[37] | 1 | |
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| 2 | function [At,c,K,yfeas,y,info] = lmiranktest |
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| 3 | % [At,c,K,yfeas,y,info] = lmiranktest; |
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| 4 | % |
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| 5 | % LMIRANKTEST runs a test problem for LMIRANK. It uses randomly generated |
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| 6 | % feasible rank constrained LMI problems created by CREATERANDOMDATA. |
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| 7 | %
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| 8 | % Problem parameters:
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| 9 | % nLP : no. of LP inequalities. This can be 0.
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| 10 | % nF : the Fi matrices are size nF x nF
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| 11 | % nG : the Gi matrices are size nG x nG
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| 12 | % r : rank bound
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| 13 | % m : number of variables
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| 14 | % maxiter : max. no. of iterations
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| 15 | %
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| 16 | % Outputs: (LP ineq. data at the start of At and c is not shown)
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| 17 | % At = -[vec(F1),...,vec(Fm);
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| 18 | % vec(G1),...,vec(Gm)]
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| 19 | % c = [vec(F0);
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| 20 | % vec(G0)]
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| 21 | % yfeas : known feasible solution
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| 22 | % y : calculated solution |
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| 23 | % info.solved : 1 if a solution was found, 0 otherwise |
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| 24 | % info.cpusec : solution time |
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| 25 | % info.iters : no. of iterations required to find a solution
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| 26 | % info.gap : constraint gap |
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| 27 | % info.rank : ranks (with respect to tolerance pars.eps) |
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| 28 | % |
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| 29 | % See also LMIRANK, CREATERANDOMDATA. |
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| 30 | |
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| 31 | % Author Robert Orsi |
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| 32 | % Feb 2005 |
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| 33 | |
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| 34 |
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| 35 | %%%% Random problem parameters
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| 36 | nLP=3;
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| 37 | nF=10;
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| 38 | nG=10;
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| 39 | r=5;
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| 40 | m=20;
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| 41 |
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| 42 | %%%% Max. no. of iterations
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| 43 | maxiter=100;
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| 44 |
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| 45 | %%%% Create random data
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| 46 | [At,c,K,yfeas] = createrandomdata(nF,nG,r,m);
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| 47 | %%%% Add LP ineq. constraints
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| 48 | At=[rand(nLP,m); At];
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| 49 | c=[(At(1:nLP,:)*yfeas+max(randn(nLP,1),0)); c];
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| 50 | K.l=nLP;
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| 51 |
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| 52 | %%%% Call LMIRank |
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| 53 | pars.maxiter=maxiter;
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| 54 | [y,info] = lmirank(At,c,K,pars);
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| 55 |
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