I have been doing some experiments to see how the number of function evaluations (F-count) change depending on the type of gradient information that is passed (objective gradient vs. constraint gradient). Here are the results of a test case (decision space: 60 variables) alongwith the time ...
参数说明 x的返回值是决策向量x的取值,fval的返回值是目标函数f(x)的取值。 fun是用M文件定义的函数f(x),代表了(非)线性目标函数。 x0是x的初始值。 A,b,Aeq,beq定义了线性约束,如果没有线性约束,则A=[],b=[],Aeq=[],beq=[]。 lb和ub是变量x的下界和上界,如果下界和上界没有约束,则lb=[],ub...
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 0.0149 3 15 -7.67712 7.682e-009 1 0.00013 4 7.35e-007 Optimization terminated: first-order optimality measure less than...
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 ≤...
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,
funcCount: 13 algorithm: 'golden section search, parabolic interpolation'（二）无约束非线性多变量优化问题 这里我们介绍两个命令：fminsearch()和fminunc()，前者适合处理阶次低但是间断点多的函数，后者则对于高阶连续的函数比较有效。命令fminsearch()的格式为：X= fminsearch(fun,X0)[X,fval,...
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 0.0149 3 15 -7.67712 7.682e-009 1 0.000134 7.35e-007 Optimization terminated: first-order optimality measure less ...
Iter F-count f(x) Feasibility optimality step 0 31 3.000000e+01 0.000e+00 2.000e+00 1 63 4.263256e-13 0.000e+00 2.533e-07 5.477e+00 Local minimum found that satisfies the constraints. First-order Norm of Iter F-count f(x) Feasibility optimality step 0 301 3.000000e+02 0.000e+00 2....
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...
x = 286.7422 267.6533 fval = 4.1273e+003 exitflag = 0 output = iterations: 16 funcCount: 202 lssteplength: 4.8828e-004 stepsize: 0.0294 algorithm: 'medium-scale: SQP, Quasi-Newton, line-search'firstorderopt: 223.6754 constrviolation: 16.1374 message: [1x145 char]不过...