TOMLAB OPTIMIZATION ENVIRONMENT: GetSolver

GetSolver

Purpose

Returns a TOMLAB default solver.

Syntax

   Solver = GetSolver(Type, LargeScale, Convex);

Description

GetSolver returns the TOMLAB default solver for different problems. All standard Types of optimization and also type: FP and DLP

Problem

        min   0.5 * x' * F * x + c' * x.  x in R^n
         x
        s/t   x_L <=   x  <= x_U
              b_L <= A x  <= b_U

 Equality equations: Set b_L==b_U
 Fixed    variables: Set x_L==x_U

Input Parameters

 Type:      String with type of problem:
  'qp'      Quadratic programming, F is nonempty (QP)
  'lp'      Linear programming, F is empty, c is nonempty, and c~=0 (LP)
  'fp'      Feasible point (phase 1) linear programming, F and c empty or 0
  'dlp'     Dual linear programming. A standard LP problem with a 
            dual feasible initial point available.
 Also Type can be any of the following standard types
  'uc'      Unconstrained optimization
  'con'     Nonlinear programming (constrained optimization) (NLP)
  'ls'      Nonlinear least squares (NLLS)
  'lls'     Linear least squares (LLS)
  'cls'     Constrained nonlinear least squares
  'mip'     Mixed-integer (linear) programming (MIP or MILP)
  'glb'     Global optimization (box-bounded)
  'glc'     Global optimization (box-bounded, integer and constrained)
  'miqp'    Mixed-integer quadratic programming (MIQP)
  'minlp'   Mixed-integer nonlinear programming (MINLP)
  'sdp'     Semidefinite programming (SDP) - Linear SDP with LMI constraints
  'bmi'     Linear SDP with BMI constraints (BMI)
  'exp'     Exponential sum fitting
            
 LargeScale If the flag Prob.LargeScale > 0 is set, LargeScale is set true

 Convex     If Convex > 0, problem is convex, i.e. F is positive semidefinite
            for QP problems. Only used for type QP.

Output Parameters

 Solver     String with name of default solver

tomRun

preSolve