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| # NLopt on Windows | ||
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| NLopt on Windows | ||
| ---------------- | ||
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| NLopt works fine on Microsoft Windows computers, and you can compile it directly using the included [CMake](https://en.wikipedia.org/wiki/CMake) build scripts. | ||
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| To simplify installation, there are also precompiled 32-bit and 64-bit Windows [DLLs](https://en.wikipedia.org/wiki/Dynamic-link_library) (along with binaries for many other systems) at [NLoptBuilder/releases](https://github.com/stevengj/NLoptBuilder/releases). In particular, the Windows builds are | ||
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| - [NLopt.v2.6.1.i686-w64-mingw32.tar.gz](https://github.com/stevengj/NLoptBuilder/releases/download/v2.6.1/NLopt.v2.6.1.i686-w64-mingw32.tar.gz) (32-bit) | ||
| - [NLopt.v2.6.1.x86_64-w64-mingw32.tar.gz](https://github.com/stevengj/NLoptBuilder/releases/download/v2.6.1/NLopt.v2.6.1.x86_64-w64-mingw32.tar.gz) (64-bit) | ||
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| These `.tar.gz` files unpack (with a variety of Windows software, e.g. 7-zip) into a folder with a `bin` subdirectory that contains `libnlopt.dll`. To link with this in your compiler, you will typically also want the [import library](https://stackoverflow.com/questions/3573475/how-does-the-import-library-work-details) for the DLL, which can be found in the `lib` subdirectory and is called `libnlopt.dll.a` (this can be used similarly to the `.lib` files you may be used to). | ||
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| Unofficial Python binaries for Windows are available from Christoph Gohike: | ||
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| - [Python binaries for 64-bit Windows](http://www.lfd.uci.edu/~gohlke/pythonlibs/#nlopt) | ||
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| ### NLopt with MinGW | ||
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| If you want to compile NLopt on Windows: | ||
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| 1. Install [MSYS2](https://www.msys2.org/). | ||
| 2. Run the following command: | ||
| pacman -S --needed base-devel mingw-w64-i686-toolchain mingw-w64-x86_64-toolchain \ | ||
| git subversion mercurial \ | ||
| mingw-w64-i686-cmake mingw-w64-x86_64-cmake --disable-download-timeout | ||
| 3. Download the NLopt using git | ||
| git clone https://github.com/stevengj/nlopt.git | ||
| 4. Create a build folder inside the project. | ||
| 5. From build folder run the following command: | ||
| cmake -G"MSYS Makefiles" .. | ||
| 6. Then run `make` | ||
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| ### Octave plugin | ||
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| To build the NLopt plugin for [GNU Octave](https://en.wikipedia.org/wiki/GNU_Octave) (a free Matlab clone, which uses the [same NLopt interface as in Matlab](NLopt_Matlab_Reference.md)), you will need the following additional steps. (See [Octave for Windows](https://wiki.octave.org/Octave_for_Microsoft_Windows) on the Octave web page to download Octave.) | ||
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| 1. Copy `libnlopt.dll` from `build` to `C:\Octave\Octave-X.X.X.X\mingw64\bin` | ||
| 2. Copy `nlopt.f`, `nlopt.h`, and `nlopt.hpp` from `build\src\api` to `C:\Octave\Octave-X.X.X.X\mingw64\include` | ||
| 3. Create a folder named "nlopt" inside `C:\Octave\Octave-X.X.X.X\mingw64\lib\cmake` | ||
| 4. Copy `NLoptConfig.cmake`, `NLoptConfigVersion.cmake`, `NLoptLibraryDepends.cmake`, and `NLoptLibraryDepends-release.cmake` from `build` and `build\CMakeFiles\Export\lib\cmake\nlopt`, respectively, to `C:\Octave\Octave-X.X.X.X\mingw64\lib\cmake` | ||
| 5. Copy `nlopt.pc` from `build` to `C:\Octave\Octave-X.X.X.X\mingw64\lib\pkgconfig` | ||
| 6. Copy `libnlopt.dll.a` from `build` to `C:\Octave\Octave-X.X.X.X\mingw64\lib` | ||
| 7. Compile the Octave plugin (`.oct` file) with `mkoctfile -lnlopt --output nlopt_optimize.oct nlopt_optimize-oct.cc` (`mkoctfile` is a program included with Octave).fLyMd-mAkEr | ||
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| #define NLOPT_OPTIMIZE_USAGE \ | ||
|
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| "Usage: [xopt, fopt, retcode] = nlopt_optimize(opt, xinit)\n" \ | ||
| "\n" \ | ||
| "Optimizes (minimizes or maximizes) a nonlinear function under\n" \ | ||
| "nonlinear constraints from the starting guess xinit, where the\n" \ | ||
| "objective, constraints, stopping criteria, and other options are \n" \ | ||
| "specified in the structure opt described below. A variety of local\n" \ | ||
| "and global optimization algorithms can be used, as specified by the \n" \ | ||
| "opt.algorithm parameter described below. Returns the optimum\n" \ | ||
| "function value fopt, the location xopt of the optimum, and a\n" \ | ||
| "return code retcode described below (> 0 on success).\n" \ | ||
| "\n" \ | ||
| "The dimension (n) of the problem, i.e. the number of design variables,\n" \ | ||
| "is specified implicitly via the length of xinit.\n" \ | ||
| "\n" \ | ||
| "This function is a front-end for the external routine nlopt_optimize\n" \ | ||
| "in the free NLopt nonlinear-optimization library, which is a wrapper\n" \ | ||
| "around a number of free/open-source optimization subroutines. More\n" \ | ||
| "details can be found on the NLopt web page (ab-initio.mit.edu/nlopt)\n" \ | ||
| "and also under 'man nlopt_minimize' on Unix.\n" \ | ||
| "\n" \ | ||
| "OBJECTIVE FUNCTION:\n" \ | ||
| "\n" \ | ||
| "The objective function f is specified via opt.min_objective or\n" \ | ||
| "opt.max_objective for minimization or maximization, respectively.\n" \ | ||
| "opt.min/max_objective should be a handle (@) to a function of the form:\n" \ | ||
| "\n" \ | ||
| " [val, gradient] = f(x)\n" \ | ||
| "\n" \ | ||
| "where x is a row vector, val is the function value f(x), and gradient\n" \ | ||
| "is a row vector giving the gradient of the function with respect to x.\n" \ | ||
| "The gradient is only used for gradient-based optimization algorithms;\n" \ | ||
| "some of the algorithms (below) are derivative-free and only require\n" \ | ||
| "f to return val (its value).\n" \ | ||
| "\n" \ | ||
| "BOUND CONSTRAINTS:\n" \ | ||
| "\n" \ | ||
| "Lower and/or upper bounds for the design variables x are specified\n" \ | ||
| "via opt.lower_bounds and/or opt.upper_bounds, respectively: these\n" \ | ||
| "are vectors (of the same length as xinit, above) giving the bounds\n" \ | ||
| "in each component. An unbounded component may be specified by a\n" \ | ||
| "lower/upper bound of -inf/+inf, respectively. If opt.lower_bounds\n" \ | ||
| "and/or opt.upper_bounds are not specified, the default bounds are\n" \ | ||
| "-inf/+inf (i.e. unbounded), respectively.\n" \ | ||
| "\n" \ | ||
| "NONLINEAR CONSTRAINTS:\n" \ | ||
| "\n" \ | ||
| "Several of the algorithms in NLopt (MMA, COBYLA, and ORIG_DIRECT) also\n" \ | ||
| "support arbitrary nonlinear inequality constraints, and some also allow\n" \ | ||
| "nonlinear equality constraints (ISRES and AUGLAG). For these \n" \ | ||
| "algorithms, you can specify as many nonlinear constraints as you wish.\n" \ | ||
| "(The default is no nonlinear constraints.)\n" \ | ||
| "\n" \ | ||
| "Inequality constraints of the form fc{i}(x) <= 0 are specified via opt.fc,\n" \ | ||
| "which is a cell array of function handles (@) of the same form as\n" \ | ||
| "the objective function above (i.e., returning the value and optionally\n" \ | ||
| "the gradient of the constraint function fc{i}, where the gradient is\n" \ | ||
| "only needed for gradient-based algorithms).\n" \ | ||
| "\n" \ | ||
| "Equality constraints of the form h{i}(x) = 0 are specified via opt.h,\n" \ | ||
| "which is a cell array of function handles (@) of the same form as\n" \ | ||
| "the objective function above (i.e., returning the value and optionally\n" \ | ||
| "the gradient of the constraint function h{i}, where the gradient is\n" \ | ||
| "only needed for gradient-based algorithms).\n" \ | ||
| "\n" \ | ||
| "For both inequality and equality constraints, you can supply a\n" \ | ||
| "\"tolerance\" for each constraint: this tolerance is used for convergence\n" \ | ||
| "tests only, and a point x is considered feasible for purposes of\n" \ | ||
| "convergence if the constraint is violated by the given tolerance.\n" \ | ||
| "The tolerances are specified via opt.fc_tol and opt.h_tol, respectively,\n" \ | ||
| "which must be vectors of the same length as opt.fc and opt.h, so\n" \ | ||
| "that opt.fc_tol(i) is the tolerance for opt.fc{i} and opt.h_tol(i)\n" \ | ||
| "is the tolerance for opt.h{i}. These tolerances default to zero; a\n" \ | ||
| "small nonzero tolerance is recommended, however, especially for h_tol.\n" \ | ||
| "\n" \ | ||
| "ALGORITHMS\n" \ | ||
| "\n" \ | ||
| "The optimization algorithm must be specified via opt.algorithm.\n" \ | ||
| "\n" \ | ||
| "The algorithm should be one of the following constants (name and\n" \ | ||
| "interpretation are the same as for the C language interface). Names\n" \ | ||
| "with _G*_ are global optimization, and names with _L*_ are local\n" \ | ||
| "optimization. Names with _*N_ are derivative-free, while names\n" \ | ||
| "with _*D_ are gradient-based algorithms. Algorithms:\n" \ | ||
| "\n" \ | ||
| "NLOPT_GD_MLSL_LDS, NLOPT_GD_MLSL, NLOPT_GD_STOGO, NLOPT_GD_STOGO_RAND, \n" \ | ||
| "NLOPT_GN_CRS2_LM, NLOPT_GN_DIRECT_L, NLOPT_GN_DIRECT_L_NOSCAL, \n" \ | ||
| "NLOPT_GN_DIRECT_L_RAND, NLOPT_GN_DIRECT_L_RAND_NOSCAL, NLOPT_GN_DIRECT, \n" \ | ||
| "NLOPT_GN_DIRECT_NOSCAL, NLOPT_GN_ISRES, NLOPT_GN_MLSL_LDS, NLOPT_GN_MLSL, \n" \ | ||
| "NLOPT_GN_ORIG_DIRECT_L, NLOPT_GN_ORIG_DIRECT, NLOPT_LD_AUGLAG_EQ, \n" \ | ||
| "NLOPT_LD_AUGLAG, NLOPT_LD_LBFGS, NLOPT_LD_LBFGS_NOCEDAL, NLOPT_LD_MMA, \n" \ | ||
| "NLOPT_LD_TNEWTON, NLOPT_LD_TNEWTON_PRECOND, \n" \ | ||
| "NLOPT_LD_TNEWTON_PRECOND_RESTART, NLOPT_LD_TNEWTON_RESTART, \n" \ | ||
| "NLOPT_LD_VAR1, NLOPT_LD_VAR2, NLOPT_LN_AUGLAG_EQ, NLOPT_LN_AUGLAG, \n" \ | ||
| "NLOPT_LN_BOBYQA, NLOPT_LN_COBYLA, NLOPT_LN_NELDERMEAD, \n" \ | ||
| "NLOPT_LN_NEWUOA_BOUND, NLOPT_LN_NEWUOA, NLOPT_LN_PRAXIS, NLOPT_LN_SBPLX\n" \ | ||
| "\n" \ | ||
| "For more information on individual algorithms, see their individual\n" \ | ||
| "help pages (e.g. \"help NLOPT_LN_SBPLX\").\n" \ | ||
| "\n" \ | ||
| "STOPPING CRITERIA:\n" \ | ||
| "\n" \ | ||
| "Multiple stopping criteria can be specified by setting one or more of\n" \ | ||
| "the following fields of opt. The optimization halts whenever any\n" \ | ||
| "one of the given criteria is satisfied.\n" \ | ||
| "\n" \ | ||
| "opt.stopval: Stop when an objective value of at least stopval is found.\n" \ | ||
| " That is, stop minimizing when a value <= stopval is found, or stop\n" \ | ||
| " maximizing when a value >= stopval is found.\n" \ | ||
| "\n" \ | ||
| "opt.ftol_rel: Relative tolerance on function value, to stop when\n" \ | ||
| " an optimization step (or an estimate of the optimum) changes\n" \ | ||
| " the function value by less than opt.ftol_rel multiplied by\n" \ | ||
| " the absolute value of the function.\n" \ | ||
| "\n" \ | ||
| "opt.ftol_abs: Absolute tolerance on function value, to stop when\n" \ | ||
| " an optimization step (or an estimate of the optimum) changes\n" \ | ||
| " the function value by less than opt.ftol_abs.\n" \ | ||
| "\n" \ | ||
| "opt.xtol_rel: Relative tolerance on function value, to stop when\n" \ | ||
| " an optimization step (or an estimate of the optimum) changes\n" \ | ||
| " every component of x by less than opt.xtol_rel multiplied by\n" \ | ||
| " the absolute value of that component of x.\n" \ | ||
| "\n" \ | ||
| "opt.xtol_abs: Absolute tolerance on function value, to stop when\n" \ | ||
| " an optimization step (or an estimate of the optimum) changes\n" \ | ||
| " every component of x by less than that component of opt.xtol_abs\n" \ | ||
| " -- should be a vector of same length as x.\n" \ | ||
| "\n" \ | ||
| "opt.maxeval: Maximum number of function evaluations.\n" \ | ||
| "\n" \ | ||
| "opt.maxtime: Maximum runtime (in seconds) for the optimization.\n" \ | ||
| "\n" \ | ||
| "RETURN CODE:\n" \ | ||
| "\n" \ | ||
| "The retcode result is positive upon successful completion, and\n" \ | ||
| "negative for an error. The specific values are:\n" \ | ||
| "\n" \ | ||
| "generic success code: +1\n" \ | ||
| " stopval reached: +2\n" \ | ||
| " ftol reached: +3\n" \ | ||
| " xtol reached: +4\n" \ | ||
| " maxeval reached: +5\n" \ | ||
| " maxtime reached: +6\n" \ | ||
| "generic failure code: -1\n" \ | ||
| " invalid arguments: -2\n" \ | ||
| " out of memory: -3\n" \ | ||
| " roundoff-limited: -4\n" \ | ||
| "\n" \ | ||
| "LOCAL OPTIMIZER:\n" \ | ||
| "\n" \ | ||
| "Some of the algorithms, especially MLSL and AUGLAG, use a different\n" \ | ||
| "optimization algorithm as a subroutine, typically for local optimization.\n" \ | ||
| "By default, they use MMA or COBYLA for gradient-based or derivative-free\n" \ | ||
| "searching, respectively. However, you can change this by specifying\n" \ | ||
| "opt.local_optimizer: this is a structure with the same types of fields as opt\n" \ | ||
| "(stopping criteria, algorithm, etcetera). The objective function\n" \ | ||
| "and nonlinear constraint parameters of opt.local_optimizer are ignored.\n" \ | ||
| "\n" \ | ||
| "INITIAL STEP SIZE:\n" \ | ||
| "\n" \ | ||
| "For derivative-free local-optimization algorithms, the optimizer must\n" \ | ||
| "somehow decide on some initial step size to perturb x by when it begins\n" \ | ||
| "the optimization. This step size should be big enough that the value\n" \ | ||
| "of the objective changes significantly, but not too big if you want to\n" \ | ||
| "find the local optimum nearest to x. By default, NLopt chooses this\n" \ | ||
| "initial step size heuristically from the bounds, tolerances, and other\n" \ | ||
| "information, but this may not always be the best choice.\n" \ | ||
| "\n" \ | ||
| "You can modify the initial step by setting opt.initial_step, which\n" \ | ||
| "is a vector of the same length as x containing the (nonzero) initial\n" \ | ||
| "step size for each component of x.\n" \ | ||
| "\n" \ | ||
| "STOCHASTIC POPULATION:\n" \ | ||
| "\n" \ | ||
| "Several of the stochastic search algorithms (e.g., CRS, MLSL, and\n" \ | ||
| "ISRES) start by generating some initial \"population\" of random points\n" \ | ||
| "x. By default, this initial population size is chosen heuristically in\n" \ | ||
| "some algorithm-specific way, but the initial population can by changed\n" \ | ||
| "by setting opt.population to the desired initial population size.\n" \ | ||
| "\n" \ | ||
| "VERBOSE OUTPUT:\n" \ | ||
| "\n" \ | ||
| "If opt.verbose is set to a nonzero value, then nlopt_optimize\n" \ | ||
| "will print out verbose output; for example, it will print the\n" \ | ||
| "value of the objective function after each evaluation.\n" \ | ||
| "\n" \ | ||
| "MORE INFORMATION:\n" \ | ||
| "\n" \ | ||
| "For more documentation, such as a detailed description of all the\n" \ | ||
| "algorithms, see the NLopt home page: http://ab-initio.mit.edu/nlopt\n" \ | ||
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