Talk:Beginning with CVX

From Wikimization

lamda_W=eig(full(W))

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Thanks for the idea

full(W)

it's great, it works!!! Thank you very much :D.

I have an answer, how to calculate the normalized eigenvector.

Maybe?

v_W=eig(full(W))/max(eig(full(W)))

And... how does i have to undersand the result?

In the tutorial don't explain anything, or, with type K in Matlab(is the variable I want to know) thats all?

Thanks a lot again.

Here is the new code:

clear all;
n=2; m=1;

A_a=3*eye(2*n,2*n)
B_a=4*eye(2*n,2*m) 
W=eye(4)
R=(zeros(2,4))

 cvx_begin

    expression K(2*m,2*n) 
  
    H=W*A_a'+A_a*W-B_a*R-R'*B_a'
    
    variables p1 p2 Epsilon1 Epsilon2 W(4,4) R(2,4)
    
    minimize (p1+p2)
    
    subject to
        for p=1:2
            W(1,1)<=p1
            W(2,2)<=p1
            W(3,3)==W(1,1)
            W(4,4)==W(2,2)

            for q=1
                R(1,1)>=-p2
                R(1,1)<=p2
                R(2,3)==R(1,1)
                
                R(1,2)>=-p2
                R(1,2)<=p2
                R(2,4)==R(1,2)
             end
        end
        W>=Epsilon1*eye(2*n,2*n)
        H<=-Epsilon2*eye(2*n,2*n)
        
cvx_end

lamda_W=eig(full(W))
lamda_H=eig(H)
v_W=eig(full(W))/max(eig(full(W)))%%normalized eigenvector :|
v_H=eig(H)/max(eig(H))

para=0 %STOP

while para==0
    
if ( Epsilon1 - lamda_W )>(lamda_H+Epsilon2)

    cvx_begin

    H=W*A_a'+A_a*W-B_a*R-R'*B_a'
    
    variables p1 p2 Epsilon1 Epsilon2 W(4,4) R(2,4)
    
    minimize (p1+p2)
    
    subject to
        for p=1:2
            W(1,1)<=p1
            W(2,2)<=p1
            W(3,3)==W(1,1)
            W(4,4)==W(2,2)

            for q=1
                R(1,1)>=-p2
                R(1,1)<=p2
                R(2,3)==R(1,1)
                
                R(1,2)>=-p2
                R(1,2)<=p2
                R(2,4)==R(1,2)
             end
        end
        W>=Epsilon1*eye(2*n,2*n)
        H<=-Epsilon2*eye(2*n,2*n)
        
        v_W'*W*v_w>=Epsilon1

cvx_end

else 

    cvx_begin

    H=W*A_a'+A_a*W-B_a*R-R'*B_a'
    
    variables p1 p2 Epsilon1 Epsilon2 W(4,4) R(2,4)
    
    minimize (p1+p2)
    
    subject to
        for p=1:2
            W(1,1)<=p1
            W(2,2)<=p1
            W(3,3)==W(1,1)
            W(4,4)==W(2,2)

            for q=1
                R(1,1)>=-p2
                R(1,1)<=p2
                R(2,3)==R(1,1)
                
                R(1,2)>=-p2
                R(1,2)<=p2
                R(2,4)==R(1,2)
            end
        end
        W>=Epsilon1*eye(2*n,2*n)
        H<=-Epsilon2*eye(2*n,2*n)
        
        v_H'*W*v_H<=-Epsilon2

cvx_end
end

lamda_W=eig(full(W))
lamda_H=eig(H)
v_W=eig(full(W))/min(eig(full(W)))%%Cálculo del normalized eigenvector
v_H=eig(H)/min(eig(H))

%STOP
if(lamda_W>=Epsilon1)
    if(lamda_H<=-Epsilon2)     para=1
    else para = 0
    end
else para =0
end
        
end

R
W
K=R/W