Please note that the recommended version of Scilab is 2026.0.0. This page might be outdated.
See the recommended documentation of this function
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 - evalfuncmust be as follows:- [LME,LMI,OBJ]=evalfunct(X)where- Xis a list of matrices,- LME, LMIare lists and- OBJa real scalar.
- XLISTF
- a list of matrices (e.g. - XLIST0=list(X1,X2,..,Xn))
- options
- optional parameter. If given, - optionsis 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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