MATLAB and Optimization: Question

MATLAB and Optimization: Question

Post by mgw.. » Mon, 10 Apr 1995 04:00:00



Can MATLAB solve a non-linear programming problem which has
16 variables and 40 constraints?. The objective function
of the problem is non-linear, and among 40 constraints, 10 are
also non-linear. If it solves, how?. If it does not, what are
the other alternatives?.

 
 
 

MATLAB and Optimization: Question

Post by Andy Potv » Mon, 10 Apr 1995 04:00:00



: Can MATLAB solve a non-linear programming problem which has
: 16 variables and 40 constraints?. The objective function
: of the problem is non-linear, and among 40 constraints, 10 are
: also non-linear. If it solves, how?. If it does not, what are
: the other alternatives?.

The problem description you pose may be solved by the Optimization
Toolbox routine constr

Quote:>> help constr

 CONSTR Finds the constrained minimum of a function of several variables.

        X=CONSTR('FUN',X0) starts at X0 and finds a constrained minimum to
        the function which is described in FUN (usually an M-file: FUN.M).
        The function 'FUN' should return two arguments: a scalar value of the
        function to be minimized, F, and a matrix of constraints, G:
        [F,G]=FUN(X). F is minimized such that G < zeros(G).

        X=CONSTR('FUN',X,OPTIONS) allows a vector of optional parameters to
        be defined. For more information type HELP FOPTIONS.

        X=CONSTR('FUN',X,OPTIONS,VLB,VUB) defines a set of lower and upper
        bounds on the design variables, X, so that the solution is always in
        the range VLB < X < VUB.

        X=CONSTR('FUN',X,OPTIONS,VLB,VUB,'GRADFUN') allows a function
        'GRADFUN' to be entered which returns the partial derivatives of the
        function and the  constraints at X:  [gf,GC] = GRADFUN(X).

Hope this helps,
Andy P


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