在求解如下凸优化问题中遇到了问题:
1.不知道如何编程得到最优的γ²
2.求解提示为没有可行解
是否是我退出的不等式有问题 或者其他方面有问题
A=[0,1,0,-1;-882,-28.4,0,28.4;0,0,0,1;1696.15,54.62,-657.12,-2680.39;];
B=[0,0,0,-0.039]';
Bw=[0,0,-1,2625.77]';
C1=[-882,-28.4,0,28.4];
C2=[0,0,10,0];
Ea=[0,0,-65.71,262.58]
%Eb=0
L=[0,0,0,1]'
I=eye(1)
gam=4.2; %γ=gam
a=0.01; %ρ=a
b=0.1; %ε=b
c=inv(b);
umax=3000
setlmis([]);
X=lmivar(1,[4 1]); %定义决策变量
Z=lmivar(2,[1 4]);
lmiterm([1 1 1 X],A,1,'s');
lmiterm([1 1 1 Z],B,1,'s');
lmiterm([1 1 2 0],L);
lmiterm([1 1 3 X],1,Ea'); %Ea→Ea'
lmiterm([1 1 4 0],Bw);
lmiterm([1 1 5 X],1,C1');
lmiterm([1 2 2 0],-c);
lmiterm([1 3 3 0],-b);
lmiterm([1 4 4 0],-gam^2);
lmiterm([1 5 5 0],-1);
lmiterm([-2 1 1 X],1,1);
lmiterm([3 1 1 0],-1);
lmiterm([3 1 2 Z],0.95,1);
lmiterm([3 2 2 X],-3000,1);
lmiterm([4 1 1 0],-1);
lmiterm([4 1 2 X],C2,0.95);
lmiterm([4 2 2 X],-1,1);
lmisys=getlmis; %完成LTI框架的设设置
[tmin,xfeas]=feasp(lmisys); %求解可行解问题
X=dec2mat(lmisys,xfeas,X); %提取解矩阵 把决策变量转化为矩阵形式
Z=dec2mat(lmisys,xfeas,Z);
P=inv(X);
K=Z*P
运行提示为:
Solver for LMI feasibility problems L(x) < R(x)
This solver minimizes t subject to L(x) < R(x) + t*I
The best value of t should be negative for feasibility
Iteration : Best value of t so far
1 2487.161836
2 1661.789005
3 1200.565677
4 542.424422
5 311.999933
6 311.999933
7 279.917289
8 279.917289
*** new lower bound: 36.447556
Result: best value of t: 279.917289
f-radius saturation: 0.000% of R = 1.00e+009
These LMI constraints were found infeasible
K =
2.0861 0.1139 -16.8718 3.5340
麻烦各位指出其中有错误的地方 不胜感激!
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