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非线性约束的优化问题_序列二次规划算法代码

news 2025/8/7 5:33:48

1. 理论部分

2. 序列二次规划算法代码及解析

3.完整代码

1.理论部分

a.约束优化问题的极值条件

库恩塔克条件(Kuhn-Tucker conditions，KT条件)是确定某点为极值点的必要条件。如果所讨论的规划是凸规划，那么库恩-塔克条件也是充分条件。

（1）判断数值x处起作用的约束，计算x处各约束的取值是否为0，找出起作用的约束条件。并非所有约束条件都起作用，即并非落在所有约束条件的边界交集处。对于起作用的边界条件，满足边界函数g(x)或h(x)为0；

（2）数值x处，目标函数导数f'(x)和起作用的约束导数g'(x)的数值；

（3）代入KT条件（ex. $f'(x) + \lambda_1 g'(x_1) + \lambda_2 g'(x_2) =0$ ），求解拉格朗日乘子λ的大小，判断λ是否大于0，若大于零，则为极值点。

（4）若不满足条件，继续迭代。

具体做法如下：

其中λ为拉格朗日乘子，当某一点x使λ > 0存在时，称满足KT条件，则该点x是极值点。
约束优化问题的应用示例如下图：

b.约束优化方法及迭代

此处介绍和使用外惩罚函数法/外点法

外点法是一种从随机一点（一般在可行域外）出发，逐渐移动到可行区域的方法。
对于约束优化问题：

$\min_{x}f(x)$

$s.t. \space g_u(x) \leqslant 0, \space u=1,2,...m$

$s.t. \space h_v(x) = 0, \space v=1,2,...p$

构造外惩罚函数：

$\Phi(x, M_k) = f(x) + M_k \left \{ \sum_{u=1}^{m} (max[g_u(x),0])^2 + \sum_{v=1}^{p} (h_v(x))^2 \right \}$

即，当g(x)不满足条件时，该项有大于0的值，h(x)不满足条件时，该项也有值，加和的越大代表该数值点距离距离约束的可行域越远，惩罚项的值越大，惩罚作用越明显。。其中Mk为外罚因子，M 0 < M 1 < M 2 . . . < M k 为递增的正数序列，一般M0=1，递增系数c=5~8。

迭代终止满足两个判断条件中一个即可：

1. $Q\leqslant 10^{-3}$ ，Q为当前数值点多个约束函数中最大的值，即满足了所有的约束函数条件。

2. $M> R$ 且 $\left \| xM_{k+1} - xM_k \right \| \leqslant \epsilon$ ，R为外罚因子控制量，M超出外罚因子的极限R

外点法的迭代图如下：

外点法的示例如下图：

a. 定义变量

vars = 2; % 自变量数量
cons = 1; % 约束数量
maxIter=100; % 最大迭代次数
x = [-1;4]; % 初始猜测值
l =0; % 拉格朗日乘子
H = eye(vars,vars); % Hessian 矩阵，假设为I矩阵

目标函数为

$f(x)= x_1^4 - 2x_1^2x_2+x_2^2+x_1^2-2x_1+5$

及目标函数的导数为

$f'(x_1) = 4x_1^3 - 4x_1x_2 + 2x_1 - 2$

$f'(x_2) = -2x_1^2 + 2x_2$

function y = f(x)y = x(1)^4 - 2*x(2)*x(1)^2 + x(2)^2 + x(1)^2 - 2*x(1)+5;
endfunction y = gradf(x)y(1) = 2*x(1)-4*x(1)*x(2)+4*x(1)^3-2;y(2) = -2*x(1)^2 + 2*x(2);
end

约束项不等式为

$-(x_1 + 0.25)^2 + 0.75x_2 >= 0$

以及其导数为

$g'(x_1) = -2(x_1 -0.25)=-2x_1-0.5$

$g'(x_2) = 0.75$

function y = g(x)y(1) = -(x(1)+0.25)^2+0.75*x(2);
endfunction y = gradg(x)y(1,1) = -2*x(1)-1/2;y(1,2) = 3/4;
end

求解KKT条件

function y = SolveKKT(gradfEval,gradgEval,gEval,Hessian)A = [Hessian -gradgEval';gradgEval 0]; %对称矩阵，约束函数的梯度矩阵b = [-gradfEval -gEval]';y = A\b; %方程  Ay = b 解出y值，分别对应最优的x值的梯度和最优的拉格朗日乘子的值%即此时Ay-b = 0，kkt条件成立，
end

判断数值点x是否满足约束条件

function [gViol,lViol] = Viols(gEval,l)%约束条件函数值，拉格朗日乘子lgViol=[];lViol=[];for i = 1:length(gEval)if gEval(i)<0  %如果约束条件函数值小于零，即，不满足约束条件gViol(i)=gEval(i); %将约束条件函数值和拉格朗日乘子l，放入列表lViol(i)=l(i);end    end
end

处理不满足约束的情况，构造惩罚函数，lViol对应Mk，累加得到惩罚函数

function y = Penalty(fEval,gViol,lViol)
%代入该点处的函数值，不满足约束的约束函数值，以及拉格朗日乘子lsum = 0;y = fEval;for i = 1:length(gViol) %找出不满足约束条件的约束函数值sum = sum +  lViol(i)*abs(gViol(i)); %约束函数值g(x)求反，再乘一个系数，即，||l_1*g(x1)+l2*g(x2) ||endy = y + sum;
end

在初始点首先进行初始计算，找出不满足约束的约束，计算其损失函数。

% EVALUATE AT STARTING POINTfEval= f(x);
gEval = g(x);
[gViol,lViol] = Viols(gEval,l); 
gradfEval = gradf(x);
gradgEval = gradg(x);
P = Penalty(fEval,gViol,lViol);

开始进入SQP算法主体,求解kkt条件，得到xSol为函数x的解，lSol为拉格朗日乘子的解。

for i=1:maxIter %开始循环迭代% SOLVE KKT CONDITIONS FOR THE OPTIMUM OF THE QUADRATIC APPROXIMATION%求解二次优化的KKT条件sol = SolveKKT(gradfEval,gradgEval,gEval,H);%代入导数f'(x)，g'(x),约束函数值g(x)，Hessian矩阵，xSol = sol(1:vars);lSol = sol(vars+1:vars+cons);

如果拉格朗日乘子有负的，即，不满足约束条件，则将其设为0，并重新求解方程 $\frac{\partial {f_(x^*)}^2 }{\partial x^2} {sol}= f'(x^*)$

    for j = 1:length(lSol)if lSol(j)<0    %如果拉格朗日乘子有一个是负的sol= H\(-gradfEval)'; %将结果重新求解H*x = -gradfEvalxSol = sol(1:vars);lSol(j)=0;  %该约束的拉格朗日乘子置为0endend

计算新的数值点情况

    xNew = x + xSol; %更新得到新的数值点lNew = lSol;  % 得到新的拉格朗日乘子fEvalNew = f(xNew);%计算新点目标函数的数值gEvalNew = g(xNew);%计算新点的约束函数数值gradfEvalNew = gradf(xNew);%计算该点目标函数梯度gradgEvalNew = gradg(xNew);%计算该点约束函数梯度[gViolNew,lViolNew] = Viols(gEvalNew,lNew);%找出不满足的约束条件PNew = Penalty(fEvalNew,gViolNew,lViolNew);%求解惩罚函数

如果惩罚函数增加，减小步长到原来的一半

    % IF PENALTY FUNCTION INCREASED, LOWER THE STEP BY 0.5while PNew-P>1e-4xSol = 0.5*xSol;xNew = x + xSol;fEvalNew = f(xNew);gEvalNew = g(xNew);gradfEvalNew = gradf(xNew);gradgEvalNew = gradg(xNew);[gViolNew,lViolNew] = Viols(gEvalNew,lNew);PNew = Penalty(fEvalNew,gViolNew,lViolNew);end

如果x数值变化很小，停止迭代，找到最优，结束迭代

    % STOPPING CRITERIONif norm(xNew(1:vars)-x(1:vars))<=1e-2breakend

更新Hessian矩阵，Q为Mk+1- Mk的值

    % UPDATE THE HESSIANgradLEval = gradLagr(gradfEval,gradgEval,lNew,vars); %拉格朗日函数的梯度 lnew not l!!!gradLEvalNew = gradLagr(gradfEvalNew,gradgEvalNew,lNew,vars);%新的数值点处，拉格朗日函数的梯度Q = gradLEvalNew-gradLEval;%拉格朗日函数数值的差分L(x_k+1) - L(x_k)dx = xNew-x;%自变量x的差分HNew = UpdateH(H,dx,Q);%求解H矩阵

计算拉格朗日乘子梯度的函数

function y = gradLagr(gradfEval,gradgEval,l,n)y = gradfEval';sum = zeros(n,1);for i = 1:length(l)sum = sum -l(i)*gradgEval(i:n)';endy = y + sum;  %x处目标函数的梯度，减去约束函数的梯度。即，拉格朗日函数的梯度
end

更新Hessian矩阵，gamma为Mk+1- Mk的值

function y = UpdateH(H,dx,gamma)%gamma是拉格朗日函数的两次差分term1=(gamma*gamma') / (gamma'*dx);term2 = ((H*dx)*(dx'*H)) / (dx'*(H*dx));y = H + term1-term2; %更新得到新的H矩阵
end

更新所有的变量，用于下次的迭代，包括Hessian矩阵H，数值点x，惩罚函数值P，目标函数值fEval，目标函数值梯度gradEval，约束函数值gEval，约束函数值梯度gradgEval

    % UPDATE ALL VALUES FOR NEXT ITERATIONH = HNew;fEval = fEvalNew;gEval = gEvalNew;gradfEval = gradfEvalNew;gradgEval = gradgEvalNew;P = PNew;x = xNew;

3.可以运行的完整代码如下：

% EXAMPLE OF SQP ALGORITHMclear variables; close all; clc;
fprintf("---------------------------------------------------------------\n")
fprintf("An implementation of Sequential Quadratic Programming method\nin a nonlinear constrained optimization problem\n")
fprintf("---------------------------------------------------------------\n")% INITIAL VALUES - INPUTvars = 2; % number of variables
cons = 1; % number of constraints
maxIter=100; % max iterations
x = [-1;4]; % initial guess point
l =0; % LagrangeMultipliers vector
H = eye(vars,vars); % Hessian matrix assumed to be identity% EVALUATE AT STARTING POINTfEval= f(x);
gEval = g(x);
[gViol,lViol] = Viols(gEval,l); 
gradfEval = gradf(x);
gradgEval = gradg(x);
P = Penalty(fEval,gViol,lViol);% SQP ALGORITHMfor i=1:maxIter% SOLVE KKT CONDITIONS FOR THE OPTIMUM OF THE QUADRATIC APPROXIMATIONsol = SolveKKT(gradfEval,gradgEval,gEval,H);xSol = sol(1:vars);lSol = sol(vars+1:vars+cons);% IF THE LAGRANGE MULTIPLIER IS NEGATIVE SET IT TO ZEROfor j = 1:length(lSol)if lSol(j)<0 sol= H\(-gradfEval)';xSol = sol(1:vars);lSol(j)=0;endend% EVALUATE AT NEW CANDIDATE POINTxNew = x + xSol; lNew = lSol;fEvalNew = f(xNew);gEvalNew = g(xNew);gradfEvalNew = gradf(xNew);gradgEvalNew = gradg(xNew);[gViolNew,lViolNew] = Viols(gEvalNew,lNew);PNew = Penalty(fEvalNew,gViolNew,lViolNew);% IF PENALTY FUNCTION INCREASED, LOWER THE STEP BY 0.5while PNew-P>1e-4xSol = 0.5*xSol;xNew = x + xSol;fEvalNew = f(xNew);gEvalNew = g(xNew);gradfEvalNew = gradf(xNew);gradgEvalNew = gradg(xNew);[gViolNew,lViolNew] = Viols(gEvalNew,lNew);PNew = Penalty(fEvalNew,gViolNew,lViolNew);end% STOPPING CRITERIONif norm(xNew(1:vars)-x(1:vars))<=1e-2breakend% UPDATE THE HESSIANgradLEval = gradLagr(gradfEval,gradgEval,lNew,vars); % lnew not l!!!gradLEvalNew = gradLagr(gradfEvalNew,gradgEvalNew,lNew,vars);Q = gradLEvalNew-gradLEval;dx = xNew-x;HNew = UpdateH(H,dx,Q);% UPDATE ALL VALUES FOR NEXT ITERATIONH = HNew;fEval = fEvalNew;gEval = gEvalNew;gradfEval = gradfEvalNew;gradgEval = gradgEvalNew;P = PNew;x = xNew;
endfprintf('SQP: Optimum point:\n x1=%10.4f\n x2=%10.4f\n iterations =%10.0f \n', x(1), x(2), i)% FUNCTIONS NEEDEDfunction y = SolveKKT(gradfEval,gradgEval,gEval,Hessian)A = [Hessian -gradgEval';gradgEval 0];b = [-gradfEval -gEval]';y = A\b;
endfunction y = f(x)y = x(1)^4 - 2*x(2)*x(1)^2 + x(2)^2 + x(1)^2 - 2*x(1)+5;
endfunction y = gradf(x)y(1) = 2*x(1)-4*x(1)*x(2)+4*x(1)^3-2;y(2) = -2*x(1)^2 + 2*x(2);
endfunction y = gradLagr(gradfEval,gradgEval,l,n)y = gradfEval';sum = zeros(n,1);for i = 1:length(l)sum = sum -l(i)*gradgEval(i:n)';endy = y + sum;
endfunction y = gradg(x)y(1,1) = -2*x(1)-1/2;y(1,2) = 3/4;
endfunction y = g(x)y(1) = -(x(1)+0.25)^2+0.75*x(2);
endfunction [gViol,lViol] = Viols(gEval,l)gViol=[];lViol=[];for i = 1:length(gEval)if gEval(i)<0gViol(i)=gEval(i);lViol(i)=l(i);end    end
endfunction y = Penalty(fEval,gViol,lViol)sum = 0;y = fEval;for i = 1:length(gViol)sum = sum +  lViol(i)*abs(gViol(i));endy = y + sum;
endfunction y = UpdateH(H,dx,gamma)term1=(gamma*gamma') / (gamma'*dx);term2 = ((H*dx)*(dx'*H)) / (dx'*(H*dx));y = H + term1-term2;
end

最后输出结果为：

---------------------------------------------------------------
An implementation of Sequential Quadratic Programming method
in a nonlinear constrained optimization problem
---------------------------------------------------------------
SQP: Optimum point:x1=    0.4999x2=    0.7498iterations =         9

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