fmincon
代码生成的静态内存分配
此示例说明了即使在计算过程中某些矩阵大小发生变化,如何在代码生成中使用静态内存分配。
该问题是一个简单的非线性最小化,具有非线性约束函数和线性约束。线性约束矩阵的大小在每次迭代时都会发生变化,这导致内存需求在每次迭代时都会增加。该示例显示如何使用 coder.varsize
命令为静态内存分配设置适当的变量大小。
nlp_for_loop.m
文件包含目标函数、线性约束和非线性约束函数。复制以下代码以在您的 MATLAB® 路径上创建此文件。
function nlp_for_loop % Driver for an example fmincon use case. Adding constraints increases the % minimum and uses more memory. maxIneq = 4; % Number of linear inequality constraints nVar = 5; % Number of problem variables x A = zeros(0,nVar); b = zeros(0,1); % The next step is required for static memory support. Because you concatenate % constraints in a "for" loop, you need to limit the dimensions of the % constraint matrices. %coder.varsize('var name', [maxRows, maxCols], [canRowsChange, canColsChange]); coder.varsize('A',[maxIneq,nVar],[true,false]); coder.varsize('b',[maxIneq,1],[true,false]); Aeq = [1,0,0,0,1]; beq = 0; lb = []; ub = []; % Initial point x0 = [2;-3;0;0;-2]; options = optimoptions('fmincon','Algorithm','sqp','Display','none'); for idx = 1:maxIneq % Add a new linear inequality constraint at each iteration A = [A; circshift([1,1,0,0,0],idx-1)]; b = [b; -1]; [x,fval,exitflag] = fmincon(@rosenbrock_nd,x0,A,b,Aeq,beq,... lb,ub,@circleconstr,options); % Set initial point to found point x0 = x; % Print fval, ensuring that the datatypes are consistent with the % corresponding fprintf format specifiers fprintf('%i Inequality Constraints; fval: %f; Exitflag: %i \n',... int32(numel(b)),fval,int32(exitflag)); end end function fval = rosenbrock_nd(x) fval = 100*sum((x(2:end)-x(1:end-1).^2).^2 + (1-x(1:end-1)).^2); end function [c,ceq] = circleconstr(x) radius = 2; ceq = []; c = sum(x.^2) - radius^2; end
要使用静态内存分配从此文件生成代码,请按如下所示设置编码器配置。
cfg = coder.config('mex'); cfg.DynamicMemoryAllocation = 'Off'; % No dynamic memory allocation cfg.SaturateOnIntegerOverflow = false; % No MATLAB integer saturation checking cfg.IntegrityChecks = false; % No checking for out-of-bounds access in arrays
为 nlp_for_loop.m
文件生成代码。
codegen -config cfg nlp_for_loop
运行生成的 MEX 文件。
nlp_for_loop_mex
1 Inequality Constraints; fval: 542.688894; Exitflag: 1 2 Inequality Constraints; fval: 793.225322; Exitflag: 1 3 Inequality Constraints; fval: 1072.945843; Exitflag: 1 4 Inequality Constraints; fval: 1400.000000; Exitflag: 1
由于问题有更多的约束,因此函数值在每次迭代中都会增加。