TOMLAB OPTIMIZATION ENVIRONMENT: mcpAssign

mcpAssign

Purpose

For setting up Mixed Complementarity (MCP) problems.

Syntax

   Prob = mcpAssign(F, J, JacPattern, x_L, x_U, Name, x_0, ...                       
                    A, b_L, b_U, x_min, x_max, f_opt, x_opt);

Description

mcpAssign is a direct way of setting up a Mixed Complementarity Problem (MCP) in the TOMLAB (TQ) format.

The information is put into the TOMLAB input problem structure Prob.

 Prob = mcpAssign(....)

It is then possible to solve the MCP using the TOMLAB PATH solver pathTL with the call:

 Result = tomRun(Prob, 'path'...);

See the file tomlab\examples\testTLmcp1.m for an example.

mcpAssign may also create an Init File in the TOMLAB Init File format, see the input argument setupFile.

Input Parameters

 F           Name of the function that computes the function values F(x) 
 J           Name of the function that computes the Jacobian
 JacPattern  zero-one sparse or dense matrix, where 0 values indicate 
             zeros in the Jacobian and ones indicate values that might 
             be non-zero. If empty indicates estimation of all elements 
             JacPattern is used when estimating the Jacobian numerically.
             Estimated before solve, if Prob.LargeScale==1, JacPattern==[]

 x_L         Lower bounds on parameters x. If [] set as a nx1 0   vector.
 x_U         Upper bounds on parameters x. If [] set as a nx1 Inf vector.
 Name        The name of the problem (string)
 x_0         Starting values, default nx1 zero vector 

 Note:       The number n of the unknown variables x are taken as
             max(length(x_L),length(x_U),length(x_0))
             You must specifiy at least one of these with correct length,
             then the others are given default values

             The following parameters are optional, and problem type dependent
             Set empty to get default value

 L I N E A R   C O N S T R A I N T S
 A           mA x n matrix A, linear constraints b_L<=A*x<=b_U. Dense or sparse
 b_L         Lower bound vector in linear constraints b_L <= A*x <= b_U. 
 b_U         Upper bound vector in linear constraints b_L <= A*x <= b_U. 

 A D D I T I O N A L   P A R A M E T E R S
 mu      Starting values for the multipliers.
 x_min   Lower bounds on each x-variable, used for plotting
 x_max   Upper bounds on each x-variable, used for plotting
 f_opt   Optimal function value(s), if known (Stationary points)
 x_opt   The x-values corresponding to the given f_opt, if known.
         If only one f_opt, give x_opt as a 1 by n vector
         If several f_opt values, give x_opt as a length(f_opt) by n matrix
         If adding one extra column n+1 in x_opt, 0 is min, 1 saddle, 2 is max.
         x_opt and f_opt is used in printouts and plots.

lpAssign

minlpAssign