce the amount of jacobian
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46 ## calculations.
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47 ## If your user function always calculates the Jacobian (regardless of number
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48 ## of output arguments), this option provides no advantage and should be set to
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49 ## false.
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50 ##
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51 ## For description of the other options, see @code{optimset}.
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52 ##
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53 ## On return, @var{fval} contains the value of the function @var{fcn}
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54 ## evaluated at @var{x}, and @var{info} may be one of the following values:
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55 ##
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56 ## @table @asis
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57 ## @item 1
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58 ## Converged to a solution point. Relative residual error is less than specified
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59 ## by TolFun.
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60 ## @item 2
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61 ## Last relative step size was less that TolX.
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62 ## @item 3
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63 ## Last relative decrease in residual was less than TolF.
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64 ## @item 0
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65 ## Iteration limit exceeded.
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66 ## @item -3
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67 ## The trust region radius became excessively small.
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68 ## @end table
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69 ##
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70 ## Note: If you only have a single nonlinear equation of one variable, using
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71 ## @code{fzero} is usually a much better idea.
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72 ## @seealso{fzero, optimset}
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73 ## @end deftypefn
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74
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75 ## PKG_ADD: __all_opts__ ("fsolve");
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76
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77 function [x, fvec, info, output, fjac] = fsolve (fcn, x0, options = struct ())
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78
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79 ## Get default options if requested.
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80 if (nargin == 1 && ischar (fcn) && strcmp (fcn, 'defaults'))
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81 x = optimset ("MaxIter", 400, "MaxFunEvals", Inf, \
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82 "Jacobian", "off", "TolX", 1.5e-8, "TolFun", 1.5e-8,
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83 "OutputFcn", [], "Updating", "on", "FunValCheck", "off");
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84 return;
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85 endif
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86
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87 if (nargin < 2 || nargin > 3 || ! ismatrix (x0))
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88 print_usage ();
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89 endif
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90
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91 if (ischar (fcn))
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92 fcn = str2func (fcn);
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93 endif
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94
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95 xsiz = size (x0);
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96 n = numel (x0);
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97
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98 has_jac = strcmpi (optimget (options, "Jacobian", "off"), "on");
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99 maxiter = optimget (options, "MaxIter", 400);
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100 maxfev = optimget (options, "MaxFunEvals", Inf);
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101 outfcn = optimget (options, "OutputFcn");
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102 updating = strcmpi (optimget (options, "Updating", "on"), "on");
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103
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104 funvalchk = strcmpi (optimget (options, "FunValCheck", "off"), "on");
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105
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106 if (funvalchk)
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107 ## Replace fcn with a guarded version.
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108 fcn = @(x) guarded_eval (fcn, x);
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109 endif
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110
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111 ## These defaults are rather stringent. I think that normally, user
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112 ## prefers accuracy to performance.
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113
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114 macheps = eps (class (x0));
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115
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116 tolx = optimget (options, "TolX", sqrt (macheps));
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117 tolf = optimget (options, "TolFun", sqrt (macheps));
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118
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119 factor = 100;
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120 ## FIXME: TypicalX corresponds to user scaling (???)
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121 autodg = true;
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122
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123 niter = 1;
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124 nfev = 0;
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125
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126 x = x0(:);
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127 info = 0;
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128
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129 ## Outer loop.
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130 while (niter < maxiter && nfev < maxfev && ! info)
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131
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132 ## Calculate function value and Jacobian (possibly via FD).
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133 ## Handle arbitrary shapes of x and f and remember them.
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134 if (has_jac)
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135 [fvec, fjac] = fcn (reshape (x, xsiz));
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136 ## If the jacobian is sparse, disable Broyden updating.
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137 if (issparse (fjac))
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138 updating = false;
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139 endif
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140 nfev ++;
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141 else
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142 [fvec, fjac] = __fdjac__ (fcn, reshape (x, xsiz));
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143 nfev += 1 + length (x);
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144 endif
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145 fsiz = size (fvec);
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146 fvec = fvec(:);
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147 fn = norm (fvec);
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148 m = length (fvec);
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149 n = length (x);
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150
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151 ## For square and overdetermined systems, we update a (pivoted) QR
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152 ## factorization of the jacobian to avoid solving a full system in each
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153 ## step. In this case, we pass a triangular matrix to __dogleg__.
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154 ## Pivoted QR is used for slightly better robustness and invariance
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155 ## w.r.t. permutations of variables.
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156 useqr = updating && m >= n && n > 10;
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157
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158 if (useqr)
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159 ## FIXME: Currently, pivoting is mostly useless because the \ operator
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160 ## cannot exploit the resulting props of the triangular factor.
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161 ## Unpivoted QR is significantly faster so it doesn't seem right to pivot
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162 ## just to get invariance. Original MINPACK didn't pivot either, at least
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163 ## when qr updating was used.
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164 [q, r] = qr (fjac, 0);
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165 endif
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166
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167 ## Get column norms, use them as scaling factors.
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168 jcn = norm (fjac, 'columns').';
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169 if (niter == 1)
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170 if (autodg)
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171 dg = jcn;
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172 dg(dg == 0) = 1;
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173 endif
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174 xn = norm (dg .* x);
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175 ## FIXME: something better?
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176 delta = max (factor * xn, 1);
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177 endif
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178
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179 ## Rescale if necessary.
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180 if (autodg)
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181 ## FIXME: the original minpack used the following rescaling strategy:
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182 ## dg = max (dg, jcn);
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183 ## but it seems not good if we start with a bad guess yielding jacobian
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184 ## columns with large norms that later decrease, because the corresponding
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185 ## variable will still be overscaled. So instead, we only give the old
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186 ## scaling a small momentum, but do not honor it.
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187
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188 dg = max (0.1*dg, jcn);
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189
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190 ## It also seems that in the case of fast (and inhomogeneously) changing
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191 ## jacobian, the Broyden updates are of little use, so maybe we could
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192 ## skip them if a big disproportional change is expected. The question is,
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193 ## of course, how to define the above terms :)
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194 endif
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195
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196 nfail = 0;
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197 nsuc = 0;
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198 ## Inner loop.
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199 while (niter <= maxiter && nfev < maxfev && ! info)
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200
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201 if (useqr)
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202 tr_mat = r;
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203 tr_vec = q'*fvec;
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204 else
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205 tr_mat = fjac;
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206 tr_vec = fvec;
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207 endif
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208
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209 ## Get trust-region model (dogleg) minimizer.
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210 if (useqr)
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211 qtf = q'*fvec;
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212 s = - __dogleg__ (r, qtf, dg, delta);
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213 w = qtf + r * s;
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214 s = p * s;
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215 else
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216 s = - __dogleg__ (fjac, fvec, dg, delta);
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217 w = fvec + fjac * s;
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218 endif
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219
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220 sn = norm (dg .* s);
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221 if (niter == 1)
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222 delta = min (delta, sn);
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223 endif
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224
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225 fvec1 = fcn (reshape (x + s, xsiz)) (:);
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226 fn1 = norm (fvec1);
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227 nfev ++;
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228
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229 if (fn1 < fn)
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230 ## Scaled actual reduction.
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231 actred = 1 - (fn1/fn)^2;
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232 else
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233 actred = -1;
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234 endif
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235
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236 ## Scaled predicted reduction, and ratio.
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237 t = norm (w);
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238 if (t < fn)
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239 prered = 1 - (t/fn)^2;
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240 ratio = actred / prered;
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241 else
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242 prered = 0;
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243 ratio = 0;
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244 endif
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245
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246 ## Update delta.
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247 if (ratio < 0.1)
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248 nsuc = 0;
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249 nfail ++;
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250 delta *= 0.5;
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251 if (delta <= 1e1*macheps*xn)
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252 ## Trust region became uselessly small.
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253 info = -3;
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254 break;
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255 endif
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256 else
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257 nfail = 0;
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258 nsuc ++;
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259 if (abs (1-ratio) <= 0.1)
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260 delta = 2*sn;
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261 elseif (ratio >= 0.5 || nsuc > 1)
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262 delta = max (delta, 2*sn);
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263 endif
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264 endif
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265
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266 if (ratio >= 1e-4)
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267 ## Successful iteration.
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268 x += s;
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269 xn = norm (dg .* x);
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270 fvec = fvec1;
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271 fn = fn1;
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272 niter ++;
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273 endif
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274
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275 ## FIXME: should outputfcn be only called after a successful iteration?
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276 if (! isempty (outfcn))
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277 optimvalues.iter = niter;
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278 optimvalues.funccount = nfev;
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279 optimvalues.fval = fn;
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280 optimvalues.searchdirection = s;
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281 state = 'iter';
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282 stop = outfcn (x, optimvalues, state);
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283 if (stop)
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284 info = -1;
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285 break;
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286 endif
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287 endif
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288
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289 ## Tests for termination conditions. A mysterious place, anything
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290 ## can happen if you change something here...
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291
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292 ## The rule of thumb (which I'm not sure M*b is quite following)
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293 ## is that for a tolerance that depends on scaling, only 0 makes
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294 ## sense as a default value. But 0 usually means uselessly long
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295 ## iterations, so we need scaling-independent tolerances wherever
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296 ## possible.
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297
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298 ## FIXME -- why tolf*n*xn? If abs (e) ~ abs(x) * eps is a vector
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299 ## of perturbations of x, then norm (fjac*e) <= eps*n*xn, i.e. by
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300 ## tolf ~ eps we demand as much accuracy as we can expect.
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301 if (fn <= tolf*n*xn)
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302 info = 1;
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303 ## The following tests done only after successful step.
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304 elseif (actred > 0)
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305 ## This one is classic. Note that we use scaled variables again,
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306 ## but compare to scaled step, so nothing bad.
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307 if (sn <= tolx*xn)
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308 info = 2;
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309 ## Again a classic one. It seems weird to use the same tolf
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310 ## for two different tests, but that's what M*b manual appears
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311 ## to say.
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312 elseif (actred < tolf)
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313 info = 3;
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314 endif
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315 endif
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316
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317 ## Criterion for recalculating jacobian.
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318 if (! updating || nfail == 2)
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319 break;
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320 endif
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321
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322 ## Compute the scaled Broyden update.
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323 if (useqr)
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324 u = (fvec1 - q*w) / sn;
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325 v = dg .* ((dg .* s) / sn);
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326
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327 ## Update the QR factorization.
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328 [q, r] = qrupdate (q, r, u, v);
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329 else
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330 u = (fvec1 - w);
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331 v = dg .* ((dg .* s) / sn);
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332
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333 ## update the jacobian
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334 fjac += u * v';
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335 endif
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336 endwhile
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337 endwhile
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338
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339 ## Restore original shapes.
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340 x = reshape (x, xsiz);
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341 fvec = reshape (fvec, fsiz);
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342
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343 output.iterations = niter;
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344 output.funcCount = nfev;
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345
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346 endfunction
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347
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348 ## An assistant function that evaluates a function handle and checks for
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349 ## bad results.
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350 function fx = guarded_eval (fun, x)
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351 fx = fun (x);
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352 if (! all (isreal (fx)))
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353 error ("fsolve:notreal", "fsolve: non-real value encountered");
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354 elseif (any (isnan (fx)))
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355 error ("fsolve:isnan", "fsolve: NaN value encountered");
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356 endif
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357 endfunction
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358
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359 %!function retval = f (p)
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360 %! x = p(1);
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361 %! y = p(2);
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362 %! z = p(3);
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363 %! retval = zeros (3, 1);
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364 %! retval(1) = sin(x) + y**2 + log(z) - 7;
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365 %! retval(2) = 3*x + 2**y -z**3 + 1;
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366 %! retval(3) = x + y + z - 5;
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367 %!test
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368 %! x_opt = [ 0.599054;
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369 %! 2.395931;
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370 %! 2.005014 ];
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371 %! tol = 1.0e-5;
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372 %! [x, fval, info] = fsolve (@f, [ 0.5; 2.0; 2.5 ]);
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373 %! assert (info > 0);
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374 %! assert (norm (x - x_opt, Inf) < tol);
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375 %! assert (norm (fval) < tol);
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376
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377 %!function retval = f (p)
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378 %! x = p(1);
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379 %! y = p(2);
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380 %! z = p(3);
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381 %! w = p(4);
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382 %! retval = zeros (4, 1);
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383 %! retval(1) = 3*x + 4*y + exp (z + w) - 1.007;
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384 %! retval(2) = 6*x - 4*y + exp (3*z + w) - 11;
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385 %! retval(3) = x^4 - 4*y^2 + 6*z - 8*w - 20;
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386 %! retval(4) = x^2 + 2*y^3 + z - w - 4;
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387 %!test
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388 %! x_opt = [ -0.767297326653401, 0.590671081117440, 1.47190018629642, -1.52719341133957 ];
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389 %! tol = 1.0e-5;
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390 %! [x, fval, info] = fsolve (@f, [-1, 1, 2, -1]);
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391 %! assert (info > 0);
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392 %! assert (norm (x - x_opt, Inf) < tol);
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393 %! assert (norm (fval) < tol);
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394
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395 %!function retval = f (p)
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396 %! x = p(1);
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397 %! y = p(2);
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398 %! z = p(3);
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399 %! retval = zeros (3, 1);
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400 %! retval(1) = sin(x) + y**2 + log(z) - 7;
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401 %! retval(2) = 3*x + 2**y -z**3 + 1;
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402 %! retval(3) = x + y + z - 5;
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403 %! retval(4) = x*x + y - z*log(z) - 1.36;
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404 %!test
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405 %! x_opt = [ 0.599054;
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406 %! 2.395931;
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407 %! 2.005014 ];
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408 %! tol = 1.0e-5;
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409 %! [x, fval, info] = fsolve (@f, [ 0.5; 2.0; 2.5 ]);
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410 %! assert (info > 0);
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411 %! assert (norm (x - x_opt, Inf) < tol);
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412 %! assert (norm (fval) < tol);
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413
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414 %!function retval = f (p)
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415 %! x = p(1);
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416 %! y = p(2);
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417 %! z = p(3);
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418 %! retval = zeros (3, 1);
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419 %! retval(1) = sin(x) + y**2 + log(z) - 7;
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420 %! retval(2) = 3*x + 2**y -z**3 + 1;
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421 %! retval(3) = x + y + z - 5;
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422 %!test
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423 %! x_opt = [ 0.599054;
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424 %! 2.395931;
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425 %! 2.005014 ];
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426 %! tol = 1.0e-5;
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427 %! opt = optimset ("Updating", "qrp");
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428 %! [x, fval, info] = fsolve (@f, [ 0.5; 2.0; 2.5 ], opt);
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429 %! assert (info > 0);
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430 %! assert (norm (x - x_opt, Inf) < tol);
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431 %! assert (norm (fval) < tol);
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432
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433 %!test
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434 %! b0 = 3;
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435 %! a0 = 0.2;
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436 %! x = 0:.5:5;
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437 %! noise = 1e-5 * sin (100*x);
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438 %! y = exp (-a0*x) + b0 + noise;
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439 %! c_opt = [a0, b0];
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440 %! tol = 1e-5;
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441 %!
c136d313206a improvements to fsolve
Jaroslav Hajek <highegg@gmail.com>
parents: 8562
diff changeset
442 %! [c, fval, info, output] = fsolve (@(c) (exp(-c(1)*x) + c(2) - y), [0, 0]);
c136d313206a improvements to fsolve
Jaroslav Hajek <highegg@gmail.com>
parents: 8562
diff changeset
443 %! assert (info > 0);
c136d313206a improvements to fsolve
Jaroslav Hajek <highegg@gmail.com>
parents: 8562
diff changeset
444 %! assert (norm (c - c_opt, Inf) < tol);
c136d313206a improvements to fsolve
Jaroslav Hajek <highegg@gmail.com>
parents: 8562
diff changeset
445 %! assert (norm (fval) < norm (noise));
c136d313206a improvements to fsolve
Jaroslav Hajek <highegg@gmail.com>
parents: 8562
diff changeset
446