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From: "Michael Zhang" <cy_xiaoxiao@126.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: LMI term dimensions incompatible
Date: Fri, 9 May 2008 01:07:06 +0000 (UTC)
Organization: HeNan Institue of Finance &#38; Economics
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"Johan L?fberg" <loefberg@control.ee.ethz.ch> wrote in 
message <fvv38c$sug$1@fred.mathworks.com>...
> "Michael Zhang" <cy_xiaoxiao@126.com> wrote in message
> <fvv2lk$lua$1@fred.mathworks.com>...
> > "Johan L?fberg" <loefberg@control.ee.ethz.ch> wrote in 
> > message <fvuskh$mq9$1@fred.mathworks.com>...
> > > "Johan L?fberg" <loefberg@control.ee.ethz.ch> wrote in
> > > message <fvus3i$kci$1@fred.mathworks.com>...
> > > > "Johan L?fberg" <loefberg@control.ee.ethz.ch> wrote 
in
> > > > message <fvur63$oog$1@fred.mathworks.com>...
> > > > > "Michael Zhang" <cy_xiaoxiao@126.com> wrote in 
message
> > > > > <fvunbr$1tb$1@fred.mathworks.com>...
> > > > > > "Johan L?fberg" <loefberg@control.ee.ethz.ch> 
wrote 
> > in 
> > > > > > message <fvueq0$mf3$1@fred.mathworks.com>...
> > > > > > > Well, I won't directly answer your question, 
but 
> > when I 
> > > > > > see
> > > > > > > this, I can see that it must be annoying. My 
> > advice
> > > to you
> > > > > > > is to use more modern tools to setup and 
solve 
> > the SDP.
> > > > > > > 
> > > > > > > Using YALMIP and the solver SeDuMi
> > > > > > > 
http://control.ee.ethz.ch/~joloef/wiki/pmwiki.php
> > > > > > > 
http://control.ee.ethz.ch/~joloef/wiki/pmwiki.php?
> > > > > > n=Solvers.SEDUMI
> > > > > > > 
> > > > > > > you would code your problem as below. 
> > > > > > > 
> > > > > > > I think this approach would save you a lot of 
> > agony
> > > during
> > > > > > > the modelling.
> > > > > > > 
> > > > > > > /johan
> > > > > > > 
> > > > > > > 
> > > > > > > Q = sdpvar(n,n);
> > > > > > > S1 = sdpvar(n,n);
> > > > > > > S2 = sdpvar(n,n);
> > > > > > > 
> > > > > > > gammasquared = sdpvar(1,1);
> > > > > > > 
> > > > > > > A = randn(n,n);
> > > > > > > Ad = randn(n,n);
> > > > > > > Bd = randn(n,n);
> > > > > > > B1 = randn(n,n);
> > > > > > > B2 = randn(n,n);
> > > > > > > M = randn(n,n);
> > > > > > > Cd = randn(n,n);
> > > > > > > C = randn(n,n);
> > > > > > > D11 = randn(n,n);
> > > > > > > D12 = randn(n,n);
> > > > > > > Dd = randn(n,n);
> > > > > > > 
> > > > > > > 
> > > > > > > % We could do something like this, but then 
we 
> > would
> > > have 
> > > > > > to
> > > > > > > find out the
> > > > > > > % dimensions of the zero blocks, and we are 
too 
> > lazy to 
> > > > > > do that 
> > > > > > > %B = [ -Q+Bd*S2*Bd'+Ad*S1*Ad' 
A*Q+B2*M         
> > > > > > B1          
> > > > > > >        Bd*S2*Dd'+Ad*S1*Cd'  0       0;
> > > > > > > %  Q*A'+M'*B2'                -Q            
> > > > > > 0              
> > > > > > >     Q*C'+M'*D12'         M'      Q;
> > > > > > > %  B1'                         0          -
> > > > > > gammasquared*I  
> > > > > > >          D11'                0       0;
> > > > > > > %  Dd*S2*Bd'+Cd*S1*Ad'    C*Q+D12*M         
> > > > > > D11            
> > > > > > >   -I+Cd*S1*Cd'+Dd*S2*Dd' 0       0;
> > > > > > > %  0                      M                 
> > > > > > 0              
> > > > > > >      0                  -S2      0;
> > > > > > > %  0                      Q                 
> > > > > > 0              
> > > > > > >      0                   0      -S1 ];
> > > > > > > 
> > > > > > > % Instead, we use the blkvar shortcut. It 
> > automatically
> > > > > > > fills in the blanks and symmetrizes for you
> > > > > > > B = blkvar;
> > > > > > > B(1,1) =  -Q+Bd*S2*Bd'+Ad*S1*Ad';
> > > > > > > B(1,2) =  A*Q+B2*M;
> > > > > > > B(1,3) = B1;
> > > > > > > B(1,4) = Bd*S2*Dd'+Ad*S1*Cd';
> > > > > > > B(2,2) = -Q;
> > > > > > > B(2,4) = Q*C'+M'*D12';
> > > > > > > B(2,5) = M';
> > > > > > > B(2,6) = Q;
> > > > > > > B(3,3) = -gammasquared*eye(size(B1,2));
> > > > > > > B(3,4) = D11';
> > > > > > > B(4,4) = -eye(size(Cd,1))+Cd*S1*Cd'+Dd*S2*Dd';
> > > > > > > B(5,5) = -S2;
> > > > > > > B(6,6) = -S1;
> > > > > > > 
> > > > > > > % set up all constraints
> > > > > > > Constraints = [B < 0, Q>0, S1>0, S2>0];
> > > > > > > Objective = gammasquared;
> > > > > > > % Minimize gamma^2
> > > > > > > solvesdp(Constraints,Objective)
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > 
> > > > > > > "Michael Zhang" <cy_xiaoxiao@126.com> wrote 
in 
> > message
> > > > > > > <fvtuvp$aiv$1@fred.mathworks.com>...
> > > > > > > > Is anyone can help me? I can figure out 
what 
> > happened 
> > > > > > to my 
> > > > > > > > programm.
> > > > > > > > 
> > > > > > > > I need to solve a inequality:
> > > > > > > > 
> > > > > > > > | -Q+Bd*S2*Bd'+Ad*S1*Ad' A*Q+B2*M   B1
> > > > > > > > |      Q*A'+M'*B2'         -Q       0
> > > > > > > > |          B1'              0       -
gammar^2*I
> > > > > > > > |   Dd*S2*Bd'+Cd*S1*Ad'  C*Q+D12*M  D11
> > > > > > > > |          0                M       0
> > > > > > > > |          0                Q       0
> > > > > > > > 
> > > > > > > >     Bd*S2*Dd'+Ad*S1*Cd'     0       0  |
> > > > > > > >          Q*C'+M'*D12'       M'      Q  |
> > > > > > > >              D11'           0       0  |  <0
> > > > > > > >   -I+Cd*S1*Cd'+Dd*S2*Dd'    0       0  |
> > > > > > > >              0             -S2      0  |
> > > > > > > >              0              0      -S1 | 
> > > > > > > >   
> > > > > > > > Matrices:Q, S1, S2 are positive-define.
> > > > > > > > 
> > > > > > > > Below is programm:
> > > > > > > > 
> > ===================================================
> > > > > > > > A = [2 1; 0 1];
> > > > > > > > Ad = [0.2 0.1; 0 0.1];
> > > > > > > > B1 = [0.1 0.1]';
> > > > > > > > B2 = [1 1]';
> > > > > > > > Bd = [0.1 0.1]';
> > > > > > > > 
> > > > > > > > C = [1, 1];
> > > > > > > > Cd = [0.1, 0.1];
> > > > > > > > 
> > > > > > > > D11 = 0.1;
> > > > > > > > D12 = 1;
> > > > > > > > Dd = 0.1;
> > > > > > > > 
> > > > > > > > gammar = 1;
> > > > > > > > 
> > > > > > > > % Initial a LMI system
> > > > > > > > setlmis([]);
> > > > > > > > 
> > > > > > > > % Define Variables
> > > > > > > > 
> > > > > > > > % Q is a symmetric matrix, has a block size 
of 
> > 2 and 
> > > > > > this 
> > > > > > > > block is symmetric 
> > > > > > > > Q = lmivar(1, [2 1]);
> > > > > > > > 
> > > > > > > > % S1 a symmeric matrix, size 2 by 2
> > > > > > > > S1 = lmivar(1, [2 1]);
> > > > > > > > 
> > > > > > > > 
> > > > > > > > % S2 is 1 by 1 matrix
> > > > > > > > S2 = lmivar(1, [1 0]);
> > > > > > > > 
> > > > > > > > 
> > > > > > > > % Type of 2, size 1 by 2 
> > > > > > > > M = lmivar(2, [1 2]);
> > > > > > > > 
> > > > > > > > 
> > > > > > > > %I use upper triangular to represent the LMI
> > > > > > > > 
> > > > > > > > % pos in (1, 1)
> > > > > > > > lmiterm([1 1 1 Q], -1, 1);
> > > > > > > > lmiterm([1 1 1 S2], Bd, Bd');
> > > > > > > > lmiterm([1 1 1 S1], Ad, Ad');
> > > > > > > > 
> > > > > > > > % pos (1, 2)
> > > > > > > > lmiterm([1 1 2 Q], A, 1);
> > > > > > > > lmiterm([1 1 2 M], B2, 1);
> > > > > > > > 
> > > > > > > > % pos(1, 3)
> > > > > > > > lmiterm([1 1 3 0], B1);
> > > > > > > > 
> > > > > > > > % pos(1, 4)
> > > > > > > > lmiterm([1 1 4 S2], Bd, Bd');
> > > > > > > > lmiterm([1 1 4 S1], Ad, Cd');
> > > > > > > > 
> > > > > > > > 
> > > > > > > > % pos(2, 2)
> > > > > > > > lmiterm([1 2 2 Q], -1, 1);
> > > > > > > > 
> > > > > > > > % pos(2, 4)
> > > > > > > > lmiterm([1 2 4 Q], 1, C');
> > > > > > > > 
> > > > > > > > lmiterm([1 2 4 -M], 1, D12');
> > > > > > > > 
> > > > > > > > % pos(2, 5)
> > > > > > > > lmiterm([1 2 5 -M], 1, 1);
> > > > > > > > 
> > > > > > > > % pos(2, 6)
> > > > > > > > lmiterm([1 2 6 Q], 1, 1);
> > > > > > > > 
> > > > > > > > % pos(3, 3)
> > > > > > > > lmiterm([1 3 3 0], -(gammar^2));
> > > > > > > > 
> > > > > > > > % pos(3, 4)
> > > > > > > > lmiterm([1 3 4 0], D11');
> > > > > > > > 
> > > > > > > > % pos(4, 4)
> > > > > > > > lmiterm([1 4 4 0], -1);
> > > > > > > > lmiterm([1 4 4 S1], Cd, Cd');
> > > > > > > > lmiterm([1 4 4 S2], Dd, Dd');
> > > > > > > > 
> > > > > > > > % pos(5, 5)
> > > > > > > > lmiterm([1 5 5 S2], -1, 1);
> > > > > > > > 
> > > > > > > > 
> > > > > > > > % pos(6, 6)
> > > > > > > > lmiterm([1 6 6 S1], -1, 1);
> > > > > > > > 
> > > > > > > > lmis = getlmis;
> > > > > > > > 
> > > > > > > > [tmin, feas] = feasp(lmis)
> > > > > > > > 
> > > > > > > > --------------------------------
> > > > > > > > When I run it, I get error message:
> > > > > > > > 
> > > > > > > > ??? Error using ==> lmiterm at 296
> > > > > > > > lhs of LMI #1, block (4,1): term dimensions 
> > > > > > incompatible 
> > > > > > > > with
> > > > > > > > other terms in same row
> > > > > > > > 
> > > > > > > > Error in ==> paperor at 56
> > > > > > > > lmiterm([1 1 4 S1], Ad, Cd');
> > > > > > > > 
> > > > > > > > I have checked many times and can't figure 
out 
> > why it 
> > > > > > is 
> > > > > > > > wrong, can anybody point it to me?
> > > > > > > > 
> > > > > > > 
> > > > > > 
> > > > > > Dear Johan:
> > > > > > 
> > > > > > Thank you very much. But I still have some 
doubts 
> > about 
> > > > > > this.
> > > > > > 
> > > > > > You recommend me to use YALMIP, but I think the
> > > program you 
> > > > > > write for me is not apply to me.
> > > > > > 
> > > > > > 1.The original problem is for a discrete time 
linear
> > > delay 
> > > > > > system which I have't write it here, If there 
> > exists 
> > > > > > positive-define matrices Q, S1, S2 and a matrix 
M
> > > satisfied 
> > > > > > that LMI, then we call it H-inf quadratically 
stable
> > > with H-
> > > > > > inf norm bound gammar. So I mean gammar cannot 
be a
> > > random 
> > > > > > variable. So you write the program that object 
is 
> > to 
> > > > > > minimize gamma^2, I think it is not suitable.
> > > > > > 
> > > > > > 2.In my problem, matrices like A, Ad, Dd, they 
are 
> > all 
> > > > > > specified, you use randn(n,n) to generate them. 
I 
> > replace 
> > > > > > them with specified ones.
> > > > > > 
> > > > > 
> > > > > 
> > > > > I though gammar was a decision variable that you 
> > wanted to
> > > > > optimize. If it is given, well then you just use 
that
> > > > > instead and remove the objective from the call to 
> > solvesdp
> > > > > and solve the feasibility problem instead
> > > > > 
> > > > > You can of course use your own data, I only 
defined 
> > random
> > > > > matrices to get an example that was working.
> > > > > 
> > > > > The problem can be solved with YALMIP and SeDuMi 
> > without a
> > > > > doubt, it is a trivial SDP problem.
> > > > 
> > > > 
> > > > ..and I noticed you optimized over M too, so you 
should 
> > use
> > > > 
> > > > M = sdpvar(somesize,someothersize,'full')
> > > > 
> > > > 
> > > 
> > > To summarize
> > > 
> > > A = [2 1; 0 1];
> > > Ad = [0.2 0.1; 0 0.1];
> > > B1 = [0.1 0.1]';
> > > B2 = [1 1]';
> > > Bd = [0.1 0.1]';
> > > 
> > > C = [1, 1];
> > > Cd = [0.1, 0.1];
> > > 
> > > D11 = 0.1;
> > > D12 = 1;
> > > Dd = 0.1;
> > > 
> > > gammar = 1;
> > > 
> > > Q = sdpvar(2,2);
> > > S1 = sdpvar(2,2);
> > > S2 = sdpvar(1);
> > > % Full by definition, but just to rmember it if 
> > > % we change dimensions later
> > > M = sdpvar(1,2,'full');
> > > 
> > > B = blkvar;
> > > B(1,1) = -Q+Bd*S2*Bd'+Ad*S1*Ad';
> > > B(1,2) = A*Q+B2*M;
> > > B(1,3) = B1;
> > > B(1,4) = Bd*S2*Dd'+Ad*S1*Cd';
> > > B(2,2) = -Q;
> > > B(2,4) = Q*C'+M'*D12';
> > > B(2,5) = M';
> > > B(2,6) = Q;
> > > B(3,3) = -gammar^2*eye(size(B1,2));
> > > B(3,4) = D11';
> > > B(4,4) = -eye(size(Cd,1))+Cd*S1*Cd'+Dd*S2*Dd';
> > > B(5,5) = -S2;
> > > B(6,6) = -S1;
> > > 
> > > % set up all constraints
> > > Constraints = [B < 0, Q>0, S1>0, S2>0];
> > > solvesdp(Constraints)
> > >     yalmiptime: 0.3430
> > >     solvertime: 1.3440
> > >           info: 'No problems detected (SeDuMi-1.1)'
> > >        problem: 0
> > >         dimacs: [1.4589e-015 0 0 0 7.3848e-016 
3.9007e-
> > 015]
> > > 
> > > 
> > > >> double(Q)
> > > 
> > > ans =
> > > 
> > >     0.2425   -0.2091
> > >    -0.2091    1.0081
> > > 
> > > >> double(S1)
> > > 
> > > ans =
> > > 
> > >     1.3613   -0.4978
> > >    -0.4978    2.3019
> > > 
> > > >> double(S2)
> > > 
> > > ans =
> > > 
> > >     2.0517
> > > 
> > > >> double(M)
> > > 
> > > ans =
> > > 
> > >    -0.1367   -0.6933
> > > 
> > > 
> > > 
> > This LMI example is what I read from a paper, but the 
> > author's result is different form yours. And he gave 
> > proofs, I think the author only use matlab and we use 
other 
> > toolboxes. I doubt if there really exists result 
difference 
> > because of toolboxes?
> > 
> > The paper's address is 
http://www.sciencedirect.com/science?
> > _ob=ArticleURL&_udi=B6V21-3WWV8W6-
B&_user=10&_coverDate=08%
> > 2F31%
> > 
2F1999&_alid=736573613&_rdoc=1&_fmt=high&_orig=search&_cdi=5
> > 
689&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000050221&_versi
> > 
on=1&_urlVersion=0&_userid=10&md5=8ecef13fb66b50ddc4a3990637
> > 4c61b7
> 
> 
> The solution is not unique since you only solve a
> feasibility problem
> 
> 
Thank you very much. According to this, I have a lot to 
learn.