Talk:Beginning with CVX

From Wikimization

(Difference between revisions)
Jump to: navigation, search
(New page: <pre> lamda_W=eig(full(W)) </pre>)
Line 1: Line 1:
<pre>
<pre>
lamda_W=eig(full(W))
lamda_W=eig(full(W))
 +
</pre>
 +
--------------------------------------------------------------------
 +
Thanks for the idea<pre>full(W)</pre> it's great, it works!!! Thank you very much :D.
 +
 +
I have an answer, how to calculate the normalized eigenvector.
 +
 +
Maybe? <pre>v_W=eig(full(W))/max(eig(full(W)))</pre>
 +
 +
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:
 +
 +
<pre>
 +
 +
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
</pre>
</pre>

Revision as of 23:32, 3 February 2009

lamda_W=eig(full(W))
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
Retrieved from "http://www.convexoptimization.com/wikimization/index.php/Talk:Beginning_with_CVX"
Personal tools