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Aide Scilab >> Optimisation et Simulation > lmisolver

lmisolver

linear matrix inequation solver

Calling Sequence

[XLISTF[,OPT]] = lmisolver(XLIST0,evalfunc [,options])

Arguments

XLIST0

a list of containing initial guess (e.g. XLIST0=list(X1,X2,..,Xn))

evalfunc

a Scilab function ("external" function with specific syntax)

The syntax the function evalfunc must be as follows:

[LME,LMI,OBJ]=evalfunct(X) where X is a list of matrices, LME, LMI are lists and OBJ a real scalar.

XLISTF

a list of matrices (e.g. XLIST0=list(X1,X2,..,Xn))

options

optional parameter. If given, options is a real row vector with 5 components [Mbound,abstol,nu,maxiters,reltol]

Description

lmisolver solves the following problem:

minimize f(X1,X2,...,Xn) a linear function of Xi's

under the linear constraints: Gi(X1,X2,...,Xn)=0 for i=1,...,p and LMI (linear matrix inequalities) constraints:

Hj(X1,X2,...,Xn) > 0 for j=1,...,q

The functions f, G, H are coded in the Scilab function evalfunc and the set of matrices Xi's in the list X (i.e. X=list(X1,...,Xn)).

The function evalfun must return in the list LME the matrices G1(X),...,Gp(X) (i.e. LME(i)=Gi(X1,...,Xn), i=1,...,p). evalfun must return in the list LMI the matrices H1(X0),...,Hq(X) (i.e. LMI(j)=Hj(X1,...,Xn), j=1,...,q). evalfun must return in OBJ the value of f(X) (i.e. OBJ=f(X1,...,Xn)).

lmisolver returns in XLISTF, a list of real matrices, i. e. XLIST=list(X1,X2,..,Xn) where the Xi's solve the LMI problem:

Defining Y,Z and cost by:

[Y,Z,cost]=evalfunc(XLIST), Y is a list of zero matrices, Y=list(Y1,...,Yp), Y1=0, Y2=0, ..., Yp=0.

Z is a list of square symmetric matrices, Z=list(Z1,...,Zq), which are semi positive definite Z1>0, Z2>0, ..., Zq>0 (i.e. spec(Z(j)) > 0),

cost is minimized.

lmisolver can also solve LMI problems in which the Xi's are not matrices but lists of matrices. More details are given in the documentation of LMITOOL.

Examples

//Find diagonal matrix X (i.e. X=diag(diag(X), p=1) such that
//A1'*X+X*A1+Q1 < 0, A2'*X+X*A2+Q2 < 0 (q=2) and trace(X) is maximized 
n  = 2;
A1 = rand(n,n);
A2 = rand(n,n);
Xs = diag(1:n);
Q1 = -(A1'*Xs+Xs*A1+0.1*eye());
Q2 = -(A2'*Xs+Xs*A2+0.2*eye());

deff('[LME,LMI,OBJ]=evalf(Xlist)','X   = Xlist(1); ...
                                   LME = X-diag(diag(X));...
                                   LMI = list(-(A1''*X+X*A1+Q1),-(A2''*X+X*A2+Q2)); ...
                                   OBJ = -sum(diag(X))  ');

X=lmisolver(list(zeros(A1)),evalf);

X=X(1)
[Y,Z,c]=evalf(X)

See Also

  • lmitool — tool for solving linear matrix inequations
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Last updated:
Wed Oct 05 12:10:58 CEST 2011