# nlopt-f **Repository Path**: fortran-base/nlopt-f ## Basic Information - **Project Name**: nlopt-f - **Description**: 有约束的非线性方程组求解库 - **Primary Language**: FORTRAN - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-02-15 - **Last Updated**: 2024-05-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Fortran bindings for the NLopt library [![License](https://img.shields.io/badge/license-MIT%7CApache%202.0-blue)](LICENSE-Apache) [![CI](https://github.com/grimme-lab/nlopt-f/actions/workflows/build.yml/badge.svg)](https://github.com/grimme-lab/nlopt-f/actions/workflows/build.yml) Fortran bindings for the NLopt library. While the NLopt library supports Fortran by using implicit interface calling conventions, those are not type-safe. This project offers an alternative interface to the NLopt C-API. ## Installation To build this project from the source code in this repository you need to have - a Fortran compiler supporting Fortran 2008 (GCC 5 or newer or Intel Fortran) - One of the supported build systems - [meson](https://mesonbuild.com) version 0.55 or newer, with a build-system backend, *i.e.* [ninja](https://ninja-build.org) version 1.7 or newer - [cmake](https://cmake.org) version 3.14 or newer, with a build-system backend, *i.e.* [ninja](https://ninja-build.org) version 1.10 or newer - [Fortran package manager (fpm)](https://github.com/fortran-lang/fpm) version 0.2.0 or newer - [nlopt](https://nlopt.readthedocs.io/en/latest/) version 2.0.0 or later ### Building with meson Setup a build with ``` meson setup _build ``` You can select the Fortran compiler by the `FC` environment variable. To compile the project run ``` meson compile -C _build ``` You can run the projects testsuite with ``` meson test -C _build --print-errorlogs ``` To include ``nlopt-f`` in your project add the following wrap file to your subprojects directory: ```ini [wrap-git] directory = nlopt-f url = https://github.com/grimme-lab/nlopt-f revision = head ``` You can retrieve the dependency from the wrap fallback with ```meson nlopt_dep = dependency('nlopt-f', fallback: ['nlopt-f', 'nlopt_dep']) ``` and add it as dependency to your targets. ### Building with CMake Alternatively, this project can be build with CMake (in this case ninja 1.10 or newer is required): ``` cmake -B _build -G Ninja ``` To compile the project with CMake run ``` cmake --build _build ``` You can run the project testsuite with ``` pushd _build && ctest && popd ``` Finally, install the project using (the install prefix can be customized with ``-DCMAKE_INSTALL_PREFIX=/path/to/install`` in the first step) ``` cmake --install _build ``` Now you can use it in your CMake project by finding it again ```cmake if(NOT "nlopt-f::nlopt-f") find_package("nlopt-f" REQUIRED) endif() # ... target_link_libraries("${PROJECT_NAME}-lib" PRIVATE "nlopt-f::nlopt-f") ``` ### Building with fpm Invoke fpm in the project root with ``` fpm build ``` To run the testsuite use ``` fpm test ``` To use ``nlopt-f`` include it as dependency in your package manifest ```toml [dependencies] nlopt-f.git = "https://github.com/grimme-lab/nlopt-f" ``` The Fortran bindings target NLopt 2.5.0 by default. You can target a specific NLopt version by adding the preprocessor define ``NLOPT_VERSION`` using a compact integer representation (major * 10000 + minor * 100 + patch). To target NLopt 2.6.2 use ``` fpm --profile debug --flag "-DNLOPT_VERSION=20602" ``` If NLopt is not installed in a standard location use pkg-config to find it ``` fpm --profile debug --flag "$(pkg-config nlopt --cflags --libs-only-L)" ``` You can check your installed NLopt version using ``` pkg-config nlopt --modversion ``` Which will print the version number. ## Usage The Fortran bindings allow an object oriented usage of the NLopt C-API: ```f90 module example_funcs implicit none integer, parameter :: wp = kind(0.0d0) type :: constraint_data real(wp) :: d(2) end type contains function myfunc(x, gradient, func_data) result(f) real(wp), intent(in) :: x(:) real(wp), intent(inout), optional :: gradient(:) class(*), intent(in), optional :: func_data real(wp) :: f if (present(gradient)) then gradient(1) = 0.0_wp gradient(2) = 0.5_wp / sqrt(x(2)) endif f = sqrt(x(2)) end function myfunc function myconstraint(x, gradient, func_data) result(f) real(wp), intent(in) :: x(:) real(wp), intent(inout), optional :: gradient(:) class(*), intent(in), optional :: func_data real(wp) :: f select type(func_data) type is(constraint_data) associate(a => func_data%d(1), b => func_data%d(2)) if (present(gradient)) then gradient(1) = 3.0_wp * a * (a*x(1) + b)**2 gradient(2) = -1.0_wp endif f = (a*x(1) + b)**3 - x(2) end associate end select end function myconstraint end module example_funcs program example use example_funcs use nlopt_wrap, only : nlopt_opt, nlopt_func, create, destroy use nlopt_enum, only : NLOPT_SUCCESS, algorithm_from_string implicit none type(nlopt_opt) :: opt real(wp) :: lb(2), x(2), minf integer :: stat type(constraint_data), target :: d1, d2 real(wp), parameter :: xtol = 1.0e-4_wp call create(opt, algorithm_from_string("LD_MMA"), 2) call opt%get_lower_bounds(lb) lb(2) = 0.0_wp call opt%set_lower_bounds(lb) d1%d = [+2.0_wp, +0.0_wp] d2%d = [-1.0_wp, +1.0_wp] associate(& & f => nlopt_func(myfunc), & & fc1 => nlopt_func(myconstraint, d1), & & fc2 => nlopt_func(myconstraint, d2)) call opt%set_min_objective(f) call opt%add_inequality_constraint(fc1, 1.0e-8_wp) call opt%add_inequality_constraint(fc2, 1.0e-8_wp) call opt%set_xtol_rel(xtol) x = [1.234_wp, 5.678_wp] call opt%optimize(x, minf, stat) end associate if (stat < NLOPT_SUCCESS) then write(*, '(a)') "NLopt failed!" stop 1 endif write(*, '(a, *(1x, g0))') "Found minimum at", x write(*, '(a, *(1x, g0))') "Minimum value is", minf call destroy(opt) end program example ``` ## License This project is free software: you can redistribute it and/or modify it under the terms of the [Apache License, Version 2.0](LICENSE-Apache) or [MIT license](LICENSE-MIT) at your opinion. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an _as is_ basis, without warranties or conditions of any kind, either express or implied. See the License for the specific language governing permissions and limitations under the License. Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.