Mercurial > octave
annotate scripts/optimization/sqp.m @ 27919:1891570abac8
update Octave Project Developers copyright for the new year
In files that have the "Octave Project Developers" copyright notice,
update for 2020.
author | John W. Eaton <jwe@octave.org> |
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date | Mon, 06 Jan 2020 22:29:51 -0500 |
parents | b442ec6dda5c |
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1 ## Copyright (C) 2005-2020 The Octave Project Developers |
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2 ## |
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3 ## See the file COPYRIGHT.md in the top-level directory of this distribution |
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4 ## or <https://octave.org/COPYRIGHT.html/>. |
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5 ## |
5289 | 6 ## |
7 ## This file is part of Octave. | |
8 ## | |
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9 ## Octave is free software: you can redistribute it and/or modify it |
5289 | 10 ## under the terms of the GNU General Public License as published by |
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11 ## the Free Software Foundation, either version 3 of the License, or |
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12 ## (at your option) any later version. |
5289 | 13 ## |
14 ## Octave is distributed in the hope that it will be useful, but | |
15 ## WITHOUT ANY WARRANTY; without even the implied warranty of | |
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16 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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17 ## GNU General Public License for more details. |
5289 | 18 ## |
19 ## You should have received a copy of the GNU General Public License | |
7016 | 20 ## along with Octave; see the file COPYING. If not, see |
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21 ## <https://www.gnu.org/licenses/>. |
5289 | 22 |
23 ## -*- texinfo -*- | |
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24 ## @deftypefn {} {[@var{x}, @var{obj}, @var{info}, @var{iter}, @var{nf}, @var{lambda}] =} sqp (@var{x0}, @var{phi}) |
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25 ## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}) |
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26 ## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}) |
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27 ## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}) |
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28 ## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}, @var{maxiter}) |
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29 ## @deftypefnx {} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}, @var{maxiter}, @var{tol}) |
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30 ## Minimize an objective function using sequential quadratic programming (SQP). |
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31 ## |
5289 | 32 ## Solve the nonlinear program |
6741 | 33 ## @tex |
34 ## $$ | |
35 ## \min_x \phi (x) | |
36 ## $$ | |
37 ## @end tex | |
38 ## @ifnottex | |
5289 | 39 ## |
40 ## @example | |
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41 ## @group |
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42 ## min phi (x) |
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43 ## x |
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44 ## @end group |
5289 | 45 ## @end example |
46 ## | |
6741 | 47 ## @end ifnottex |
48 ## subject to | |
5289 | 49 ## @tex |
6741 | 50 ## $$ |
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51 ## g(x) = 0 \qquad h(x) \geq 0 \qquad lb \leq x \leq ub |
6741 | 52 ## $$ |
5289 | 53 ## @end tex |
6741 | 54 ## @ifnottex |
5289 | 55 ## |
56 ## @example | |
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57 ## @group |
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58 ## g(x) = 0 |
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59 ## h(x) >= 0 |
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60 ## lb <= x <= ub |
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61 ## @end group |
5289 | 62 ## @end example |
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63 ## |
6741 | 64 ## @end ifnottex |
5289 | 65 ## @noindent |
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66 ## using a sequential quadratic programming method. |
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68 ## The first argument is the initial guess for the vector @var{x0}. |
5289 | 69 ## |
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70 ## The second argument is a function handle pointing to the objective function |
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71 ## @var{phi}. The objective function must accept one vector argument and |
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72 ## return a scalar. |
5289 | 73 ## |
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74 ## The second argument may also be a 2- or 3-element cell array of function |
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75 ## handles. The first element should point to the objective function, the |
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76 ## second should point to a function that computes the gradient of the |
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77 ## objective function, and the third should point to a function that computes |
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78 ## the Hessian of the objective function. If the gradient function is not |
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79 ## supplied, the gradient is computed by finite differences. If the Hessian |
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80 ## function is not supplied, a BFGS update formula is used to approximate the |
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81 ## Hessian. |
5289 | 82 ## |
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83 ## When supplied, the gradient function @code{@var{phi}@{2@}} must accept one |
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84 ## vector argument and return a vector. When supplied, the Hessian function |
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85 ## @code{@var{phi}@{3@}} must accept one vector argument and return a matrix. |
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86 ## |
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87 ## The third and fourth arguments @var{g} and @var{h} are function handles |
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88 ## pointing to functions that compute the equality constraints and the |
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89 ## inequality constraints, respectively. If the problem does not have |
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90 ## equality (or inequality) constraints, then use an empty matrix ([]) for |
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91 ## @var{g} (or @var{h}). When supplied, these equality and inequality |
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92 ## constraint functions must accept one vector argument and return a vector. |
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93 ## |
5289 | 94 ## The third and fourth arguments may also be 2-element cell arrays of |
95 ## function handles. The first element should point to the constraint | |
96 ## function and the second should point to a function that computes the | |
97 ## gradient of the constraint function: | |
6741 | 98 ## @tex |
99 ## $$ | |
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100 ## \Bigg( {\partial f(x) \over \partial x_1}, |
6741 | 101 ## {\partial f(x) \over \partial x_2}, \ldots, |
102 ## {\partial f(x) \over \partial x_N} \Bigg)^T | |
103 ## $$ | |
104 ## @end tex | |
105 ## @ifnottex | |
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106 ## |
5289 | 107 ## @example |
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108 ## @group |
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109 ## [ d f(x) d f(x) d f(x) ] |
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110 ## transpose ( [ ------ ----- ... ------ ] ) |
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111 ## [ dx_1 dx_2 dx_N ] |
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112 ## @end group |
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114 ## |
6741 | 115 ## @end ifnottex |
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116 ## The fifth and sixth arguments, @var{lb} and @var{ub}, contain lower and |
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117 ## upper bounds on @var{x}. These must be consistent with the equality and |
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118 ## inequality constraints @var{g} and @var{h}. If the arguments are vectors |
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119 ## then @var{x}(i) is bound by @var{lb}(i) and @var{ub}(i). A bound can also |
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120 ## be a scalar in which case all elements of @var{x} will share the same |
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121 ## bound. If only one bound (lb, ub) is specified then the other will |
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122 ## default to (-@var{realmax}, +@var{realmax}). |
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123 ## |
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124 ## The seventh argument @var{maxiter} specifies the maximum number of |
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125 ## iterations. The default value is 100. |
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126 ## |
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127 ## The eighth argument @var{tol} specifies the tolerance for the stopping |
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128 ## criteria. The default value is @code{sqrt (eps)}. |
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129 ## |
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130 ## The value returned in @var{info} may be one of the following: |
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131 ## |
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132 ## @table @asis |
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133 ## @item 101 |
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134 ## The algorithm terminated normally. |
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135 ## All constraints meet the specified tolerance. |
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136 ## |
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137 ## @item 102 |
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138 ## The BFGS update failed. |
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139 ## |
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140 ## @item 103 |
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141 ## The maximum number of iterations was reached. |
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142 ## |
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143 ## @item 104 |
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144 ## The stepsize has become too small, i.e., |
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145 ## @tex |
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146 ## $\Delta x,$ |
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147 ## @end tex |
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148 ## @ifnottex |
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149 ## delta @var{x}, |
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150 ## @end ifnottex |
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151 ## is less than @code{@var{tol} * norm (x)}. |
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152 ## @end table |
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153 ## |
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154 ## An example of calling @code{sqp}: |
5289 | 155 ## |
156 ## @example | |
7031 | 157 ## function r = g (x) |
158 ## r = [ sumsq(x)-10; | |
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159 ## x(2)*x(3)-5*x(4)*x(5); |
7031 | 160 ## x(1)^3+x(2)^3+1 ]; |
161 ## endfunction | |
162 ## | |
163 ## function obj = phi (x) | |
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164 ## obj = exp (prod (x)) - 0.5*(x(1)^3+x(2)^3+1)^2; |
7031 | 165 ## endfunction |
5289 | 166 ## |
7031 | 167 ## x0 = [-1.8; 1.7; 1.9; -0.8; -0.8]; |
168 ## | |
169 ## [x, obj, info, iter, nf, lambda] = sqp (x0, @@phi, @@g, []) | |
5289 | 170 ## |
7031 | 171 ## x = |
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172 ## |
7031 | 173 ## -1.71714 |
174 ## 1.59571 | |
175 ## 1.82725 | |
176 ## -0.76364 | |
177 ## -0.76364 | |
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178 ## |
7031 | 179 ## obj = 0.053950 |
180 ## info = 101 | |
181 ## iter = 8 | |
182 ## nf = 10 | |
183 ## lambda = | |
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184 ## |
7031 | 185 ## -0.0401627 |
186 ## 0.0379578 | |
187 ## -0.0052227 | |
5289 | 188 ## @end example |
189 ## | |
5642 | 190 ## @seealso{qp} |
5289 | 191 ## @end deftypefn |
192 | |
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193 function [x, obj, info, iter, nf, lambda] = sqp (x0, objf, cef, cif, lb, ub, maxiter, tolerance) |
5289 | 194 |
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195 globals = struct (); # data and handles, needed and changed by subfunctions |
5289 | 196 |
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197 if (nargin < 2 || nargin > 8 || nargin == 5) |
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198 print_usage (); |
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199 endif |
5289 | 200 |
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201 if (! isvector (x0)) |
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202 error ("sqp: X0 must be a vector"); |
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203 endif |
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204 if (rows (x0) == 1) |
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205 x0 = x0'; |
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206 endif |
5289 | 207 |
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208 have_grd = 0; |
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209 have_hess = 0; |
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210 if (iscell (objf)) |
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211 switch (numel (objf)) |
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212 case 1 |
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213 obj_fun = objf{1}; |
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214 obj_grd = @(x, obj) fd_obj_grd (x, obj, obj_fun); |
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215 case 2 |
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216 obj_fun = objf{1}; |
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217 obj_grd = objf{2}; |
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218 have_grd = 1; |
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219 case 3 |
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220 obj_fun = objf{1}; |
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221 obj_grd = objf{2}; |
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222 obj_hess = objf{3}; |
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223 have_grd = 1; |
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224 have_hess = 1; |
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225 otherwise |
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226 error ("sqp: invalid objective function specification"); |
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227 endswitch |
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228 else |
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229 obj_fun = objf; # No cell array, only obj_fun set |
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230 obj_grd = @(x, obj) fd_obj_grd (x, obj, obj_fun); |
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231 endif |
5289 | 232 |
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233 ce_fun = @empty_cf; |
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234 ce_grd = @empty_jac; |
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235 if (nargin > 2) |
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236 if (iscell (cef)) |
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237 switch (numel (cef)) |
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238 case 1 |
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239 ce_fun = cef{1}; |
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240 ce_grd = @(x) fd_ce_jac (x, ce_fun); |
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241 case 2 |
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242 ce_fun = cef{1}; |
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243 ce_grd = cef{2}; |
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244 otherwise |
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245 error ("sqp: invalid equality constraint function specification"); |
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246 endswitch |
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247 elseif (! isempty (cef)) |
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248 ce_fun = cef; # No cell array, only constraint equality function set |
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249 ce_grd = @(x) fd_ce_jac (x, ce_fun); |
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250 endif |
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251 endif |
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252 |
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253 ci_fun = @empty_cf; |
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254 ci_grd = @empty_jac; |
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255 if (nargin > 3) |
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256 ## constraint function given by user with possible gradient |
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257 globals.cif = cif; |
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258 ## constraint function given by user without gradient |
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259 globals.cifcn = @empty_cf; |
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260 if (iscell (cif)) |
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261 if (length (cif) > 0) |
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262 globals.cifcn = cif{1}; |
5289 | 263 endif |
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264 elseif (! isempty (cif)) |
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265 globals.cifcn = cif; |
5289 | 266 endif |
267 | |
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268 if (nargin < 5 || (nargin > 5 && isempty (lb) && isempty (ub))) |
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269 ## constraint inequality function only without any bounds |
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270 ci_grd = @(x) fd_ci_jac (x, globals.cifcn); |
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271 if (iscell (cif)) |
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272 switch (length (cif)) |
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273 case 1 |
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274 ci_fun = cif{1}; |
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275 case 2 |
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276 ci_fun = cif{1}; |
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277 ci_grd = cif{2}; |
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278 otherwise |
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279 error ("sqp: invalid inequality constraint function specification"); |
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280 endswitch |
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281 elseif (! isempty (cif)) |
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282 ci_fun = cif; # No cell array, only constraint inequality function set |
5289 | 283 endif |
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284 else |
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285 ## constraint inequality function with bounds present |
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286 lb_idx = ub_idx = true (size (x0)); |
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287 ub_grad = - (lb_grad = eye (rows (x0))); |
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288 if (isvector (lb)) |
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289 globals.lb = tmp_lb = lb(:); |
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290 lb_idx(:) = tmp_idx = (lb != -Inf); |
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291 globals.lb = globals.lb(tmp_idx, 1); |
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292 lb_grad = lb_grad(lb_idx, :); |
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293 elseif (isempty (lb)) |
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294 if (isa (x0, "single")) |
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295 globals.lb = tmp_lb = -realmax ("single"); |
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296 else |
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297 globals.lb = tmp_lb = -realmax; |
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299 else |
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300 error ("sqp: invalid lower bound"); |
6768 | 301 endif |
302 | |
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303 if (isvector (ub)) |
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304 globals.ub = tmp_ub = ub(:); |
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305 ub_idx(:) = tmp_idx = (ub != Inf); |
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306 globals.ub = globals.ub(tmp_idx, 1); |
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307 ub_grad = ub_grad(ub_idx, :); |
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308 elseif (isempty (ub)) |
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309 if (isa (x0, "single")) |
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310 globals.ub = tmp_ub = realmax ("single"); |
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311 else |
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312 globals.ub = tmp_ub = realmax; |
10549 | 313 endif |
6768 | 314 else |
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315 error ("sqp: invalid upper bound"); |
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316 endif |
6768 | 317 |
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318 if (any (tmp_lb > tmp_ub)) |
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319 error ("sqp: upper bound smaller than lower bound"); |
6768 | 320 endif |
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321 bounds_grad = [lb_grad; ub_grad]; |
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322 ci_fun = @(x) cf_ub_lb (x, lb_idx, ub_idx, globals); |
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323 ci_grd = @(x) cigrad_ub_lb (x, bounds_grad, globals); |
6768 | 324 endif |
5289 | 325 |
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326 endif # if (nargin > 3) |
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327 |
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328 iter_max = 100; |
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329 if (nargin > 6 && ! isempty (maxiter)) |
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330 if (isscalar (maxiter) && maxiter > 0 && fix (maxiter) == maxiter) |
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331 iter_max = maxiter; |
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332 else |
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333 error ("sqp: invalid number of maximum iterations"); |
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334 endif |
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335 endif |
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336 |
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337 tol = sqrt (eps); |
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338 if (nargin > 7 && ! isempty (tolerance)) |
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339 if (isscalar (tolerance) && tolerance > 0) |
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340 tol = tolerance; |
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341 else |
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342 error ("sqp: invalid value for TOLERANCE"); |
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343 endif |
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344 endif |
5289 | 345 |
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346 ## Initialize variables for search loop |
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347 ## Seed x with initial guess and evaluate objective function, constraints, |
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348 ## and gradients at initial value x0. |
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349 ## |
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350 ## obj_fun -- objective function |
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351 ## obj_grad -- objective gradient |
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352 ## ce_fun -- equality constraint functions |
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353 ## ci_fun -- inequality constraint functions |
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354 ## A == [grad_{x_1} cx_fun, grad_{x_2} cx_fun, ..., grad_{x_n} cx_fun]^T |
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355 x = x0; |
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356 |
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357 obj = feval (obj_fun, x0); |
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358 globals.nfun = 1; |
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359 |
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360 if (have_grd) |
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361 c = feval (obj_grd, x0); |
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362 else |
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363 c = feval (obj_grd, x0, obj); |
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364 endif |
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365 |
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366 ## Choose an initial NxN symmetric positive definite Hessian approximation B. |
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367 n = length (x0); |
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368 if (have_hess) |
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369 B = feval (obj_hess, x0); |
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370 else |
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371 B = eye (n, n); |
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372 endif |
5289 | 373 |
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374 ce = feval (ce_fun, x0); |
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375 F = feval (ce_grd, x0); |
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376 |
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377 ci = feval (ci_fun, x0); |
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378 C = feval (ci_grd, x0); |
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379 |
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380 A = [F; C]; |
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381 |
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382 ## Choose an initial lambda (x is provided by the caller). |
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383 lambda = 100 * ones (rows (A), 1); |
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384 |
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385 qp_iter = 1; |
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386 alpha = 1; |
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387 |
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388 info = 0; |
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389 iter = 0; |
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390 # report (); # Called with no arguments to initialize reporting |
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391 # report (iter, qp_iter, alpha, __sqp_nfun__, obj); |
5289 | 392 |
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393 while (++iter < iter_max) |
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394 |
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395 ## Check convergence. This is just a simple check on the first |
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396 ## order necessary conditions. |
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397 nr_f = rows (F); |
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398 |
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399 lambda_e = lambda((1:nr_f)'); |
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400 lambda_i = lambda((nr_f+1:end)'); |
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401 |
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402 con = [ce; ci]; |
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403 |
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404 t0 = norm (c - A' * lambda); |
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405 t1 = norm (ce); |
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406 t2 = all (ci >= 0); |
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407 t3 = all (lambda_i >= 0); |
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408 t4 = norm (lambda .* con); |
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409 |
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410 ## Normal convergence. All constraints are satisfied |
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411 ## and objective has converged. |
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412 if (t2 && t3 && max ([t0; t1; t4]) < tol) |
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413 info = 101; |
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414 break; |
6382 | 415 endif |
416 | |
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417 ## Compute search direction p by solving QP. |
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418 g = -ce; |
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419 d = -ci; |
5289 | 420 |
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421 old_lambda = lambda; |
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422 [p, obj_qp, INFO, lambda] = qp (x, B, c, F, g, [], [], d, C, |
26332
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qp and sqp: Non-fixed tolerance for qp (bug #53506).
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423 Inf (size (d)), struct ("TolX", tol)); |
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424 |
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425 info = INFO.info; |
5289 | 426 |
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427 ## FIXME: check QP solution and attempt to recover if it has failed. |
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428 ## For now, just warn about possible problems. |
19593
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429 |
13206
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430 id = "Octave:SQP-QP-subproblem"; |
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431 switch (info) |
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432 case 2 |
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433 warning (id, "sqp: QP subproblem is non-convex and unbounded"); |
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434 case 3 |
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435 warning (id, "sqp: QP subproblem failed to converge in %d iterations", |
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436 INFO.solveiter); |
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437 case 6 |
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438 warning (id, "sqp: QP subproblem is infeasible"); |
22224
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Allow sequential quadratic programs with infeasible QPs (bug #36015).
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parents:
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439 lambda = old_lambda; # The return value was size 0x0 in this case. |
13206
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440 endswitch |
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441 |
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442 ## Choose mu such that p is a descent direction for the chosen |
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443 ## merit function phi. |
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444 [x_new, alpha, obj_new, globals] = ... |
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445 linesearch_L1 (x, p, obj_fun, obj_grd, ce_fun, ci_fun, lambda, ... |
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446 obj, c, globals); |
26617
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447 |
26237
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448 delx = x_new - x; |
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449 |
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450 ## Check if step size has become too small (indicates lack of progress). |
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451 if (norm (delx) < tol * norm (x)) |
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452 info = 104; |
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453 break; |
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454 endif |
5289 | 455 |
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456 ## Evaluate objective function, constraints, and gradients at x_new. |
26237
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457 if (have_grd) |
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458 c_new = feval (obj_grd, x_new); |
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459 else |
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460 c_new = feval (obj_grd, x_new, obj_new); |
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461 endif |
5289 | 462 |
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463 ce_new = feval (ce_fun, x_new); |
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464 F_new = feval (ce_grd, x_new); |
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465 |
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466 ci_new = feval (ci_fun, x_new); |
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467 C_new = feval (ci_grd, x_new); |
5289 | 468 |
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469 A_new = [F_new; C_new]; |
5289 | 470 |
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471 ## Set |
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472 ## |
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473 ## s = alpha * p |
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474 ## y = grad_x L (x_new, lambda) - grad_x L (x, lambda}) |
6527 | 475 |
10678
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476 y = c_new - c; |
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477 |
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478 if (! isempty (A)) |
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479 t = ((A_new - A)'*lambda); |
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480 y -= t; |
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|
481 endif |
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|
482 |
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483 if (have_hess) |
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|
484 |
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485 B = feval (obj_hess, x); |
5289 | 486 |
10678
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|
487 else |
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|
488 ## Update B using a quasi-Newton formula. |
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489 delxt = delx'; |
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|
490 |
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|
491 ## Damped BFGS. Or maybe we would actually want to use the Hessian |
11348
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492 ## of the Lagrangian, computed directly? |
10678
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493 d1 = delxt*B*delx; |
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|
494 |
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|
495 t1 = 0.2 * d1; |
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496 t2 = delxt*y; |
5289 | 497 |
10678
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|
498 if (t2 < t1) |
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499 theta = 0.8*d1/(d1 - t2); |
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|
500 else |
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|
501 theta = 1; |
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502 endif |
5289 | 503 |
10678
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504 r = theta*y + (1-theta)*B*delx; |
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|
505 |
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|
506 d2 = delxt*r; |
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|
507 |
18255
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Arun Giridhar <arungiridhar@gmail.com>
parents:
17744
diff
changeset
|
508 ## Check if the next BFGS update will work properly. |
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parents:
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509 ## If d1 or d2 vanish, the BFGS update will fail. |
10678
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510 if (d1 == 0 || d2 == 0) |
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511 info = 102; |
10549 | 512 break; |
5289 | 513 endif |
514 | |
10678
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515 B = B - B*delx*delxt*B/d1 + r*r'/d2; |
5289 | 516 |
517 endif | |
518 | |
10678
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519 x = x_new; |
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520 |
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521 obj = obj_new; |
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|
522 |
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changeset
|
523 c = c_new; |
5289 | 524 |
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525 ce = ce_new; |
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526 F = F_new; |
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|
527 |
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|
528 ci = ci_new; |
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529 C = C_new; |
5289 | 530 |
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531 A = A_new; |
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|
532 |
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533 # report (iter, qp_iter, alpha, __sqp_nfun__, obj); |
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|
534 |
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|
535 endwhile |
5289 | 536 |
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parents:
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changeset
|
537 ## Check if we've spent too many iterations without converging. |
10678
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538 if (iter >= iter_max) |
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|
539 info = 103; |
5289 | 540 endif |
541 | |
14595
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parents:
14552
diff
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|
542 nf = globals.nfun; |
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543 |
7399 | 544 endfunction |
5289 | 545 |
546 | |
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|
547 function [merit, obj, globals] = phi_L1 (obj, obj_fun, ce_fun, ci_fun, ... |
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|
548 x, mu, globals) |
5289 | 549 |
550 ce = feval (ce_fun, x); | |
551 ci = feval (ci_fun, x); | |
552 | |
553 idx = ci < 0; | |
554 | |
555 con = [ce; ci(idx)]; | |
556 | |
557 if (isempty (obj)) | |
558 obj = feval (obj_fun, x); | |
20735
418ae0cb752f
Replace ++,-- with in-place operators for performance.
Rik <rik@octave.org>
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20165
diff
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|
559 globals.nfun += 1; |
5289 | 560 endif |
561 | |
562 merit = obj; | |
563 t = norm (con, 1) / mu; | |
564 | |
565 if (! isempty (t)) | |
566 merit += t; | |
567 endif | |
568 | |
7399 | 569 endfunction |
5289 | 570 |
571 | |
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|
572 function [x_new, alpha, obj, globals] = ... |
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|
573 linesearch_L1 (x, p, obj_fun, obj_grd, ce_fun, ci_fun, lambda, obj, c, globals) |
5289 | 574 |
575 ## Choose parameters | |
576 ## | |
577 ## eta in the range (0, 0.5) | |
578 ## tau in the range (0, 1) | |
579 | |
580 eta = 0.25; | |
581 tau = 0.5; | |
582 | |
583 delta_bar = sqrt (eps); | |
584 | |
585 if (isempty (lambda)) | |
586 mu = 1 / delta_bar; | |
587 else | |
588 mu = 1 / (norm (lambda, Inf) + delta_bar); | |
589 endif | |
590 | |
591 alpha = 1; | |
592 | |
593 ce = feval (ce_fun, x); | |
594 | |
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595 [phi_x_mu, obj, globals] = phi_L1 (obj, obj_fun, ce_fun, ci_fun, x, ... |
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596 mu, globals); |
5289 | 597 |
598 D_phi_x_mu = c' * p; | |
599 d = feval (ci_fun, x); | |
600 ## only those elements of d corresponding | |
601 ## to violated constraints should be included. | |
602 idx = d < 0; | |
603 t = - norm ([ce; d(idx)], 1) / mu; | |
604 if (! isempty (t)) | |
605 D_phi_x_mu += t; | |
606 endif | |
607 | |
608 while (1) | |
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609 [p1, obj, globals] = phi_L1 ([], obj_fun, ce_fun, ci_fun, ... |
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610 x+alpha*p, mu, globals); |
5289 | 611 p2 = phi_x_mu+eta*alpha*D_phi_x_mu; |
612 if (p1 > p2) | |
613 ## Reset alpha = tau_alpha * alpha for some tau_alpha in the | |
614 ## range (0, tau). | |
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615 tau_alpha = 0.9 * tau; # ?? |
5289 | 616 alpha = tau_alpha * alpha; |
617 else | |
618 break; | |
619 endif | |
620 endwhile | |
621 | |
622 x_new = x + alpha * p; | |
623 | |
7399 | 624 endfunction |
5289 | 625 |
626 | |
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627 function grd = fdgrd (f, x, y0) |
5289 | 628 |
629 if (! isempty (f)) | |
630 nx = length (x); | |
631 grd = zeros (nx, 1); | |
632 deltax = sqrt (eps); | |
633 for i = 1:nx | |
634 t = x(i); | |
635 x(i) += deltax; | |
636 grd(i) = (feval (f, x) - y0) / deltax; | |
637 x(i) = t; | |
638 endfor | |
639 else | |
640 grd = zeros (0, 1); | |
641 endif | |
642 | |
7399 | 643 endfunction |
5289 | 644 |
645 | |
646 function jac = fdjac (f, x) | |
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647 |
6768 | 648 nx = length (x); |
5289 | 649 if (! isempty (f)) |
650 y0 = feval (f, x); | |
651 nf = length (y0); | |
652 nx = length (x); | |
653 jac = zeros (nf, nx); | |
654 deltax = sqrt (eps); | |
655 for i = 1:nx | |
656 t = x(i); | |
657 x(i) += deltax; | |
658 jac(:,i) = (feval (f, x) - y0) / deltax; | |
659 x(i) = t; | |
660 endfor | |
661 else | |
662 jac = zeros (0, nx); | |
663 endif | |
664 | |
7399 | 665 endfunction |
5289 | 666 |
667 | |
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668 function grd = fd_obj_grd (x, obj, obj_fun) |
5289 | 669 |
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670 grd = fdgrd (obj_fun, x, obj); |
5289 | 671 |
7399 | 672 endfunction |
5289 | 673 |
674 | |
675 function res = empty_cf (x) | |
676 | |
677 res = zeros (0, 1); | |
678 | |
7399 | 679 endfunction |
5289 | 680 |
681 | |
682 function res = empty_jac (x) | |
683 | |
684 res = zeros (0, length (x)); | |
685 | |
7399 | 686 endfunction |
5289 | 687 |
688 | |
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689 function jac = fd_ce_jac (x, ce_fun) |
5289 | 690 |
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691 jac = fdjac (ce_fun, x); |
5289 | 692 |
7399 | 693 endfunction |
5289 | 694 |
695 | |
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696 function jac = fd_ci_jac (x, cifcn) |
5289 | 697 |
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698 ## cifcn = constraint function without gradients and lb or ub |
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699 jac = fdjac (cifcn, x); |
6768 | 700 |
7399 | 701 endfunction |
6768 | 702 |
7017 | 703 |
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704 function res = cf_ub_lb (x, lbidx, ubidx, globals) |
5289 | 705 |
6768 | 706 ## combine constraint function with ub and lb |
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707 if (isempty (globals.cifcn)) |
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708 res = [x(lbidx,1)-globals.lb; globals.ub-x(ubidx,1)]; |
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709 else |
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710 res = [feval(globals.cifcn,x); x(lbidx,1)-globals.lb; |
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711 globals.ub-x(ubidx,1)]; |
6768 | 712 endif |
5289 | 713 |
7399 | 714 endfunction |
6768 | 715 |
7017 | 716 |
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717 function res = cigrad_ub_lb (x, bgrad, globals) |
6768 | 718 |
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719 cigradfcn = @(x) fd_ci_jac (x, globals.cifcn); |
6768 | 720 |
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721 if (iscell (globals.cif) && length (globals.cif) > 1) |
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722 cigradfcn = globals.cif{2}; |
6768 | 723 endif |
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|
724 |
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725 if (isempty (cigradfcn)) |
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726 res = bgrad; |
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727 else |
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728 res = [feval(cigradfcn,x); bgrad]; |
6768 | 729 endif |
730 | |
7399 | 731 endfunction |
7361 | 732 |
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733 ## Utility function used to debug sqp |
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734 function report (iter, qp_iter, alpha, nfun, obj) |
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735 |
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|
736 if (nargin == 0) |
21634
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|
737 printf (" Itn ItQP Step Nfun Objective\n"); |
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738 else |
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|
739 printf ("%5d %4d %8.1g %5d %13.6e\n", iter, qp_iter, alpha, nfun, obj); |
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740 endif |
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741 |
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742 endfunction |
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743 |
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744 |
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745 ################################################################################ |
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746 ## Test Code |
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747 |
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|
748 %!function r = __g (x) |
7371 | 749 %! r = [sumsq(x)-10; |
750 %! x(2)*x(3)-5*x(4)*x(5); | |
751 %! x(1)^3+x(2)^3+1 ]; | |
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752 %!endfunction |
7361 | 753 %! |
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754 %!function obj = __phi (x) |
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755 %! obj = exp (prod (x)) - 0.5*(x(1)^3 + x(2)^3 + 1)^2; |
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756 %!endfunction |
7361 | 757 %! |
758 %!test | |
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759 %! |
7361 | 760 %! x0 = [-1.8; 1.7; 1.9; -0.8; -0.8]; |
761 %! | |
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762 %! [x, obj, info, iter, nf, lambda] = sqp (x0, @__phi, @__g, []); |
7361 | 763 %! |
764 %! x_opt = [-1.717143501952599; | |
765 %! 1.595709610928535; | |
766 %! 1.827245880097156; | |
767 %! -0.763643103133572; | |
768 %! -0.763643068453300]; | |
769 %! | |
7371 | 770 %! obj_opt = 0.0539498477702739; |
7361 | 771 %! |
15757
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|
772 %! assert (x, x_opt, 8*sqrt (eps)); |
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773 %! assert (obj, obj_opt, sqrt (eps)); |
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|
774 |
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|
775 ## Test input validation |
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|
776 %!error sqp () |
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|
777 %!error sqp (1) |
35338deff753
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|
778 %!error sqp (1,2,3,4,5,6,7,8,9) |
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779 %!error sqp (1,2,3,4,5) |
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|
780 %!error sqp (ones (2,2)) |
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781 %!error sqp (1, cell (4,1)) |
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782 %!error sqp (1, cell (3,1), cell (3,1)) |
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|
783 %!error sqp (1, cell (3,1), cell (2,1), cell (3,1)) |
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|
784 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1), ones (2,2),[]) |
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|
785 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[], ones (2,2)) |
f3d52523cde1
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|
786 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),1,-1) |
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|
787 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[], ones (2,2)) |
f3d52523cde1
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|
788 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],-1) |
f3d52523cde1
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|
789 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],1.5) |
f3d52523cde1
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|
790 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],[], ones (2,2)) |
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|
791 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],[],-1) |