5 In this tutorial, we illustrate the usage of NLopt in various languages via one or two trivial examples.
7 Example nonlinearly constrained problem
8 ---------------------------------------
11 ![right|thumb|400px|Feasible region for a simple example optimization problem with two nonlinear (cubic) constraints.](images/NLopt-example-constraints.png)
13 As a first example, we'll look at the following simple nonlinearly constrained minimization problem:
15 $$\min_{\mathbf{x}\in\mathbb{R}^2} \sqrt{x_2}$$
18 subject to $x_2 \geq 0$, $x_2 \geq (a_1 x_1 + b_1)^3$, and $x_2 \geq (a_2 x_1 + b_2)^3$
20 for parameters *a*<sub>1</sub>=2, *b*<sub>1</sub>=0, *a*<sub>2</sub>=-1, *b*<sub>2</sub>=1.
22 The feasible region defined by these constraints is plotted at right: *x*<sub>2</sub> is constrained to lie above the maximum of two cubics, and the optimum point is located at the intersection (1/3, 8/27) where the objective function takes on the value $\sqrt{8/27} \approx 0.5443310539518\ldots$.
24 (This problem is especially trivial, because by formulating it in terms of the cube root of *x*<sub>2</sub> you can turn it into a linear-programming problem, but we won't do that here.)
26 In principle, we don't need the bound constraint *x*<sub>2</sub>≥0, since the nonlinear constraints already imply a positive-*x*<sub>2</sub> feasible region. However, NLopt doesn't guarantee that, on the way to finding the optimum, it won't violate the nonlinear constraints at some intermediate steps, while it *does* guarantee that all intermediate steps will satisfy the bound constraints. So, we will explicitly impose *x*<sub>2</sub>≥0 in order to ensure that the √*x*<sub>2</sub> in our objective is real.
28 **Note:** The objective function here is not differentiable at *x*<sub>2</sub>=0. This doesn't cause problems in the examples below, but may cause problems with some other algorithms if they try to evaluate the gradient at *x*<sub>2</sub>=0 (e.g. I've seen it cause AUGLAG with a gradient-based solver to fail). To prevent this, you might want to use a small nonzero lower bound instead, e.g. *x*<sub>2</sub>≥10<sup>−6</sup>.
33 To implement the above example in C or C++, we would first do:
40 to include the NLopt header file as well as the standard math header file (needed for things like the `sqrt` function and the `HUGE_VAL` constant), then we would define our objective function as:
43 double myfunc(unsigned n, const double *x, double *grad, void *my_func_data)
47 grad[1] = 0.5 / sqrt(x[1]);
54 There are several things to notice here. First, since this is C, our indices are zero-based, so we have `x[0]` and `x[1]` instead of *x*<sub>1</sub> and *x*<sub>2</sub>. The return value of our function is the objective $\sqrt{x_2}$. Also, if the parameter `grad` is not `NULL`, then we set `grad[0]` and `grad[1]` to the partial derivatives of our objective with respect to `x[0]` and `x[1]`. The gradient is only needed for [gradient-based algorithms](NLopt_Introduction#Gradient-based_versus_derivative-free_algorithms.md); if you use a derivative-free optimization algorithm, `grad` will always be `NULL` and you need never compute any derivatives. Finally, we have an extra parameter `my_func_data` that can be used to pass additional data to `myfunc`, but no additional data is needed here so that parameter is unused.
56 For the constraints, on the other hand, we *will* have additional data. Each constraint is parameterized by two numbers *a* and *b*, so we will declare a data structure to hold this information:
65 Then, we implement our constraint function as follows.
68 double myconstraint(unsigned n, const double *x, double *grad, void *data)
70 my_constraint_data *d = (my_constraint_data *) data;
71 double a = d->a, b = d->b;
73 grad[0] = 3 * a * (a*x[0] + b) * (a*x[0] + b);
76 return ((a*x[0] + b) * (a*x[0] + b) * (a*x[0] + b) - x[1]);
81 The form of the constraint function is the same as that of the objective function. Here, the `data` parameter will actually be a pointer to `my_constraint_data` (because this is the type that we will pass to `nlopt_minimize_constrained` below), so we use a typecast to get the constraint data. NLopt always expects constraints to be of the form `myconstraint`(**x**) ≤ 0, so we implement the constraint *x*<sub>2</sub> ≥ (*a* *x*<sub>1</sub> + *b*)<sup>3</sup> as the function (*a* *x*<sub>1</sub> + *b*)<sup>3</sup> − *x*<sub>2</sub>. Again, we only compute the gradient if `grad` is non-`NULL`, which will never occur if we use a derivative-free optimization algorithm.
83 Now, to specify this optimization problem, we create an "object" of type `nlopt_opt` (an opaque pointer type) and set its various parameters:
86 double lb[2] = { -HUGE_VAL, 0 }; /* lower bounds */
92 opt = nlopt_create(NLOPT_LD_MMA, 2); /* algorithm and dimensionality */
93 nlopt_set_lower_bounds(opt, lb);
94 nlopt_set_min_objective(opt, myfunc, NULL);
98 Note that we do not need to set an upper bound (`nlopt_set_upper_bounds`), since we are happy with the default upper bounds (+∞). To add the two inequality constraints, we do:
101 my_constraint_data data[2] = { {2,0}, {-1,1} };
106 nlopt_add_inequality_constraint(opt, myconstraint, &data[0], 1e-8);
107 nlopt_add_inequality_constraint(opt, myconstraint, &data[1], 1e-8);
111 Here, the `1e-8` is an optional tolerance for the constraint: for purposes of convergence testing, a point will be considered feasible if the constraint is violated (is positive) by that tolerance (10<sup>−8</sup>). A nonzero tolerance is a good idea for many algorithms lest tiny errors prevent convergence. Speaking of convergence tests, we should also set one or more stopping criteria, e.g. a relative tolerance on the optimization parameters **x**:
114 nlopt_set_xtol_rel(opt, 1e-4);
118 There are many more possible parameters that you can set to control the optimization, which are described in detail by the [reference manual](NLopt_Reference.md), but these are enough for our example here (any unspecified parameters are set to innocuous defaults). At this point, we can call nlopt_optimize to actually perform the optimization, starting with some initial guess:
121 double x[2] = { 1.234, 5.678 }; /* `*`some` `initial` `guess`*` */
122 double minf; /* `*`the` `minimum` `objective` `value,` `upon` `return`*` */
123 if (nlopt_optimize(opt, x, &minf) < 0) {
124 printf("nlopt failed!\n");
127 printf("found minimum at f(%g,%g) = %0.10g\n", x[0], x[1], minf);
132 `nlopt_optimize` will return a negative result code on failure, but this usually only happens if you pass invalid parameters, it runs out of memory, or something like that. (Actually, most of the other NLopt functions also return an error code that you can check if you are paranoid.) (However, if it returns the failure code `NLOPT_ROUNDOFF_LIMITED`, indicating a breakdown due to roundoff errors, the minimum found may still be useful and you may want to still use it.) Otherwise, we print out the minimum function value and the corresponding parameters **x**.
134 Finally, we should call nlopt_destroy to dispose of the `nlopt_opt` object when we are done with it:
141 Assuming we save this in a file tutorial.c, we would compile and link (on Unix) with:
144 cc tutorial.c -o tutorial -lnlopt -lm
148 The result of running the program should then be something like:
151 found minimum at f(0.333334,0.296296) = 0.544330847
155 That is, it found the correct parameters to about 5 significant digits and the correct minimum function value to about 6 significant digits. (This is better than we specified; this often occurs because the local optimization routines usually try to be conservative in estimating the error.)
157 ### Number of evaluations
159 Let's modify our program to print out the number of function evaluations that were required to obtain this result. First, we'll change our objective function to:
163 double myfunc(int n, const double *x, double *grad, void *my_func_data)
168 grad[1] = 0.5 / sqrt(x[1]);
175 using a global variable `count` that is incremented for each function evaluation. (We could also pass a pointer to a counter variable as `my_func_data`, if we wanted to avoid global variables.) Then, adding a `printf`:
178 printf("found minimum after %d evaluations\n", count);
185 found minimum after 11 evaluations
186 found minimum at f(0.333334,0.296296) = 0.544330847
190 For such a simple problem, a gradient-based local optimization algorithm like MMA can converge very quickly!
192 ### Switching to a derivative-free algorithm
194 We can also try a derivative-free algorithm. Looking at the [NLopt Algorithms](NLopt_Algorithms.md) list, another algorithm in NLopt that handles nonlinear constraints is COBYLA, which is derivative-free. To use it, we just change `NLOPT_LD_MMA` ("LD" means local optimization, derivative/gradient-based) into `NLOPT_LN_COBYLA` ("LN" means local optimization, no derivatives), and obtain:
197 found minimum after 31 evaluations
198 found minimum at f(0.333329,0.2962) = 0.544242301
202 In such a low-dimensional problem, derivative-free algorithms usually work quite well—in this case, it only triples the number of function evaluations. However, the comparison is not perfect because, for the same relative **x** tolerance of 10<sup>−4</sup>, COBYLA is a bit less conservative and only finds the solution to 3 significant digits.
204 To do a fairer comparison of the two algorithms, we could set the **x** tolerance to zero and ask how many function evaluations each one requires to get the correct answer to three decimal places. We can specify this by using the `stopval` termination criterion, which allows us to halt the process as soon as a feasible point attains an objective function value less than `stopval`. In this case, we would set `stopval` to $\sqrt(8/27)+10^{-3}$, replacing `nlopt_set_xtol_rel` with the statement:
207 nlopt_set_stopval(opt, sqrt(8./27.)+1e-3);
211 corresponding to the last line of arguments to `nlopt_minimize_constrained` being `sqrt(8./27.)+1e-3,` `0.0,` `0.0,` `0.0,` `NULL,` `0,` `0.0`. If we do this, we find that COBYLA requires 25 evaluations while MMA requires 10.
213 The advantage of gradient-based algorithms over derivative-free algorithms typically grows for higher-dimensional problems. On the other hand, derivative-free algorithms are much easier to use because you don't need to worry about how to compute the gradient (which might be tricky if the function is very complicated).
218 Although it is perfectly possible to use the C interface from C++, many C++ programmers will find it more natural to use real C++ objects instead of opaque `nlopt_opt` pointers, `std::vector`<double> instead of arrays, and exceptions instead of error codes. NLopt provides a C++ header file `nlopt.hpp` that you can use for this purpose, which simply wraps a C++ object interface around the C interface above.
220 `#include `<nlopt.hpp>
222 The equivalent of the above example would then be:
225 nlopt::opt opt(nlopt::LD_MMA, 2);
226 std::vector`<double>` lb(2);
227 lb[0] = -HUGE_VAL; lb[1] = 0;
228 opt.set_lower_bounds(lb);
229 opt.set_min_objective(myfunc, NULL);
230 my_constraint_data data[2] = { {2,0}, {-1,1} };
231 opt.add_inequality_constraint(myconstraint, &data[0], 1e-8);
232 opt.add_inequality_constraint(myconstraint, &data[1], 1e-8);
233 opt.set_xtol_rel(1e-4);
234 std::vector`<double>` x(2);
235 x[0] = 1.234; x[1] = 5.678;
237 nlopt::result result = opt.optimize(x, minf);
241 There is no need to deallocate the `opt` object; its destructor will do that for you once it goes out of scope. Also, there is no longer any need to check for error codes; the NLopt C++ functions will throw exceptions if there is an error, which you can `catch` normally.
243 Here, we are using the same objective and constraint functions as in C, taking `double*` array arguments. Alternatively, you can define objective and constraint functions to take `std::vector`<double> arguments if you prefer. (Using `std::vector`<double> in the objective/constraint imposes a slight overhead because NLopt must copy the `double*` data to a `std::vector`<double>, but this overhead is unlikely to be significant in most real applications.) That is, you would do:
246 double myvfunc(const std::vector`<double>` &x, std::vector`<double>` &grad, void *my_func_data)
250 grad[1] = 0.5 / sqrt(x[1]);
258 double myvconstraint(const std::vector`<double>` &x, std::vector`<double>` &grad, void *data)
260 my_constraint_data *d = reinterpret_cast`<my_constraint_data*>`(data);
261 double a = d->a, b = d->b;
263 grad[0] = 3 * a * (a*x[0] + b) * (a*x[0] + b);
266 return ((a*x[0] + b) * (a*x[0] + b) * (a*x[0] + b) - x[1]);
271 Notice that, instead of checking whether `grad` is `NULL`, we check whether it is empty. (The vector arguments, if non-empty, are guaranteed to be of the same size as the dimension of the problem that you specified.) We then specify these in the same way as before:
274 opt.set_min_objective(myvfunc, NULL);
275 opt.add_inequality_constraint(myvconstraint, &data[0], 1e-8);
276 opt.add_inequality_constraint(myvconstraint, &data[1], 1e-8);
280 Note that the data pointers passed to these functions must remain valid (or rather, what they point to must remain valid) until you are done with `opt`. (It might have been nicer to use `shared_ptr`, but I don't like to rely on bleeding-edge language features.)
282 Instead of passing a separate data pointer, some users may wish to define a C++ [function object](https://en.wikipedia.org/wiki/Function_object) class that contains all of the data needed by their function, with an overloaded `operator()` method to implement the function call. You can easily do this with a two-line helper function. If your function class is MyFunction, then you could define a static member function:
285 static double wrap(const std::vector`<double>` &x, std::vector`<double>` &grad, void *data) {
286 return (*reinterpret_cast`<MyFunction*>`(data))(x, grad); }
290 which you would then use e.g. by `opt.set_min_objective(MyFunction::wrap,` `&some_MyFunction)`. Again, you have to make sure that `some_MyFunction` does not go out of scope before you are done calling `nlopt::opt::optimize`.
292 To link your program, just link to the C NLopt library (`-lnlopt` `-lm` on Unix).
294 Example in Matlab or GNU Octave
295 -------------------------------
297 To implement this objective function in Matlab (or GNU Octave), we would write a file myfunc.m that looks like:
300 function [val, gradient] = myfunc(x)
303 gradient = [0, 0.5 / val];
308 Notice that we check the Matlab builtin variable `nargout` (the number of output arguments) to decide whether to compute the gradient. If we use a derivative-free optimization algorithm below, then `nargout` will always be 1 and the gradient need never be computed.
310 Our constraint function looks similar, except that it is parameterized by the coefficients *a* and *b*. We can just add these on as extra parameters, in a file `myconstraint.m`:
313 function [val, gradient] = myconstraint(x,a,b)
314 val = (a*x(1) + b)^3 - x(2);
316 gradient = [3*a*(a*x(1) + b)^2, -1];
321 The equivalent of the `nlopt_opt` is just a [structure](http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_prog/f2-88951.html), with fields corresponding to any parameters that we want to set. (Any structure fields that we don't include are equivalent to not setting those parameters, and using the defaults instead). You can get more information on the available parameters by typing `help` `nlopt_optimize` in Matlab. The equivalent of the C example above is to define an `opt` structure by:
324 opt.algorithm = NLOPT_LD_MMA
325 opt.lower_bounds = [-inf, 0]
326 opt.min_objective = @myfunc
327 opt.fc = { (@(x) myconstraint(x,2,0)), (@(x) myconstraint(x,-1,1)) }
328 opt.fc_tol = [1e-8, 1e-8];
333 We do not need to specify the dimension of the problem; this is implicitly specified by the size of the initial-guess vector passed to `nlopt_optimize` below (and must match the sizes of other vectors like `opt.lower_bounds`). The inequality constraints are specified as a [cell array](http://blogs.mathworks.com/loren/2006/06/21/cell-arrays-and-their-contents/) `opt.fc` of function handles (and the corresponding tolerances are in an array `opt.fc_tol`); notice how we use `@(x)` to define an anonymous/inline function in order to pass additional arguments to `myconstraint`.
335 Finally, we call `nlopt_optimize`:
338 [xopt, fmin, retcode] = nlopt_optimize(opt, [1.234 5.678])
342 `nlopt_optimize` returns three things: `xopt`, the optimal parameters found; `fmin`, the corresponding value of the objective function, and a return code `retcode` (positive on success and negative on failure).
344 The output of the above command is:
354 (The [return code](NLopt_Reference#Return_values.md) `4` corresponds to `NLOPT_XTOL_REACHED`, which means it converged to the specified *x* tolerance.) To switch to a derivative-free algorithm like COBYLA, we just change `opt.algorithm` parameter:
357 opt.algorithm = NLOPT_LN_COBYLA
361 ### Matlab verbose output
363 It is often useful to print out some status message to see what is happening, especially if your function evaluation is much slower or if a large number of evaluations are required (e.g. for global optimization). You can, of course, modify your function to print out whatever you want. As a shortcut, however, you can set a verbose option in NLopt's Matlab interface by:
370 If we do this, then running the MMA algorithm as above yields:
373 nlopt_minimize_constrained eval #1: 2.38286
374 nlopt_minimize_constrained eval #2: 2.35613
375 nlopt_minimize_constrained eval #3: 2.24586
376 nlopt_minimize_constrained eval #4: 2.0191
377 nlopt_minimize_constrained eval #5: 1.74093
378 nlopt_minimize_constrained eval #6: 1.40421
379 nlopt_minimize_constrained eval #7: 1.0223
380 nlopt_minimize_constrained eval #8: 0.685203
381 nlopt_minimize_constrained eval #9: 0.552985
382 nlopt_minimize_constrained eval #10: 0.544354
383 nlopt_minimize_constrained eval #11: 0.544331
387 This shows the objective function values at each intermediate step of the optimization. As in the C example above, it converges in 11 steps. The COBYLA algorithm requires a few more iterations, because it doesn't exploit the gradient information:
390 nlopt_optimize eval #1: 2.38286
391 nlopt_optimize eval #2: 2.38286
392 nlopt_optimize eval #3: 3.15222
393 nlopt_optimize eval #4: 1.20627
394 nlopt_optimize eval #5: 0.499441
395 nlopt_optimize eval #6: 0.709216
396 nlopt_optimize eval #7: 0.20341
397 nlopt_optimize eval #8: 0.745201
398 nlopt_optimize eval #9: 0.989693
399 nlopt_optimize eval #10: 0.324679
400 nlopt_optimize eval #11: 0.804318
401 nlopt_optimize eval #12: 0.541431
402 nlopt_optimize eval #13: 0.561137
403 nlopt_optimize eval #14: 0.531346
404 nlopt_optimize eval #15: 0.515784
405 nlopt_optimize eval #16: 0.541724
406 nlopt_optimize eval #17: 0.541422
407 nlopt_optimize eval #18: 0.541504
408 nlopt_optimize eval #19: 0.541499
409 nlopt_optimize eval #20: 0.541514
410 nlopt_optimize eval #21: 0.541352
411 nlopt_optimize eval #22: 0.542089
412 nlopt_optimize eval #23: 0.542575
413 nlopt_optimize eval #24: 0.543027
414 nlopt_optimize eval #25: 0.544911
415 nlopt_optimize eval #26: 0.541793
416 nlopt_optimize eval #27: 0.545225
417 nlopt_optimize eval #28: 0.544331
418 nlopt_optimize eval #29: 0.544256
419 nlopt_optimize eval #30: 0.544242
420 nlopt_optimize eval #31: 0.544116
424 Notice that some of the objective function values are below the minimum of 0.54433 — these are simply values of the objective function at infeasible points (violating the nonlinear constraints).
429 The same example in Python is:
437 grad[1] = 0.5 / sqrt(x[1])
439 def myconstraint(x, grad, a, b):
441 grad[0] = 3 * a * (a*x[0] + b)**2
443 return (a*x[0] + b)**3 - x[1]
444 opt = nlopt.opt(nlopt.LD_MMA, 2)
445 opt.set_lower_bounds([-float('inf'), 0])
446 opt.set_min_objective(myfunc)
447 opt.add_inequality_constraint(lambda x,grad: myconstraint(x,grad,2,0), 1e-8)
448 opt.add_inequality_constraint(lambda x,grad: myconstraint(x,grad,-1,1), 1e-8)
449 opt.set_xtol_rel(1e-4)
450 x = opt.optimize([1.234, 5.678])
451 minf = opt.last_optimum_value()
452 print("optimum at ", x[0], x[1])
453 print("minimum value = ", minf)
454 print("result code = ", opt.last_optimize_result())
458 Notice that the `optimize` method returns only the location of the optimum (as a NumPy array), and that the value of the optimum and the result code are obtained by `last_optimum_value` and `last_optimize_result` values. Like in C++, the NLopt functions raise exceptions on errors, so we don't need to check return codes to look for errors.
460 The objective and constraint functions take NumPy arrays as arguments; if the `grad` argument is non-empty it must be modified *in-place* to the value of the gradient. Notice how we use Python's `lambda` construct to pass additional parameters to the constraints. Alternatively, we could define the objective/constraints as classes with a `__call__(self,` `x,` `grad)` method so that they can behave like functions.
462 The result of running the above code should be:
465 optimum at 0.333333331366 0.296296292697
466 minimum value = 0.544331050646
471 finding the same correct optimum as in the C interface (of course). (The [return code](NLopt_Reference#Return_values.md) `4` corresponds to `nlopt.XTOL_REACHED`, which means it converged to the specified *x* tolerance.)
473 ### Important: Modifying `grad` in-place
475 The grad argument of your objective/constraint functions must be modified *in-place*. If you use an operation like
482 however, Python allocates a new array to hold `2*x` and reassigns grad to point to it, rather than modifying the original contents of grad. **This will not work.** Instead, you should do:
489 which *overwrites* the old contents of grad with `2*x`. See also the [NLopt Python Reference](NLopt_Python_Reference#Assigning_results_in-place.md).
491 Example in GNU Guile (Scheme)
492 -----------------------------
494 In [GNU Guile](https://en.wikipedia.org/wiki/GNU_Guile), which is an implementation of the [Scheme programming language](https://en.wikipedia.org/wiki/Scheme_(programming_language)), the equivalent of the example above would be:
497 (use-modules (nlopt))
498 (define (myfunc x grad)
501 (vector-set! grad 0 0.0)
502 (vector-set! grad 1 (/ 0.5 (sqrt (vector-ref x 1))))))
503 (sqrt (vector-ref x 1)))
504 (define (myconstraint x grad a b)
505 (let ((x0 (vector-ref x 0)) (x1 (vector-ref x 1)))
508 (vector-set! grad 0 (* 3 a (expt (+ (* a x0) b) 2)))
509 (vector-set! grad 1 -1.0)))
510 (- (expt (+ (* a x0) b) 3) x1)))
511 (define opt (new-nlopt-opt NLOPT-LD-MMA 2))
512 (nlopt-opt-set-lower-bounds opt (vector (- (inf)) 0))
513 (nlopt-opt-set-min-objective opt myfunc)
514 (nlopt-opt-add-inequality-constraint opt (lambda (x grad)
515 (myconstraint x grad 2 0))
517 (nlopt-opt-add-inequality-constraint opt (lambda (x grad)
518 (myconstraint x grad -1 1))
520 (nlopt-opt-set-xtol-rel opt 1e-4)
521 (define x (nlopt-opt-optimize opt (vector 1.234 5.678)))
522 (define minf (nlopt-opt-last-optimum-value opt))
523 (define result (nlopt-opt-last-optimize-result opt))
527 Note that the objective/constraint functions take two arguments, `x` and `grad`, and return a number. `x` is a vector whose length is the dimension of the problem; grad is either false (`#f`) if it is not needed, or a `vector` that must be modified *in-place* to the gradient of the function.
529 On error conditions, the NLopt functions throw [exceptions](http://www.gnu.org/software/guile/manual/html_node/Exceptions.html) that can be caught by your Scheme code if you wish.
531 The heavy use of side-effects here is a bit unnatural in Scheme, but is used in order to closely map to the C++ interface. (Notice that `nlopt::` C++ functions map to `nlopt-` Guile functions, and `nlopt::opt::` methods map to `nlopt-opt-` functions that take the `opt` object as the first argument.) Of course, you are free to wrap your own Scheme-like functional interface around this if you wish.
536 In [Fortran](https://en.wikipedia.org/wiki/Fortran), the equivalent of the C example above would be as follows. First, we would write our functions as:
539 subroutine myfunc(val, n, x, grad, need_gradient, f_data)
540 double precision val, x(n), grad(n)
541 integer n, need_gradient
542 if (need_gradient.ne.0) then
544 grad(2) = 0.5 / dsqrt(x(2))
552 subroutine myconstraint(val, n, x, grad, need_gradient, d)
553 integer need_gradient
554 double precision val, x(n), grad(n), d(2), a, b
557 if (need_gradient.ne.0) then
558 grad(1) = 3. * a * (a*x(1) + b)**2
561 val = (a*x(1) + b)**3 - x(2)
566 Notice that that the "functions" are actually subroutines. This is because it turns out to be hard to call Fortran functions from C or vice versa in any remotely portable way. Therefore:
568 - In the NLopt Fortran interface, all C functions become subroutines in Fortran, with the return value becoming the first argument.
570 So, here the first argument `val` is used for the return value. Also, because there is no way in Fortran to pass `NULL` for the `grad` argument, we add an additional `need_gradient` argument which is nonzero if the gradient needs to be computed. Finally, the last argument is the equivalent of the `void*` argument in the C API, and can be used to pass a single argument of any type through to the objective/constraint functions: here, we use it in `myconstraint` to pass an array of two values for the constants *a* and *b*.
572 Then, to run the optimization, we can use the following Fortran program:
576 external myfunc, myconstraint
577 double precision lb(2)
579 double precision d1(2), d2(2)
580 double precision x(2), minf
584 call nlo_create(opt, NLOPT_LD_MMA, 2)
585 call nlo_get_lower_bounds(ires, opt, lb)
587 call nlo_set_lower_bounds(ires, opt, lb)
588 call nlo_set_min_objective(ires, opt, myfunc, 0)
592 call nlo_add_inequality_constraint(ires, opt,
593 $ myconstraint, d1, 1.D-8)
596 call nlo_add_inequality_constraint(ires, opt,
597 $ myconstraint, d2, 1.D-8)
599 call nlo_set_xtol_rel(ires, opt, 1.D-4)
603 call nlo_optimize(ires, opt, x, minf)
605 write(*,*) 'nlopt failed!'
607 write(*,*) 'found min at ', x(1), x(2)
608 write(*,*) 'min val = ', minf
611 call nlo_destroy(opt)
617 There are a few things to note here:
619 - All `nlopt_` functions are converted into `nlo_` subroutines, with return values converted into the first argument.
620 - The "`nlopt_opt`" variable `opt` is declared as `integer*8`. (Technically, we could use any type that is big enough to hold a pointer on all platforms; `integer*8` is big enough for pointers on both 32-bit and 64-bit machines.)
621 - The subroutines must be declared as `external`.
622 - We `include` `'nlopt.f'` in order to get the various constants like `NLOPT_LD_MMA`.
624 There is no standard Fortran 77 equivalent of C's `HUGE_VAL` constant, so instead we just call `nlo_get_lower_bounds` to get the default lower bounds (-∞) and then change one of them. In Fortran 90 (and supported as an extension in many Fortran 77 compilers), there is a `huge` intrinsic function that we could have used instead:
634 The same example in the [Julia programming language](https://en.wikipedia.org/wiki/Julia_(programming_language)) can be found at the [NLopt.jl](https://github.com/stevengj/NLopt.jl) web page.
636 [Category:NLopt](index.md)