1 /* Copyright (c) 2007-2014 Massachusetts Institute of Technology
3 * Permission is hereby granted, free of charge, to any person obtaining
4 * a copy of this software and associated documentation files (the
5 * "Software"), to deal in the Software without restriction, including
6 * without limitation the rights to use, copy, modify, merge, publish,
7 * distribute, sublicense, and/or sell copies of the Software, and to
8 * permit persons to whom the Software is furnished to do so, subject to
9 * the following conditions:
11 * The above copyright notice and this permission notice shall be
12 * included in all copies or substantial portions of the Software.
14 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
15 * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
16 * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
17 * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
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20 * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
23 #include <octave/oct.h>
24 #include <octave/oct-map.h>
25 #include <octave/ov.h>
30 #include "nlopt_optimize_usage.h"
32 #include <octave/version.h>
33 #if OCTAVE_MAJOR_VERSION < 3 || (OCTAVE_MAJOR_VERSION == 3 && OCTAVE_MINOR_VERSION < 8)
34 # define octave_map Octave_map
37 static int struct_val_default(octave_map &m, const std::string& k,
41 if (m.contents(k).length() == 1 && (m.contents(k))(0).is_real_scalar())
42 return (m.contents(k))(0).int_value();
47 static double struct_val_default(octave_map &m, const std::string& k,
51 if (m.contents(k).length() == 1 && (m.contents(k))(0).is_real_scalar())
52 return (m.contents(k))(0).double_value();
57 static Matrix struct_val_default(octave_map &m, const std::string& k,
61 if ((m.contents(k)).length() == 1) {
62 if ((m.contents(k))(0).is_real_scalar())
63 return Matrix(1, dflt.length(), (m.contents(k))(0).double_value());
64 else if ((m.contents(k))(0).is_real_matrix())
65 return (m.contents(k))(0).matrix_value();
77 static double user_function(unsigned n, const double *x,
78 double *gradient, /* NULL if not needed */
81 user_function_data *data = (user_function_data *) data_;
82 octave_value_list args(1, 0);
84 for (unsigned i = 0; i < n; ++i)
87 octave_value_list res = data->f->do_multi_index_op(gradient ? 2 : 1, args);
88 if (res.length() < (gradient ? 2 : 1))
89 gripe_user_supplied_eval("nlopt_optimize");
90 else if (!res(0).is_real_scalar()
91 || (gradient && !res(1).is_real_matrix()
92 && !(n == 1 && res(1).is_real_scalar())))
93 gripe_user_returned_invalid("nlopt_optimize");
96 if (n == 1 && res(1).is_real_scalar())
97 gradient[0] = res(1).double_value();
99 Matrix grad = res(1).matrix_value();
100 for (unsigned i = 0; i < n; ++i)
101 gradient[i] = grad(i);
105 if (data->verbose) printf("nlopt_optimize eval #%d: %g\n",
106 data->neval, res(0).double_value());
107 double f = res(0).double_value();
108 if (f != f /* isnan(f) */) nlopt_force_stop(data->opt);
114 static double user_function1(unsigned n, const double *x,
115 double *gradient, /* NULL if not needed */
118 octave_function *f = (octave_function *) data_;
119 octave_value_list args(1, 0);
121 for (unsigned i = 0; i < n; ++i)
124 octave_value_list res = f->do_multi_index_op(gradient ? 2 : 1, args);
125 if (res.length() < (gradient ? 2 : 1))
126 gripe_user_supplied_eval("nlopt_optimize");
127 else if (!res(0).is_real_scalar()
128 || (gradient && !res(1).is_real_matrix()
129 && !(n == 1 && res(1).is_real_scalar())))
130 gripe_user_returned_invalid("nlopt_optimize");
133 if (n == 1 && res(1).is_real_scalar())
134 gradient[0] = res(1).double_value();
136 Matrix grad = res(1).matrix_value();
137 for (unsigned i = 0; i < n; ++i)
138 gradient[i] = grad(i);
141 return res(0).double_value();
146 #define CHECK1(cond, msg) if (!(cond)) { fprintf(stderr, msg "\n\n"); nlopt_destroy(opt); nlopt_destroy(local_opt); return NULL; }
148 nlopt_opt make_opt(octave_map &opts, int n)
150 nlopt_opt opt = NULL, local_opt = NULL;
152 nlopt_algorithm algorithm =
153 nlopt_algorithm(struct_val_default(opts, "algorithm",
154 NLOPT_NUM_ALGORITHMS));
155 CHECK1(((int)algorithm) >= 0 && algorithm < NLOPT_NUM_ALGORITHMS,
156 "invalid opt.algorithm");
158 opt = nlopt_create(algorithm, n);
159 CHECK1(opt, "nlopt: out of memory");
161 Matrix m_inf(1, n, -HUGE_VAL);
162 Matrix lb = struct_val_default(opts, "lower_bounds", m_inf);
163 CHECK1(n == lb.length(), "wrong length of opt.lower_bounds");
164 CHECK1(nlopt_set_lower_bounds(opt, lb.data()) > 0, "nlopt: out of memory");
166 Matrix p_inf(1, n, +HUGE_VAL);
167 Matrix ub = struct_val_default(opts, "upper_bounds", p_inf);
168 CHECK1(n == ub.length(), "wrong length of opt.upper_bounds");
169 CHECK1(nlopt_set_upper_bounds(opt, ub.data()) > 0, "nlopt: out of memory");
171 nlopt_set_stopval(opt, struct_val_default(opts, "stopval", -HUGE_VAL));
172 nlopt_set_ftol_rel(opt, struct_val_default(opts, "ftol_rel", 0.0));
173 nlopt_set_ftol_abs(opt, struct_val_default(opts, "ftol_abs", 0.0));
174 nlopt_set_xtol_rel(opt, struct_val_default(opts, "xtol_rel", 0.0));
177 Matrix zeros(1, n, 0.0);
178 Matrix xtol_abs = struct_val_default(opts, "xtol_abs", zeros);
179 CHECK1(n == xtol_abs.length(), "stop.xtol_abs must have same length as x");
180 CHECK1(nlopt_set_xtol_abs(opt, xtol_abs.data())>0, "nlopt: out of memory");
183 nlopt_set_maxeval(opt, struct_val_default(opts, "maxeval", 0) < 0 ?
184 0 : struct_val_default(opts, "maxeval", 0));
185 nlopt_set_maxtime(opt, struct_val_default(opts, "maxtime", 0.0));
187 nlopt_set_population(opt, struct_val_default(opts, "population", 0));
188 nlopt_set_vector_storage(opt, struct_val_default(opts, "vector_storage", 0));
190 if (opts.contains("initial_step")) {
191 Matrix zeros(1, n, 0.0);
192 Matrix initial_step = struct_val_default(opts, "initial_step", zeros);
193 CHECK1(n == initial_step.length(),
194 "stop.initial_step must have same length as x");
195 CHECK1(nlopt_set_initial_step(opt, initial_step.data()) > 0,
196 "nlopt: out of memory");
199 if (opts.contains("local_optimizer")) {
200 CHECK1(opts.contents("local_optimizer").length() == 1
201 && (opts.contents("local_optimizer"))(0).is_map(),
202 "opt.local_optimizer must be a structure");
203 octave_map local_opts = (opts.contents("local_optimizer"))(0).map_value();
204 CHECK1((local_opt = make_opt(local_opts, n)),
205 "error initializing local optimizer");
206 nlopt_set_local_optimizer(opt, local_opt);
207 nlopt_destroy(local_opt); local_opt = NULL;
213 #define CHECK(cond, msg) if (!(cond)) { fprintf(stderr, msg "\n\n"); nlopt_destroy(opt); return retval; }
215 DEFUN_DLD(nlopt_optimize, args, nargout, NLOPT_OPTIMIZE_USAGE)
217 octave_value_list retval;
219 nlopt_opt opt = NULL;
221 CHECK(args.length() == 2 && nargout <= 3, "wrong number of args");
223 CHECK(args(0).is_map(), "opt must be structure")
224 octave_map opts = args(0).map_value();
226 CHECK(args(1).is_real_matrix() || args(1).is_real_scalar(),
227 "x must be real vector");
228 Matrix x = args(1).is_real_scalar() ?
229 Matrix(1, 1, args(1).double_value()) : args(1).matrix_value();
232 CHECK((opt = make_opt(opts, n)), "error initializing nlopt options");
234 user_function_data d;
236 d.verbose = struct_val_default(opts, "verbose", 0);
238 if (opts.contains("min_objective")) {
239 CHECK(opts.contents("min_objective").length() == 1
240 && (opts.contents("min_objective"))(0).is_function_handle(),
241 "opt.min_objective must be a function");
242 d.f = (opts.contents("min_objective"))(0).function_value();
243 nlopt_set_min_objective(opt, user_function, &d);
245 else if (opts.contains("max_objective")) {
246 CHECK(opts.contents("max_objective").length() == 1
247 && (opts.contents("max_objective"))(0).is_function_handle(),
248 "opt.max_objective must be a function");
249 d.f = (opts.contents("max_objective"))(0).function_value();
250 nlopt_set_max_objective(opt, user_function, &d);
253 CHECK(0,"either opt.min_objective or opt.max_objective must exist");
256 if (opts.contains("fc") && opts.contents("fc").length() == 1) {
257 CHECK((opts.contents("fc"))(0).is_cell(), "opt.fc must be cell array");
258 Cell fc = (opts.contents("fc"))(0).cell_value();
259 Matrix zeros(1, fc.length(), 0.0);
260 Matrix fc_tol = struct_val_default(opts, "fc_tol", zeros);
261 CHECK(fc_tol.length() == fc.length(),
262 "opt.fc must have same length as opt.fc_tol");
263 for (int i = 0; i < fc.length(); ++i) {
264 CHECK(fc(i).is_function() || fc(i).is_function_handle(),
265 "opt.fc must be a cell array of function handles");
266 CHECK(nlopt_add_inequality_constraint(opt, user_function1,
267 fc(i).function_value(),
269 "nlopt error adding inequality constraint");
273 if (opts.contains("h") && opts.contents("h").length() == 1) {
274 CHECK((opts.contents("h"))(0).is_cell(), "opt.h must be cell array");
275 Cell h = (opts.contents("h"))(0).cell_value();
276 Matrix zeros(1, h.length(), 0.0);
277 Matrix h_tol = struct_val_default(opts, "h_tol", zeros);
278 CHECK(h_tol.length() == h.length(),
279 "opt.h must have same length as opt.h_tol");
280 for (int i = 0; i < h.length(); ++i) {
281 CHECK(h(i).is_function() || h(i).is_function_handle(),
282 "opt.h must be a cell array of function handles");
283 CHECK(nlopt_add_equality_constraint(opt, user_function1,
284 h(i).function_value(),
286 "nlopt error adding equality constraint");
292 nlopt_result ret = nlopt_optimize(opt, x.fortran_vec(), &opt_f);
298 retval(2) = int(ret);