Nlopt在matlab中的配置教程

step1：克隆代码并编译

编译的前提是已经安装好MinGW64

# 使用镜像加速
git clone https://gitclone.com/github.com/stevengj/nlopt

cd nlopt
mkdir build
cd build
cmake -G="MinGW Makefiles" ..
cmake --build .

# 注意此处博主在mingw安装目录将mingw32-make重命名成了make
make install
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nlopt将被自动安装至C:/Program Files (x86)/nlopt下。

step2：编译mex文件

1. 将nlopt项目目录下的nlopt\src\octave\nlopt_optimize-mex.c拷贝到C:\Program Files (x86)\nlopt\lib\matlab，并重命名为nlopt_optimize.c
2. 在nlopt安装目录下，将include\nlopt.h和bin\libnlopt.dll拷贝到lib\matlab目录下
3. matlab工作目录切至lib\matlab，终端中执行：

    mex -v nlopt_optimize.c ../libnlopt.dll.a
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4. 将C:\Program Files (x86)\nlopt\lib\matlab添加到matlab路径中
   

至此已完成Nlopt在matlab中的配置

step3：测试

clc,clear,close all

opt.algorithm = NLOPT_LD_MMA;
opt.lower_bounds = [-inf, 0];
opt.min_objective = @myfunction;
opt.fc = { (@(x) myconstraint(x,2,0)), (@(x) myconstraint(x,-1,1)) };
opt.fc_tol = [1e-8, 1e-8];
opt.xtol_rel = 1e-4;
[xopt, fmin, retcode] = nlopt_optimize(opt, [1.234 5.678])

% 目标函数
function [val, gradient] = myfunction(x)
    val = sqrt(x(2));
    if (nargout > 1)
        gradient = [0, 0.5 / val];
    end
end

% 约束函数
function [val, gradient] = myconstraint(x,a,b)
    val = (a*x(1) + b)^3 - x(2);
    if (nargout > 1)
        gradient = [3*a*(a*x(1) + b)^2, -1];
    end
end
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参考链接：

• https://nlopt.readthedocs.io/en/latest/NLopt_Matlab_Reference/
• https://blog.csdn.net/jialing678/article/details/120415359