Mercurial > hg > octave-nkf
annotate scripts/optimization/fsolve.m @ 8395:88fd356b0d95
optionally allow pivoting in fsolve
author | Jaroslav Hajek <highegg@gmail.com> |
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date | Thu, 11 Dec 2008 07:51:40 +0100 |
parents | a762d9daa700 |
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1 ## Copyright (C) 2008 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 # Solves 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. Currently, fsolve |
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33 # recognizes these options: FunValCheck, OutputFcn, TolX, TolFun, MaxIter, |
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34 # MaxFunEvals and Jacobian. |
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35 # |
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36 # If Jacobian is 'on', it specifies that @var{fcn}, called with 2 output arguments, |
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37 # also returns the Jacobian matrix of right-hand sides at the requested point. |
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38 # TolX specifies the termination tolerance in the unknown variables, while |
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39 # TolFun is a tolerance for equations. Default is @code{1e1*eps} |
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40 # for TolX and @code{1e2*eps} for TolFun. |
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41 # For description of the other options, see @code{optimset}. |
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42 # |
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43 # On return, @var{fval} contains the value of the function @var{fcn} |
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44 # evaluated at @var{x}, and @var{info} may be one of the following values: |
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45 # |
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46 # @table @asis |
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47 # @item 1 |
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48 # Converged to a solution point. Relative residual error is less than specified |
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49 # by TolFun. |
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50 # @item 2 |
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51 # Last relative step size was less that TolX. |
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52 # @item 3 |
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53 # Last relative decrease in residual was less than TolF. |
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54 # @item 0 |
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55 # Iteration limit exceeded. |
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56 # @item -3 |
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57 # The trust region radius became excessively small. |
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58 # @end table |
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59 # |
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60 # Note: If you only have a single nonlinear equation of one variable, using |
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61 # @code{fzero} is usually a much better idea. |
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62 # @seealso{fzero,optimset} |
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63 # @end deftypefn |
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64 |
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65 function [x, fvec, info, output, fjac] = fsolve (fcn, x0, options) |
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66 |
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67 if (nargin < 3) |
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68 options = struct (); |
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69 endif |
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70 |
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71 xsiz = size (x0); |
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72 n = numel (x0); |
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73 |
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74 has_jac = strcmp (optimget (options, "Jacobian", "off"), "on"); |
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75 maxiter = optimget (options, "MaxIter", Inf); |
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76 maxfev = optimget (options, "MaxFunEvals", Inf); |
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77 outfcn = optimget (options, "OutputFcn"); |
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78 pivoting = optimget (options, "Pivoting", false); |
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79 funvalchk = strcmp (optimget (options, "FunValCheck", "off"), "on"); |
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80 |
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81 if (funvalchk) |
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82 # replace fun with a guarded version |
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83 fun = @(x) guarded_eval (fun, x); |
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84 endif |
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85 |
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86 # These defaults are rather stringent. I think that normally, user prefers |
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87 # accuracy to performance. |
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88 |
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89 macheps = eps (class (x0)); |
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90 |
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91 tolx = optimget (options, "TolX", 1e1*macheps); |
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92 tolf = optimget (options, "TolFun",1e2*macheps); |
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93 |
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94 factor = 100; |
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95 # FIXME: TypicalX corresponds to user scaling (???) |
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96 autodg = true; |
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97 |
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98 niter = 1; nfev = 0; |
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99 |
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100 x = x0(:); |
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101 info = 0; |
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102 |
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103 # outer loop |
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104 while (niter < maxiter && nfev < maxfev && ! info) |
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105 |
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106 # calc func value and jacobian (possibly via FD) |
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107 # handle arbitrary shapes of x and f and remember them |
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108 if (has_jac) |
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109 [fvec, fjac] = fcn (reshape (x, xsiz)); |
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110 nfev ++; |
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111 else |
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112 [fvec, fjac] = __fdjac__ (fcn, reshape (x, xsiz)); |
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113 nfev += 1 + length (x); |
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114 endif |
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115 fsiz = size (fvec); |
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116 fvec = fvec(:); |
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117 fn = norm (fvec); |
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118 m = length (fvec); |
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119 n = length (x); |
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120 if (m < n) |
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121 error ("fsolve:under", "cannot solve underdetermined systems"); |
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122 elseif (m > n && niter == 1) |
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123 if (isempty (optimget (options, "TolFun"))) |
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124 warning (["An overdetermined system cannot usually be solved exactly. ", ... |
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125 "Consider specifying the TolFun option."]); |
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126 endif |
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127 endif |
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128 |
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129 # get QR factorization of the jacobian |
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130 if (pivoting) |
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131 [q, r, p] = qr (fjac); |
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132 else |
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133 [q, r] = qr (fjac); |
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134 endif |
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135 |
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136 # get column norms, use them as scaling factor |
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137 jcn = norm (fjac, 'columns').'; |
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138 if (niter == 1) |
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139 if (autodg) |
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140 dg = jcn; |
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141 dg(dg == 0) = 1; |
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142 endif |
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143 xn = norm (dg .* x); |
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144 delta = factor * xn; |
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145 endif |
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146 |
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147 # rescale if necessary |
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148 if (autodg) |
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149 dg = max (dg, jcn); |
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150 endif |
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151 |
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152 nfail = 0; |
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153 nsuc = 0; |
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154 # inner loop |
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155 while (niter <= maxiter && nfev < maxfev && ! info) |
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156 |
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157 qtf = q'*fvec; |
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158 |
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159 # get TR model (dogleg) minimizer |
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160 # in case of an overdetermined system, get lsq solution |
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161 s = - __dogleg__ (r(1:n,:), qtf(1:n), dg, delta); |
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162 if (pivoting) |
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163 s = p * s; |
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164 endif |
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165 sn = norm (dg .* s); |
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166 |
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167 if (niter == 1) |
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168 delta = min (delta, sn); |
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169 endif |
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170 |
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171 fvec1 = fcn (reshape (x + s, xsiz)) (:); |
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172 fn1 = norm (fvec1); |
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173 |
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174 if (fn1 < fn) |
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175 # scaled actual reduction |
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176 actred = 1 - (fn1/fn)^2; |
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177 else |
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178 actred = -1; |
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179 endif |
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180 |
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181 # scaled predicted reduction, and ratio |
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182 if (pivoting) |
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183 w = qtf + r*(p'*s); |
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184 else |
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185 w = qtf + r*s; |
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186 endif |
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187 t = norm (w); |
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188 if (t < fn) |
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189 prered = 1 - (t/fn)^2; |
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190 ratio = actred / prered; |
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191 else |
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192 prered = 0; |
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193 ratio = 0; |
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194 endif |
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195 |
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196 # update delta |
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197 if (ratio < 0.1) |
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198 nsuc = 0; |
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199 nfail ++; |
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200 delta *= 0.5; |
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201 if (delta <= sqrt (macheps)*xn) |
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202 # trust region became uselessly small |
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203 info = -3; |
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204 break; |
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205 endif |
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206 else |
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207 nfail = 0; |
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208 nsuc ++; |
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209 if (abs (1-ratio) <= 0.1) |
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210 delta = 2*sn; |
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211 elseif (ratio >= 0.5 || nsuc > 1) |
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212 delta = max (delta, 2*sn); |
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213 endif |
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214 endif |
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215 |
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216 if (ratio >= 1e-4) |
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217 # successful iteration |
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218 x += s; |
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219 xn = norm (dg .* x); |
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220 fvec = fvec1; |
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221 fn = fn1; |
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222 niter ++; |
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223 endif |
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224 |
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225 # Tests for termination conditions. A mysterious place, anything can |
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226 # happen if you change something here... |
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227 |
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228 # The rule of thumb (which I'm not sure M*b is quite following) is that |
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229 # for a tolerance that depends on scaling, only 0 makes sense as a |
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230 # default value. But 0 usually means uselessly long iterations, |
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231 # so we need scaling-independent tolerances wherever possible. |
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232 |
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233 # XXX: why tolf*n*xn? If abs (e) ~ abs(x) * eps is a vector of |
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234 # perturbations of x, then norm (fjac*e) <= eps*n*xn, i.e. by tolf ~ |
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235 # eps we demand as much accuracy as we can expect. |
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236 if (fn <= tolf*n*xn) |
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237 info = 1; |
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238 # The following tests done only after successful step. |
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239 elseif (actred > 0) |
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240 # This one is classic. Note that we use scaled variables again, but |
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241 # compare to scaled step, so nothing bad. |
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242 if (sn <= tolx*xn) |
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243 info = 2; |
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244 # Again a classic one. It seems weird to use the same tolf for two |
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245 # different tests, but that's what M*b manual appears to say. |
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246 elseif (actred < tolf) |
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247 info = 3; |
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248 endif |
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249 endif |
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250 |
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251 # criterion for recalculating jacobian |
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252 if (nfail == 2) |
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253 break; |
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254 endif |
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255 |
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256 # compute the scaled Broyden update |
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257 u = (fvec1 - q*w) / sn; |
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258 v = dg .* ((dg .* s) / sn); |
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259 if (pivoting) |
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260 v = p'*v; |
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261 endif |
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262 |
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263 # update the QR factorization |
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264 [q, r] = qrupdate (q, r, u, v); |
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265 |
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266 endwhile |
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267 endwhile |
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268 |
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269 # restore original shapes |
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270 x = reshape (x, xsiz); |
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271 fvec = reshape (fvec, fsiz); |
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272 |
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273 output.iterations = niter; |
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274 output.funcCount = niter + 2; |
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275 |
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276 endfunction |
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277 |
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278 # an assistant function that evaluates a function handle and checks for bad |
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279 # results. |
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280 function fx = guarded_eval (fun, x) |
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281 fx = fun (x); |
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282 if (! all (isreal (fx))) |
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283 error ("fsolve:notreal", "fsolve: non-real value encountered"); |
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284 elseif (any (isnan (fx))) |
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285 error ("fsolve:isnan", "fsolve: NaN value encountered"); |
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286 endif |
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287 endfunction |
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288 |
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289 %!function retval = f (p) |
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290 %! x = p(1); |
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291 %! y = p(2); |
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292 %! z = p(3); |
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293 %! retval = zeros (3, 1); |
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294 %! retval(1) = sin(x) + y**2 + log(z) - 7; |
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295 %! retval(2) = 3*x + 2**y -z**3 + 1; |
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296 %! retval(3) = x + y + z - 5; |
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297 %!test |
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298 %! x_opt = [ 0.599054; |
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299 %! 2.395931; |
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300 %! 2.005014 ]; |
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301 %! tol = 1.0e-5; |
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302 %! [x, fval, info] = fsolve (@f, [ 0.5; 2.0; 2.5 ]); |
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303 %! assert (info > 0); |
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304 %! assert (norm (x - x_opt, Inf) < tol); |
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305 %! assert (norm (fval) < tol); |
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306 |
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307 %!function retval = f (p) |
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308 %! x = p(1); |
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309 %! y = p(2); |
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310 %! z = p(3); |
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311 %! w = p(4); |
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312 %! retval = zeros (4, 1); |
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313 %! retval(1) = 3*x + 4*y + exp (z + w) - 1.007; |
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314 %! retval(2) = 6*x - 4*y + exp (3*z + w) - 11; |
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315 %! retval(3) = x^4 - 4*y^2 + 6*z - 8*w - 20; |
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316 %! retval(4) = x^2 + 2*y^3 + z - w - 4; |
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317 %!test |
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318 %! x_opt = [ -0.767297326653401, 0.590671081117440, 1.47190018629642, -1.52719341133957 ]; |
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319 %! tol = 1.0e-5; |
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320 %! [x, fval, info] = fsolve (@f, [-1, 1, 2, -1]); |
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321 %! assert (info > 0); |
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322 %! assert (norm (x - x_opt, Inf) < tol); |
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323 %! assert (norm (fval) < tol); |
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324 |
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325 %!function retval = f (p) |
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326 %! x = p(1); |
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327 %! y = p(2); |
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328 %! z = p(3); |
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329 %! retval = zeros (3, 1); |
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330 %! retval(1) = sin(x) + y**2 + log(z) - 7; |
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331 %! retval(2) = 3*x + 2**y -z**3 + 1; |
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332 %! retval(3) = x + y + z - 5; |
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333 %! retval(4) = x*x + y - z*log(z) - 1.36; |
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334 %!test |
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335 %! x_opt = [ 0.599054; |
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336 %! 2.395931; |
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337 %! 2.005014 ]; |
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338 %! tol = 1.0e-5; |
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339 %! [x, fval, info] = fsolve (@f, [ 0.5; 2.0; 2.5 ], optimset ("TolFun", 1e-6)); |
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340 %! assert (info > 0); |
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341 %! assert (norm (x - x_opt, Inf) < tol); |
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342 %! assert (norm (fval) < tol); |
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343 |
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344 %!function retval = f (p) |
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345 %! x = p(1); |
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346 %! y = p(2); |
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347 %! z = p(3); |
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348 %! retval = zeros (3, 1); |
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349 %! retval(1) = sin(x) + y**2 + log(z) - 7; |
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350 %! retval(2) = 3*x + 2**y -z**3 + 1; |
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351 %! retval(3) = x + y + z - 5; |
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352 %!test |
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353 %! x_opt = [ 0.599054; |
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354 %! 2.395931; |
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355 %! 2.005014 ]; |
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356 %! tol = 1.0e-5; |
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357 %! opt = optimset ("Pivoting", true); |
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358 %! [x, fval, info] = fsolve (@f, [ 0.5; 2.0; 2.5 ], opt); |
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359 %! assert (info > 0); |
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360 %! assert (norm (x - x_opt, Inf) < tol); |
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361 %! assert (norm (fval) < tol); |
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362 |