Mercurial > hg > octave-nkf
annotate scripts/optimization/sqp.m @ 15116:3d7a7ae53bbf gui
Further integration of GUI in the build system.
* Makefile.am (SUBDIRS): Add gui conditionally.
* configure.ac (AC_CHECK_PROGS(rcc)): Check for new program.
(AC_ARG_ENABLE(gui): New configure flag.
* gui/src/Makefile.am: Add support for resource file compilation.
Use nodist_XXX_SOURCES for MOC files.
author | Michael Goffioul <michael.goffioul@gmail.com> |
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date | Sun, 05 Aug 2012 20:04:53 +0100 |
parents | 5d3a684236b0 |
children | 534a2c881f45 |
rev | line source |
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1 ## Copyright (C) 2005-2012 John W. Eaton |
5289 | 2 ## |
3 ## This file is part of Octave. | |
4 ## | |
5 ## Octave is free software; you can redistribute it and/or modify it | |
6 ## under the terms of the GNU General Public License as published by | |
7016 | 7 ## the Free Software Foundation; either version 3 of the License, or (at |
8 ## your option) any later version. | |
5289 | 9 ## |
10 ## Octave is distributed in the hope that it will be useful, but | |
11 ## WITHOUT ANY WARRANTY; without even the implied warranty of | |
12 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU | |
13 ## General Public License for more details. | |
14 ## | |
15 ## You should have received a copy of the GNU General Public License | |
7016 | 16 ## along with Octave; see the file COPYING. If not, see |
17 ## <http://www.gnu.org/licenses/>. | |
5289 | 18 |
19 ## -*- texinfo -*- | |
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20 ## @deftypefn {Function File} {[@var{x}, @var{obj}, @var{info}, @var{iter}, @var{nf}, @var{lambda}] =} sqp (@var{x0}, @var{phi}) |
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21 ## @deftypefnx {Function File} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}) |
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22 ## @deftypefnx {Function File} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}) |
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23 ## @deftypefnx {Function File} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}) |
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24 ## @deftypefnx {Function File} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}, @var{maxiter}) |
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25 ## @deftypefnx {Function File} {[@dots{}] =} sqp (@var{x0}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}, @var{maxiter}, @var{tol}) |
5289 | 26 ## Solve the nonlinear program |
6741 | 27 ## @tex |
28 ## $$ | |
29 ## \min_x \phi (x) | |
30 ## $$ | |
31 ## @end tex | |
32 ## @ifnottex | |
5289 | 33 ## |
34 ## @example | |
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35 ## @group |
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36 ## min phi (x) |
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37 ## x |
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38 ## @end group |
5289 | 39 ## @end example |
40 ## | |
6741 | 41 ## @end ifnottex |
42 ## subject to | |
5289 | 43 ## @tex |
6741 | 44 ## $$ |
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45 ## g(x) = 0 \qquad h(x) \geq 0 \qquad lb \leq x \leq ub |
6741 | 46 ## $$ |
5289 | 47 ## @end tex |
6741 | 48 ## @ifnottex |
5289 | 49 ## |
50 ## @example | |
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51 ## @group |
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52 ## g(x) = 0 |
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53 ## h(x) >= 0 |
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54 ## lb <= x <= ub |
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55 ## @end group |
5289 | 56 ## @end example |
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57 ## |
6741 | 58 ## @end ifnottex |
5289 | 59 ## @noindent |
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60 ## using a sequential quadratic programming method. |
5289 | 61 ## |
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62 ## The first argument is the initial guess for the vector @var{x0}. |
5289 | 63 ## |
7001 | 64 ## The second argument is a function handle pointing to the objective |
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65 ## function @var{phi}. The objective function must accept one vector |
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66 ## argument and return a scalar. |
5289 | 67 ## |
68 ## The second argument may also be a 2- or 3-element cell array of | |
69 ## function handles. The first element should point to the objective | |
70 ## function, the second should point to a function that computes the | |
71 ## gradient of the objective function, and the third should point to a | |
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72 ## function that computes the Hessian of the objective function. If the |
5289 | 73 ## gradient function is not supplied, the gradient is computed by finite |
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74 ## differences. If the Hessian function is not supplied, a BFGS update |
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75 ## formula is used to approximate the Hessian. |
5289 | 76 ## |
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77 ## When supplied, the gradient function @code{@var{phi}@{2@}} must accept |
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78 ## one vector argument and return a vector. When supplied, the Hessian |
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79 ## function @code{@var{phi}@{3@}} must accept one vector argument and |
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80 ## return a matrix. |
5289 | 81 ## |
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82 ## The third and fourth arguments @var{g} and @var{h} are function |
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83 ## handles pointing to functions that compute the equality constraints |
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84 ## and the inequality constraints, respectively. If the problem does |
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85 ## not have equality (or inequality) constraints, then use an empty |
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86 ## matrix ([]) for @var{g} (or @var{h}). When supplied, these equality |
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87 ## and inequality constraint functions must accept one vector argument |
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88 ## and return a vector. |
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89 ## |
5289 | 90 ## The third and fourth arguments may also be 2-element cell arrays of |
91 ## function handles. The first element should point to the constraint | |
92 ## function and the second should point to a function that computes the | |
93 ## gradient of the constraint function: | |
6741 | 94 ## @tex |
95 ## $$ | |
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96 ## \Bigg( {\partial f(x) \over \partial x_1}, |
6741 | 97 ## {\partial f(x) \over \partial x_2}, \ldots, |
98 ## {\partial f(x) \over \partial x_N} \Bigg)^T | |
99 ## $$ | |
100 ## @end tex | |
101 ## @ifnottex | |
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102 ## |
5289 | 103 ## @example |
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104 ## @group |
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105 ## [ d f(x) d f(x) d f(x) ] |
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106 ## transpose ( [ ------ ----- ... ------ ] ) |
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107 ## [ dx_1 dx_2 dx_N ] |
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108 ## @end group |
5289 | 109 ## @end example |
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110 ## |
6741 | 111 ## @end ifnottex |
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112 ## The fifth and sixth arguments, @var{lb} and @var{ub}, contain lower |
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113 ## and upper bounds on @var{x}. These must be consistent with the |
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114 ## equality and inequality constraints @var{g} and @var{h}. If the |
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115 ## arguments are vectors then @var{x}(i) is bound by @var{lb}(i) and |
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116 ## @var{ub}(i). A bound can also be a scalar in which case all elements |
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117 ## of @var{x} will share the same bound. If only one bound (lb, ub) is |
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118 ## specified then the other will default to (-@var{realmax}, |
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119 ## +@var{realmax}). |
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120 ## |
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121 ## The seventh argument @var{maxiter} specifies the maximum number of |
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122 ## iterations. The default value is 100. |
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123 ## |
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124 ## The eighth argument @var{tol} specifies the tolerance for the |
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125 ## stopping criteria. The default value is @code{sqrt (eps)}. |
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126 ## |
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127 ## The value returned in @var{info} may be one of the following: |
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128 ## |
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129 ## @table @asis |
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130 ## @item 101 |
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131 ## The algorithm terminated normally. |
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132 ## Either all constraints meet the requested tolerance, or the stepsize, |
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133 ## @tex |
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134 ## $\Delta x,$ |
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135 ## @end tex |
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136 ## @ifnottex |
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137 ## delta @var{x}, |
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138 ## @end ifnottex |
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139 ## is less than @code{@var{tol} * norm (x)}. |
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140 ## |
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141 ## @item 102 |
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142 ## The BFGS update failed. |
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143 ## |
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144 ## @item 103 |
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145 ## The maximum number of iterations was reached. |
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146 ## @end table |
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147 ## |
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148 ## An example of calling @code{sqp}: |
5289 | 149 ## |
150 ## @example | |
7031 | 151 ## function r = g (x) |
152 ## r = [ sumsq(x)-10; | |
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153 ## x(2)*x(3)-5*x(4)*x(5); |
7031 | 154 ## x(1)^3+x(2)^3+1 ]; |
155 ## endfunction | |
156 ## | |
157 ## function obj = phi (x) | |
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158 ## obj = exp (prod (x)) - 0.5*(x(1)^3+x(2)^3+1)^2; |
7031 | 159 ## endfunction |
5289 | 160 ## |
7031 | 161 ## x0 = [-1.8; 1.7; 1.9; -0.8; -0.8]; |
162 ## | |
163 ## [x, obj, info, iter, nf, lambda] = sqp (x0, @@phi, @@g, []) | |
5289 | 164 ## |
7031 | 165 ## x = |
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166 ## |
7031 | 167 ## -1.71714 |
168 ## 1.59571 | |
169 ## 1.82725 | |
170 ## -0.76364 | |
171 ## -0.76364 | |
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172 ## |
7031 | 173 ## obj = 0.053950 |
174 ## info = 101 | |
175 ## iter = 8 | |
176 ## nf = 10 | |
177 ## lambda = | |
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178 ## |
7031 | 179 ## -0.0401627 |
180 ## 0.0379578 | |
181 ## -0.0052227 | |
5289 | 182 ## @end example |
183 ## | |
5642 | 184 ## @seealso{qp} |
5289 | 185 ## @end deftypefn |
186 | |
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187 function [x, obj, info, iter, nf, lambda] = sqp (x0, objf, cef, cif, lb, ub, maxiter, tolerance) |
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189 globals = struct (); # data and handles, needed and changed by |
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190 # subfunctions |
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192 if (nargin < 2 || nargin > 8 || nargin == 5) |
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193 print_usage (); |
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194 endif |
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196 if (!isvector (x0)) |
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197 error ("sqp: X0 must be a vector"); |
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198 endif |
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199 if (rows (x0) == 1) |
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200 x0 = x0'; |
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201 endif |
5289 | 202 |
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203 have_hess = 0; |
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204 if (iscell (objf)) |
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205 switch (numel (objf)) |
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206 case 1 |
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207 obj_fun = objf{1}; |
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208 obj_grd = @ (x) fd_obj_grd (x, obj_fun); |
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209 case 2 |
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210 obj_fun = objf{1}; |
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211 obj_grd = objf{2}; |
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212 case 3 |
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213 obj_fun = objf{1}; |
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214 obj_grd = objf{2}; |
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215 obj_hess = objf{3}; |
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216 have_hess = 1; |
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217 otherwise |
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218 error ("sqp: invalid objective function specification"); |
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219 endswitch |
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220 else |
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221 obj_fun = objf; # No cell array, only obj_fun set |
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222 obj_grd = @ (x) fd_obj_grd (x, obj_fun); |
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223 endif |
5289 | 224 |
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225 ce_fun = @empty_cf; |
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226 ce_grd = @empty_jac; |
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227 if (nargin > 2) |
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228 if (iscell (cef)) |
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229 switch (numel (cef)) |
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230 case 1 |
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231 ce_fun = cef{1}; |
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232 ce_grd = @ (x) fd_ce_jac (x, ce_fun); |
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233 case 2 |
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234 ce_fun = cef{1}; |
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235 ce_grd = cef{2}; |
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236 otherwise |
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237 error ("sqp: invalid equality constraint function specification"); |
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238 endswitch |
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239 elseif (! isempty (cef)) |
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240 ce_fun = cef; # No cell array, only constraint equality function set |
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241 ce_grd = @ (x) fd_ce_jac (x, ce_fun); |
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242 endif |
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243 endif |
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244 |
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245 ci_fun = @empty_cf; |
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246 ci_grd = @empty_jac; |
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247 if (nargin > 3) |
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248 ## constraint function given by user with possible gradient |
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249 globals.cif = cif; |
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250 ## constraint function given by user without gradient |
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251 globals.cifcn = @empty_cf; |
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252 if (iscell (cif)) |
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253 if (length (cif) > 0) |
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254 globals.cifcn = cif{1}; |
5289 | 255 endif |
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256 elseif (! isempty (cif)) |
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257 globals.cifcn = cif; |
5289 | 258 endif |
259 | |
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260 if (nargin < 5 || (nargin > 5 && isempty (lb) && isempty (ub))) |
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261 ## constraint inequality function only without any bounds |
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262 ci_grd = @ (x) fd_ci_jac (x, globals.cifcn); |
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263 if (iscell (cif)) |
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264 switch length (cif) |
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265 case {1} |
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266 ci_fun = cif{1}; |
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267 case {2} |
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268 ci_fun = cif{1}; |
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269 ci_grd = cif{2}; |
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270 otherwise |
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271 error ("sqp: invalid inequality constraint function specification"); |
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272 endswitch |
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273 elseif (! isempty (cif)) |
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274 ci_fun = cif; # No cell array, only constraint inequality function set |
5289 | 275 endif |
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276 else |
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277 ## constraint inequality function with bounds present |
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278 lb_idx = ub_idx = true (size (x0)); |
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279 ub_grad = - (lb_grad = eye (rows (x0))); |
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280 if (isvector (lb)) |
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281 globals.lb = tmp_lb = lb(:); |
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282 lb_idx(:) = tmp_idx = (lb != -Inf); |
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283 globals.lb = globals.lb(tmp_idx, 1); |
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284 lb_grad = lb_grad(lb_idx, :); |
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285 elseif (isempty (lb)) |
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286 if (isa (x0, "single")) |
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287 globals.lb = tmp_lb = -realmax ("single"); |
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288 else |
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289 globals.lb = tmp_lb = -realmax; |
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291 else |
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292 error ("sqp: invalid lower bound"); |
6768 | 293 endif |
294 | |
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295 if (isvector (ub)) |
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296 globals.ub = tmp_ub = ub(:); |
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297 ub_idx(:) = tmp_idx = (ub != Inf); |
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298 globals.ub = globals.ub(tmp_idx, 1); |
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299 ub_grad = ub_grad(ub_idx, :); |
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300 elseif (isempty (ub)) |
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301 if (isa (x0, "single")) |
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302 globals.ub = tmp_ub = realmax ("single"); |
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303 else |
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304 globals.ub = tmp_ub = realmax; |
10549 | 305 endif |
6768 | 306 else |
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307 error ("sqp: invalid upper bound"); |
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308 endif |
6768 | 309 |
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310 if (any (tmp_lb > tmp_ub)) |
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311 error ("sqp: upper bound smaller than lower bound"); |
6768 | 312 endif |
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313 bounds_grad = [lb_grad; ub_grad]; |
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314 ci_fun = @ (x) cf_ub_lb (x, lb_idx, ub_idx, globals); |
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315 ci_grd = @ (x) cigrad_ub_lb (x, bounds_grad, globals); |
6768 | 316 endif |
5289 | 317 |
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318 endif # if (nargin > 3) |
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319 |
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320 iter_max = 100; |
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321 if (nargin > 6 && ! isempty (maxiter)) |
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322 if (isscalar (maxiter) && maxiter > 0 && fix (maxiter) == maxiter) |
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323 iter_max = maxiter; |
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324 else |
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325 error ("sqp: invalid number of maximum iterations"); |
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326 endif |
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327 endif |
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328 |
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329 tol = sqrt (eps); |
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330 if (nargin > 7 && ! isempty (tolerance)) |
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331 if (isscalar (tolerance) && tolerance > 0) |
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332 tol = tolerance; |
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333 else |
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334 error ("sqp: invalid value for TOLERANCE"); |
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335 endif |
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336 endif |
5289 | 337 |
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338 ## Initialize variables for search loop |
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339 ## Seed x with initial guess and evaluate objective function, constraints, |
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340 ## and gradients at initial value x0. |
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341 ## |
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342 ## obj_fun -- objective function |
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343 ## obj_grad -- objective gradient |
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344 ## ce_fun -- equality constraint functions |
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345 ## ci_fun -- inequality constraint functions |
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346 ## A == [grad_{x_1} cx_fun, grad_{x_2} cx_fun, ..., grad_{x_n} cx_fun]^T |
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347 x = x0; |
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348 |
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349 obj = feval (obj_fun, x0); |
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350 globals.nfun = 1; |
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351 |
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352 c = feval (obj_grd, x0); |
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353 |
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354 ## Choose an initial NxN symmetric positive definite Hessian approximation B. |
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355 n = length (x0); |
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356 if (have_hess) |
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357 B = feval (obj_hess, x0); |
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358 else |
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359 B = eye (n, n); |
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360 endif |
5289 | 361 |
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362 ce = feval (ce_fun, x0); |
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363 F = feval (ce_grd, x0); |
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364 |
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365 ci = feval (ci_fun, x0); |
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366 C = feval (ci_grd, x0); |
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367 |
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368 A = [F; C]; |
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369 |
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370 ## Choose an initial lambda (x is provided by the caller). |
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371 lambda = 100 * ones (rows (A), 1); |
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372 |
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373 qp_iter = 1; |
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374 alpha = 1; |
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375 |
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376 info = 0; |
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377 iter = 0; |
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378 # report (); # Called with no arguments to initialize reporting |
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379 # report (iter, qp_iter, alpha, __sqp_nfun__, obj); |
5289 | 380 |
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381 while (++iter < iter_max) |
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382 |
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383 ## Check convergence. This is just a simple check on the first |
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384 ## order necessary conditions. |
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385 nr_f = rows (F); |
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386 |
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387 lambda_e = lambda((1:nr_f)'); |
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388 lambda_i = lambda((nr_f+1:end)'); |
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389 |
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390 con = [ce; ci]; |
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391 |
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392 t0 = norm (c - A' * lambda); |
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393 t1 = norm (ce); |
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394 t2 = all (ci >= 0); |
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395 t3 = all (lambda_i >= 0); |
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396 t4 = norm (lambda .* con); |
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397 |
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398 if (t2 && t3 && max ([t0; t1; t4]) < tol) |
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399 info = 101; |
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400 break; |
6382 | 401 endif |
402 | |
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403 ## Compute search direction p by solving QP. |
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404 g = -ce; |
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405 d = -ci; |
5289 | 406 |
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407 [p, obj_qp, INFO, lambda] = qp (x, B, c, F, g, [], [], d, C, |
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408 Inf (size (d))); |
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409 |
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410 info = INFO.info; |
5289 | 411 |
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412 ## FIXME -- check QP solution and attempt to recover if it has |
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413 ## failed. For now, just warn about possible problems. |
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414 |
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415 id = "Octave:SQP-QP-subproblem"; |
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416 switch (info) |
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417 case 2 |
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418 warning (id, "sqp: QP subproblem is non-convex and unbounded"); |
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419 case 3 |
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420 warning (id, "sqp: QP subproblem failed to converge in %d iterations", |
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421 INFO.solveiter); |
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422 case 6 |
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423 warning (id, "sqp: QP subproblem is infeasible"); |
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424 endswitch |
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425 |
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426 ## Choose mu such that p is a descent direction for the chosen |
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427 ## merit function phi. |
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428 [x_new, alpha, obj_new, globals] = \ |
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429 linesearch_L1 (x, p, obj_fun, obj_grd, ce_fun, ci_fun, lambda, \ |
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430 obj, globals); |
5289 | 431 |
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432 ## Evaluate objective function, constraints, and gradients at x_new. |
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433 c_new = feval (obj_grd, x_new); |
5289 | 434 |
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435 ce_new = feval (ce_fun, x_new); |
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436 F_new = feval (ce_grd, x_new); |
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437 |
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438 ci_new = feval (ci_fun, x_new); |
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439 C_new = feval (ci_grd, x_new); |
5289 | 440 |
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441 A_new = [F_new; C_new]; |
5289 | 442 |
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443 ## Set |
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444 ## |
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445 ## s = alpha * p |
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446 ## y = grad_x L (x_new, lambda) - grad_x L (x, lambda}) |
6527 | 447 |
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448 y = c_new - c; |
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449 |
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450 if (! isempty (A)) |
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451 t = ((A_new - A)'*lambda); |
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452 y -= t; |
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453 endif |
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454 |
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455 delx = x_new - x; |
5289 | 456 |
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457 if (norm (delx) < tol * norm (x)) |
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458 info = 101; |
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459 break; |
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460 endif |
5289 | 461 |
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462 if (have_hess) |
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463 |
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464 B = feval (obj_hess, x); |
5289 | 465 |
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466 else |
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467 ## Update B using a quasi-Newton formula. |
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468 delxt = delx'; |
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469 |
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470 ## Damped BFGS. Or maybe we would actually want to use the Hessian |
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471 ## of the Lagrangian, computed directly? |
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472 d1 = delxt*B*delx; |
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473 |
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474 t1 = 0.2 * d1; |
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475 t2 = delxt*y; |
5289 | 476 |
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477 if (t2 < t1) |
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478 theta = 0.8*d1/(d1 - t2); |
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479 else |
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480 theta = 1; |
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481 endif |
5289 | 482 |
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483 r = theta*y + (1-theta)*B*delx; |
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484 |
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485 d2 = delxt*r; |
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486 |
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487 if (d1 == 0 || d2 == 0) |
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488 info = 102; |
10549 | 489 break; |
5289 | 490 endif |
491 | |
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492 B = B - B*delx*delxt*B/d1 + r*r'/d2; |
5289 | 493 |
494 endif | |
495 | |
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496 x = x_new; |
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497 |
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498 obj = obj_new; |
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499 |
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500 c = c_new; |
5289 | 501 |
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502 ce = ce_new; |
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503 F = F_new; |
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504 |
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505 ci = ci_new; |
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506 C = C_new; |
5289 | 507 |
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508 A = A_new; |
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509 |
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510 # report (iter, qp_iter, alpha, __sqp_nfun__, obj); |
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511 |
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512 endwhile |
5289 | 513 |
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514 if (iter >= iter_max) |
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515 info = 103; |
5289 | 516 endif |
517 | |
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518 nf = globals.nfun; |
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519 |
7399 | 520 endfunction |
5289 | 521 |
522 | |
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523 function [merit, obj, globals] = phi_L1 (obj, obj_fun, ce_fun, ci_fun, \ |
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524 x, mu, globals) |
5289 | 525 |
526 ce = feval (ce_fun, x); | |
527 ci = feval (ci_fun, x); | |
528 | |
529 idx = ci < 0; | |
530 | |
531 con = [ce; ci(idx)]; | |
532 | |
533 if (isempty (obj)) | |
534 obj = feval (obj_fun, x); | |
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535 globals.nfun++; |
5289 | 536 endif |
537 | |
538 merit = obj; | |
539 t = norm (con, 1) / mu; | |
540 | |
541 if (! isempty (t)) | |
542 merit += t; | |
543 endif | |
544 | |
7399 | 545 endfunction |
5289 | 546 |
547 | |
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548 function [x_new, alpha, obj, globals] = \ |
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549 linesearch_L1 (x, p, obj_fun, obj_grd, ce_fun, ci_fun, lambda, \ |
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550 obj, globals) |
5289 | 551 |
552 ## Choose parameters | |
553 ## | |
554 ## eta in the range (0, 0.5) | |
555 ## tau in the range (0, 1) | |
556 | |
557 eta = 0.25; | |
558 tau = 0.5; | |
559 | |
560 delta_bar = sqrt (eps); | |
561 | |
562 if (isempty (lambda)) | |
563 mu = 1 / delta_bar; | |
564 else | |
565 mu = 1 / (norm (lambda, Inf) + delta_bar); | |
566 endif | |
567 | |
568 alpha = 1; | |
569 | |
570 c = feval (obj_grd, x); | |
571 ce = feval (ce_fun, x); | |
572 | |
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573 [phi_x_mu, obj, globals] = phi_L1 (obj, obj_fun, ce_fun, ci_fun, x, \ |
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574 mu, globals); |
5289 | 575 |
576 D_phi_x_mu = c' * p; | |
577 d = feval (ci_fun, x); | |
578 ## only those elements of d corresponding | |
579 ## to violated constraints should be included. | |
580 idx = d < 0; | |
581 t = - norm ([ce; d(idx)], 1) / mu; | |
582 if (! isempty (t)) | |
583 D_phi_x_mu += t; | |
584 endif | |
585 | |
586 while (1) | |
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587 [p1, obj, globals] = phi_L1 ([], obj_fun, ce_fun, ci_fun, \ |
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588 x+alpha*p, mu, globals); |
5289 | 589 p2 = phi_x_mu+eta*alpha*D_phi_x_mu; |
590 if (p1 > p2) | |
591 ## Reset alpha = tau_alpha * alpha for some tau_alpha in the | |
592 ## range (0, tau). | |
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593 tau_alpha = 0.9 * tau; # ?? |
5289 | 594 alpha = tau_alpha * alpha; |
595 else | |
596 break; | |
597 endif | |
598 endwhile | |
599 | |
600 x_new = x + alpha * p; | |
601 | |
7399 | 602 endfunction |
5289 | 603 |
604 | |
605 function grd = fdgrd (f, x) | |
606 | |
607 if (! isempty (f)) | |
608 y0 = feval (f, x); | |
609 nx = length (x); | |
610 grd = zeros (nx, 1); | |
611 deltax = sqrt (eps); | |
612 for i = 1:nx | |
613 t = x(i); | |
614 x(i) += deltax; | |
615 grd(i) = (feval (f, x) - y0) / deltax; | |
616 x(i) = t; | |
617 endfor | |
618 else | |
619 grd = zeros (0, 1); | |
620 endif | |
621 | |
7399 | 622 endfunction |
5289 | 623 |
624 | |
625 function jac = fdjac (f, x) | |
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626 |
6768 | 627 nx = length (x); |
5289 | 628 if (! isempty (f)) |
629 y0 = feval (f, x); | |
630 nf = length (y0); | |
631 nx = length (x); | |
632 jac = zeros (nf, nx); | |
633 deltax = sqrt (eps); | |
634 for i = 1:nx | |
635 t = x(i); | |
636 x(i) += deltax; | |
637 jac(:,i) = (feval (f, x) - y0) / deltax; | |
638 x(i) = t; | |
639 endfor | |
640 else | |
641 jac = zeros (0, nx); | |
642 endif | |
643 | |
7399 | 644 endfunction |
5289 | 645 |
646 | |
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647 function grd = fd_obj_grd (x, obj_fun) |
5289 | 648 |
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649 grd = fdgrd (obj_fun, x); |
5289 | 650 |
7399 | 651 endfunction |
5289 | 652 |
653 | |
654 function res = empty_cf (x) | |
655 | |
656 res = zeros (0, 1); | |
657 | |
7399 | 658 endfunction |
5289 | 659 |
660 | |
661 function res = empty_jac (x) | |
662 | |
663 res = zeros (0, length (x)); | |
664 | |
7399 | 665 endfunction |
5289 | 666 |
667 | |
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668 function jac = fd_ce_jac (x, ce_fun) |
5289 | 669 |
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670 jac = fdjac (ce_fun, x); |
5289 | 671 |
7399 | 672 endfunction |
5289 | 673 |
674 | |
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675 function jac = fd_ci_jac (x, cifcn) |
5289 | 676 |
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677 ## cifcn = constraint function without gradients and lb or ub |
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678 jac = fdjac (cifcn, x); |
6768 | 679 |
7399 | 680 endfunction |
6768 | 681 |
7017 | 682 |
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683 function res = cf_ub_lb (x, lbidx, ubidx, globals) |
5289 | 684 |
6768 | 685 ## combine constraint function with ub and lb |
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686 if (isempty (globals.cifcn)) |
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687 res = [x(lbidx,1)-globals.lb; globals.ub-x(ubidx,1)]; |
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688 else |
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689 res = [feval(globals.cifcn,x); x(lbidx,1)-globals.lb; |
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690 globals.ub-x(ubidx,1)]; |
6768 | 691 endif |
5289 | 692 |
7399 | 693 endfunction |
6768 | 694 |
7017 | 695 |
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696 function res = cigrad_ub_lb (x, bgrad, globals) |
6768 | 697 |
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698 cigradfcn = @ (x) fd_ci_jac (x, globals.cifcn); |
6768 | 699 |
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700 if (iscell (globals.cif) && length (globals.cif) > 1) |
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701 cigradfcn = globals.cif{2}; |
6768 | 702 endif |
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703 |
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704 if (isempty (cigradfcn)) |
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705 res = bgrad; |
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706 else |
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707 res = [feval(cigradfcn,x); bgrad]; |
6768 | 708 endif |
709 | |
7399 | 710 endfunction |
7361 | 711 |
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712 # Utility function used to debug sqp |
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713 function report (iter, qp_iter, alpha, nfun, obj) |
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714 |
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715 if (nargin == 0) |
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716 printf (" Itn ItQP Step Nfun Objective\n"); |
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717 else |
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718 printf ("%5d %4d %8.1g %5d %13.6e\n", iter, qp_iter, alpha, nfun, obj); |
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719 endif |
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720 |
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721 endfunction |
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722 |
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723 |
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724 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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725 %% Test Code |
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726 |
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727 %!function r = __g (x) |
7371 | 728 %! r = [sumsq(x)-10; |
729 %! x(2)*x(3)-5*x(4)*x(5); | |
730 %! x(1)^3+x(2)^3+1 ]; | |
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731 %!endfunction |
7361 | 732 %! |
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733 %!function obj = __phi (x) |
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734 %! obj = exp (prod (x)) - 0.5*(x(1)^3 + x(2)^3 + 1)^2; |
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735 %!endfunction |
7361 | 736 %! |
737 %!test | |
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738 %! |
7361 | 739 %! x0 = [-1.8; 1.7; 1.9; -0.8; -0.8]; |
740 %! | |
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741 %! [x, obj, info, iter, nf, lambda] = sqp (x0, @__phi, @__g, []); |
7361 | 742 %! |
743 %! x_opt = [-1.717143501952599; | |
744 %! 1.595709610928535; | |
745 %! 1.827245880097156; | |
746 %! -0.763643103133572; | |
747 %! -0.763643068453300]; | |
748 %! | |
7371 | 749 %! obj_opt = 0.0539498477702739; |
7361 | 750 %! |
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751 %! assert (x, x_opt, 5*sqrt (eps)); |
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752 %! assert (obj, obj_opt, sqrt (eps)); |
10678
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|
753 |
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754 %% Test input validation |
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755 %!error sqp () |
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756 %!error sqp (1) |
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757 %!error sqp (1,2,3,4,5,6,7,8,9) |
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758 %!error sqp (1,2,3,4,5) |
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759 %!error sqp (ones (2,2)) |
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760 %!error sqp (1, cell (4,1)) |
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761 %!error sqp (1, cell (3,1), cell (3,1)) |
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762 %!error sqp (1, cell (3,1), cell (2,1), cell (3,1)) |
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763 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1), ones (2,2),[]) |
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764 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[], ones (2,2)) |
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765 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),1,-1) |
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|
766 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[], ones (2,2)) |
f3d52523cde1
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|
767 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],-1) |
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|
768 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],1.5) |
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769 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],[], ones (2,2)) |
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|
770 %!error sqp (1, cell (3,1), cell (2,1), cell (2,1),[],[],[],-1) |