[],lb,ub,@mycon,options) 在 Command Window 中,输入 youhua 回车 得到程序结果为: youhua max Directional First-order Iter F-count f(x) constraint Step-size derivative optimality Procedure 0 3 -8 1 Infeasible start point 1 7 -7.7037 0.06173 1 0.37 0.83 2 11 -7.67725 0.0003061 1 0.0268 ...
该命令求解目标函数fun的最小值和相应的x值，X0为x的初始值，fval为返回的函数值，exitflag=1表示优化结果收敛，exitflag=0 表示超过了最大迭代次数。返回值output有3个分量，其中iterations是优化过程中的迭代次数，funcCount是代入函数值的次数，algorithm是优化所采用的算法。options是一个结构，里面有...
fmincon解决的优化模型如下：min F(X)subject to: A*X <= B (线性不等式约束)Aeq*X = Beq (线性等式约束)C(X) <= 0 (非线性不等式约束)Ceq(X) = 0 (非线性等式约束)LB <= X <= UB （参数x的取值范围）x = fmincon(fun,x0,A,b,Aeq,beq,lb,ub,nonlcon,options)fmincon是求解目标fun最小...
目标函数的文件为：functionf=objfun(x)f=exp(x(1))*(4*x(1)^2+2*x(2)^2+4*x(1)*x(2)+2*x(2)+1); 约束条件的文件为：function[c,ceq]=confun(x)c=[1.5+x(1)*x(2)-x(1)-x(2);-x(1)*x(2)-10];%表示不等式非线性约束ceq=[];%表示等式非线性约束 优化的程序如下： clear...
fmincon 函数优化问题 fmincon 解决的优化模型如下: min F(X) subject to: A*X <= B (线性不等式约束) Aeq*X = Beq (线性等式约束..
f g Hinfo fun x HessMult Y is a matrix that has the same number of rows as there are dimensions in the problem W H Y although H is not formed explicitly fminunc uses Hinfo to compute the preconditioner The optional parametersp p can be any additional parameters needed by hmfun See ...
1.非线性规划的形式: 其中x是一个列向量,st中前两项为线性约束条件,后两项为非线性约束条件。 在MATLAB中fmincon是用于求解非线性多远函数的最小值的函数,这里介绍fmincon的其中一种语法格式: [x,fval,exitflag,output]=fmincon[目标函数f(x)调用,
fmincon Find a minimum of a constrained nonlinear multivariable function subject to where x, b, beq, lb, and ub are vectors, A and Aeq are matrices, c(x) and ceq(x) are functions that return vectors, and f(x) is a function that returns a scalar. f(x), c(x), and ceq(x) can...
f(x), c(x), and ceq(x) can be nonlinear functions. x, lb, and ub can be passed as vectors or matrices; see Matrix Arguments. x = fmincon(fun,x0,A,b) starts at x0 and attempts to find a minimizer x of the function described in fun subject to the linear inequalities A*x ≤...
例如,可以定义一个函数文件myfun.m,其中包含以下内容:function f = myfun(x) f = x(1)^2 + x(2)^2; endfunction [c,ceq] = mycon(x) c = x(1)^2 + x(2)^2 - 1; % 不等式约束 ceq = []; % 等式约束 endoptions = optimset('MaxIter', 100); 优化变量的初始值x0:需要根据具体问题...