Talk:Beginning with CVX
From Wikimization
(Difference between revisions)
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</pre> | </pre> | ||
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- | Thanks for the | + | Thanks for the ideas it's great. Thank you very much :D. |
I have an answer, how to calculate the normalized eigenvector. | I have an answer, how to calculate the normalized eigenvector. | ||
- | Maybe? <pre>v_W=eig(full(W))/ | + | Maybe? <pre>[v_W]=eig(full(W))/norm ....</pre> |
- | + | I've changed Epsilon1, Epsilon2, they aren't a variable, I think they are constants. | |
- | + | I'm going to see how to initialice (I'm going to research in the references of my article (Cross fingers) | |
Thanks a lot again. | Thanks a lot again. | ||
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R=(zeros(2,4)) | R=(zeros(2,4)) | ||
- | + | H=W*A_a'+A_a*W-B_a*R-R'*B_a' | |
- | + | lamda_W=min(eig(full(W))) | |
- | + | lamda_H=max(eig(H)) | |
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- | + | Epsilon1=1; | |
- | + | Epsilon2=1; | |
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- | lamda_W= | + | if(lamda_W>=Epsilon1) |
- | lamda_H=eig( | + | if(lamda_H<=-Epsilon2) para=1 |
- | v_W=eig(full(W))/ | + | else para = 0 |
- | v_H=eig(H)/max(eig(H)) | + | end |
+ | else para =0 | ||
+ | end | ||
+ | |||
+ | |||
+ | %v_W=eig(W)/(abs(eig(W))) | ||
+ | %v_W=eig(full(W))/abs(full(W))%%normalized eigenvector :| | ||
+ | %v_H=eig(H)/max(eig(H)) | ||
- | para=0 %STOP | ||
while para==0 | while para==0 | ||
+ | |||
+ | [v_W,D] = eig(W) | ||
+ | [v_H,D] = eig(H) | ||
if ( Epsilon1 - lamda_W )>(lamda_H+Epsilon2) | if ( Epsilon1 - lamda_W )>(lamda_H+Epsilon2) | ||
cvx_begin | cvx_begin | ||
- | + | ||
- | + | variables p1 p2 W(4,4) R(2,4) | |
- | + | ||
- | variables p1 p2 | + | |
minimize (p1+p2) | minimize (p1+p2) | ||
subject to | subject to | ||
- | + | ||
W(1,1)<=p1 | W(1,1)<=p1 | ||
W(2,2)<=p1 | W(2,2)<=p1 | ||
+ | W(1,1)>=Epsilon1 | ||
+ | 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) | ||
+ | |||
+ | 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) | |
- | + | ||
- | + | H=W*A_a'+A_a*W-B_a*R-R'*B_a' | |
- | + | ||
- | + | W - Epsilon1*eye(2*n) == semidefinite(2*n); | |
- | + | Epsilon2*eye(2*n) + H == -semidefinite(2*n); | |
- | + | ||
- | + | ||
- | W | + | |
- | + | ||
- | v_W'*W* | + | v_W'*W*v_W>=Epsilon1*eye(4) |
cvx_end | cvx_end | ||
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cvx_begin | cvx_begin | ||
- | |||
- | H=W*A_a'+A_a*W-B_a*R-R'*B_a' | ||
- | variables p1 p2 | + | variables p1 p2 W(4,4) R(2,4) |
minimize (p1+p2) | minimize (p1+p2) | ||
subject to | subject to | ||
- | + | ||
+ | W(1,1)>=Epsilon1 | ||
+ | W(2,2)>=Epsilon1 | ||
+ | |||
W(1,1)<=p1 | W(1,1)<=p1 | ||
W(2,2)<=p1 | W(2,2)<=p1 | ||
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W(4,4)==W(2,2) | W(4,4)==W(2,2) | ||
- | + | ||
R(1,1)>=-p2 | R(1,1)>=-p2 | ||
R(1,1)<=p2 | R(1,1)<=p2 | ||
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R(1,2)<=p2 | R(1,2)<=p2 | ||
R(2,4)==R(1,2) | R(2,4)==R(1,2) | ||
- | + | ||
- | + | H=W*A_a'+A_a*W-B_a*R-R'*B_a' | |
- | W | + | |
- | + | ||
- | v_H'* | + | W - Epsilon1*eye(2*n) == semidefinite(2*n); |
+ | Epsilon2*eye(2*n) + H == -semidefinite(2*n); | ||
+ | |||
+ | v_H'*H*v_H<=-Epsilon2*eye(4) | ||
cvx_end | cvx_end | ||
end | end | ||
- | lamda_W=eig(full(W)) | + | lamda_W=min(eig(full(W))) |
- | lamda_H=eig(H) | + | lamda_H=max(eig(H)) |
- | v_W=eig(full(W))/ | + | |
- | v_H=eig(H)/ | + | % v_W=eig(full(W))/max(eig(full(W)))%%Cálculo del normalized eigenvector |
+ | % v_H=eig(H)/max(eig(H)) | ||
%STOP | %STOP | ||
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R | R | ||
W | W | ||
- | K=R/W | + | K=R/W</pre> |
- | </pre> | + |
Revision as of 09:46, 4 February 2009
lamda_W=eig(full(W))
Thanks for the ideas it's great. Thank you very much :D.
I have an answer, how to calculate the normalized eigenvector.
Maybe?[v_W]=eig(full(W))/norm ....
I've changed Epsilon1, Epsilon2, they aren't a variable, I think they are constants.
I'm going to see how to initialice (I'm going to research in the references of my article (Cross fingers)
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)) 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=1; Epsilon2=1; if(lamda_W>=Epsilon1) if(lamda_H<=-Epsilon2) para=1 else para = 0 end else para =0 end %v_W=eig(W)/(abs(eig(W))) %v_W=eig(full(W))/abs(full(W))%%normalized eigenvector :| %v_H=eig(H)/max(eig(H)) while para==0 [v_W,D] = eig(W) [v_H,D] = eig(H) if ( Epsilon1 - lamda_W )>(lamda_H+Epsilon2) cvx_begin variables p1 p2 W(4,4) R(2,4) 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) H=W*A_a'+A_a*W-B_a*R-R'*B_a' W - Epsilon1*eye(2*n) == semidefinite(2*n); Epsilon2*eye(2*n) + H == -semidefinite(2*n); v_W'*W*v_W>=Epsilon1*eye(4) cvx_end else cvx_begin variables p1 p2 W(4,4) R(2,4) minimize (p1+p2) subject to W(1,1)>=Epsilon1 W(2,2)>=Epsilon1 W(1,1)<=p1 W(2,2)<=p1 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) H=W*A_a'+A_a*W-B_a*R-R'*B_a' W - Epsilon1*eye(2*n) == semidefinite(2*n); Epsilon2*eye(2*n) + H == -semidefinite(2*n); v_H'*H*v_H<=-Epsilon2*eye(4) cvx_end end lamda_W=min(eig(full(W))) lamda_H=max(eig(H)) % v_W=eig(full(W))/max(eig(full(W)))%%Cálculo del normalized eigenvector % v_H=eig(H)/max(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