Talk:Beginning with CVX

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</pre>
</pre>
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Thanks for the idea<pre>full(W)</pre> it's great, it works!!! Thank you very much :D.
 
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I have an answer, how to calculate the normalized eigenvector.
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Thanks a lot for all the ideas, they all are greats.
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Maybe? <pre>v_W=eig(full(W))/max(eig(full(W)))</pre>
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The code still not working. I think I’m making thinks wrong (sure). I have a dude.
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And... how does i have to undersand the result?
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The new code could be:
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In the tutorial don't explain anything, or, with type K in Matlab(is the variable I want to know) thats all?
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Thanks a lot again.
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Here is the new code:
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<pre>
<pre>
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%0)Initialization
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clear all;
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clear all;
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%cvx_setup
n=2; m=1;
n=2; m=1;
A_a=3*eye(2*n,2*n)
A_a=3*eye(2*n,2*n)
B_a=4*eye(2*n,2*m)
B_a=4*eye(2*n,2*m)
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 +
%1)1
W=eye(4)
W=eye(4)
R=(zeros(2,4))
R=(zeros(2,4))
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cvx_begin
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%2)2
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H=W*A_a'+A_a*W-B_a*R-R'*B_a'
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expression K(2*m,2*n)
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lamda_W=min(eig(full(W)))
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lamda_H=max(eig(H))
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H=W*A_a'+A_a*W-B_a*R-R'*B_a'
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Epsilon1=1e-6;
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Epsilon2=1e-6;
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%3)3
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[v_W,D] = eig( full ( W ) )
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[v_H,D] = eig( full ( H ) )
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variables p1 p2 Epsilon1 Epsilon2 W(4,4) R(2,4)
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cvx_begin
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variables p1 p2 W(4,4) R(2,4)
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minimize (p1+p2)
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if ( Epsilon1 - lamda_W )>(lamda_H+Epsilon2) CK=v_W'*W*v_W - Epsilon1*eye(2*n) ;
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else CK=-Epsilon2*eye(2*n) - v_H'*H*v_H ;
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subject to
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end
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for p=1:2
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W(1,1)<=p1
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W(2,2)<=p1
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W(3,3)==W(1,1)
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W(4,4)==W(2,2)
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for q=1
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R(1,1)>=-p2
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R(1,1)<=p2
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R(2,3)==R(1,1)
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R(1,2)>=-p2
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R(1,2)<=p2
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R(2,4)==R(1,2)
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end
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end
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W>=Epsilon1*eye(2*n,2*n)
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H<=-Epsilon2*eye(2*n,2*n)
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cvx_end
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minimize (p1+p2)
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lamda_W=eig(full(W))
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lamda_H=eig(H)
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v_W=eig(full(W))/max(eig(full(W)))%%normalized eigenvector :|
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v_H=eig(H)/max(eig(H))
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para=0 %STOP
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while para==0
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if ( Epsilon1 - lamda_W )>(lamda_H+Epsilon2)
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subject to
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cvx_begin
 
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H=W*A_a'+A_a*W-B_a*R-R'*B_a'
 
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variables p1 p2 Epsilon1 Epsilon2 W(4,4) R(2,4)
 
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minimize (p1+p2)
 
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subject to
 
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for p=1:2
 
W(1,1)<=p1
W(1,1)<=p1
W(2,2)<=p1
W(2,2)<=p1
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W(1,1)>=Epsilon1
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W(2,2)>=Epsilon1
W(3,3)==W(1,1)
W(3,3)==W(1,1)
W(4,4)==W(2,2)
W(4,4)==W(2,2)
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for q=1
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R(1,1)>=-p2
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R(1,1)>=-p2
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R(1,1)<=p2
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R(1,1)<=p2
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R(2,3)==R(1,1)
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R(2,3)==R(1,1)
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R(1,2)>=-p2
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R(1,2)<=p2
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R(1,2)>=-p2
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R(2,4)==R(1,2)
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R(1,2)<=p2
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R(2,4)==R(1,2)
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CK == semidefinite(2*n);
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end
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end
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W>=Epsilon1*eye(2*n,2*n)
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H<=-Epsilon2*eye(2*n,2*n)
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v_W'*W*v_w>=Epsilon1
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cvx_end
cvx_end
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else
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lamda_W = min ( eig ( full ( W ) ) )
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lamda_H = max ( eig ( full ( H ) ) )
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cvx_begin
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H=W*A_a'+A_a*W-B_a*R-R'*B_a'
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variables p1 p2 Epsilon1 Epsilon2 W(4,4) R(2,4)
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minimize (p1+p2)
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subject to
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for p=1:2
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W(1,1)<=p1
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W(2,2)<=p1
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W(3,3)==W(1,1)
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W(4,4)==W(2,2)
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for q=1
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R(1,1)>=-p2
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R(1,1)<=p2
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R(2,3)==R(1,1)
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R(1,2)>=-p2
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R(1,2)<=p2
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R(2,4)==R(1,2)
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end
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end
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W>=Epsilon1*eye(2*n,2*n)
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H<=-Epsilon2*eye(2*n,2*n)
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v_H'*W*v_H<=-Epsilon2
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cvx_end
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end
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lamda_W=eig(full(W))
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lamda_H=eig(H)
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v_W=eig(full(W))/min(eig(full(W)))%%Cálculo del normalized eigenvector
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v_H=eig(H)/min(eig(H))
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%STOP
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if(lamda_W>=Epsilon1)
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if(lamda_H<=-Epsilon2) para=1
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else para = 0
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end
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else para =0
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end
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end
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R
R
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W
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W=full(W)
K=R/W
K=R/W
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</pre>
</pre>

Current revision

lamda_W=eig(full(W))

Thanks a lot for all the ideas, they all are greats.

The code still not working. I think I’m making thinks wrong (sure). I have a dude.

The new code could be: 

%0)Initialization
clear all;

%cvx_setup
n=2; m=1;

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

%1)1
W=eye(4)
R=(zeros(2,4))

%2)2
H=W*A_a'+A_a*W-B_a*R-R'*B_a'

lamda_W=min(eig(full(W)))
lamda_H=max(eig(H))
Epsilon1=1e-6;
Epsilon2=1e-6;
%3)3
[v_W,D] = eig( full ( W ) )
[v_H,D] = eig( full ( H ) )
    
    cvx_begin
    variables p1 p2 W(4,4) R(2,4)
    if ( Epsilon1 - lamda_W )>(lamda_H+Epsilon2)    CK=v_W'*W*v_W - Epsilon1*eye(2*n) ;
    else CK=-Epsilon2*eye(2*n) - v_H'*H*v_H ;
    end
        
        minimize (p1+p2)
    
        subject to

            W(1,1)<=p1
            W(2,2)<=p1
            W(1,1)>=Epsilon1
            W(2,2)>=Epsilon1
            W(3,3)==W(1,1)
            W(4,4)==W(2,2)

            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)
     
            CK  == semidefinite(2*n);                  

cvx_end

lamda_W = min ( eig ( full ( W ) ) )
lamda_H = max ( eig ( full ( H ) ) )

R
W=full(W)
K=R/W
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