怎么解读这个非线性优化的结果。

下列非线性优化中（其中objfun和confun1分别为目标函数），

问题一：结果output中algorithm: 'medium-scale: SQP, Quasi-Newton, line-search'，其中的拟牛顿法是BFGS还是DFP？

问题二：结果exitflag =0的意思是最大迭代次数不够吗，有没有方法增加迭代次数？

谢谢！

命令：

x0 = [-0.657,0.0064,0]; % Make a starting guess at the solution

options = optimset('Algorithm','active-set');

[x,fval,exitflag,output,lambda,grad,hession]=fmincon(@objfun,x0,[],[],[],[],[],[],@confun1,options)

计算结果如下：

Solver stopped prematurely.

fmincon stopped because it exceeded the function evaluation limit,

options.MaxFunEvals = 300 (the default value).

x =

-0.8194 0.0108 -0.0122

fval =

0.2623

exitflag =

0

output =

iterations: 18

funcCount: 303

lssteplength: 4.8828e-04

stepsize: 0.0083

algorithm: 'medium-scale: SQP, Quasi-Newton, line-search'

firstorderopt: 100

constrviolation: 3.5892e+13

message: [1x142 char]

lambda =

lower: [3x1 double]

upper: [3x1 double]

eqlin: [0x1 double]

eqnonlin: [2x1 double]

ineqlin: [0x1 double]

ineqnonlin: [3x1 double]

grad =

1

100

0

hession =

0.0072 0.0267 -0.0747

0.0267 0.9992 0.0369

-0.0747 0.0369 13.5853

Answer 1

[gacot]

问题1： Active-Set Optimization

fmincon uses a sequential quadratic programming (SQP) method. In thismethod, the function solves a quadraticprogramming (QP) subproblem at each iteration. fmincon updatesan estimate of the Hessian of the Lagrangian at each iteration usingthe BFGS formula

问题2：

MaxFunEvals 默认值是100*变量个数，所以你的是300. 你可以通过 optimset 来设置更大的数值，比如

options = optimset('Algorithm','active-set','MaxFunEvals',1000);

如果运行后又提示其他参数（TolX，MaxIter，TolFun）太小，继续用optimset 设置