# TOMLAB Solver Reference

This page is part of the TOMLAB Manual. See TOMLAB Manual. |

Detailed descriptions of the TOMLAB solvers, driver routines and some utilities are given in the following sections. Also see the M-file help for each solver. All solvers except for the TOMLAB Base Module are described in separate manuals.

For a description of solvers called using the MEX-file interface, see the M-file help, e.g. for the MINOS solver *minosTL.m*. For more details, see the User's Guide for the particular solver.

## clsSolve

Solves dense and sparse nonlinear least squares optimization problems with linear inequality and equality con- straints and simple bounds on the variables.

## conSolve

Solve general constrained nonlinear optimization problems.

## cutPlane

Solve mixed integer linear programming problems (MIP).

## DualSolve

Solve linear programming problems when a dual feasible solution is available.

## expSolve

Solve exponential fitting problems for given number of terms p.

## glbDirect

Solve box-bounded global optimization problems.

## glbSolve

Solve box-bounded global optimization problems.

## glcCluster

Solve general constrained mixed-integer global optimization problems using a hybrid algorithm.

## glcDirect

Solve global mixed-integer nonlinear programming problems.

## glcSolve

Solve general constrained mixed-integer global optimization problems.

## infLinSolve

Finds a linearly constrained minimax solution of a function of several variables with the use of any suitable TOMLAB solver. The decision variables may be binary or integer.

## infSolve

Find a constrained minimax solution with the use of any suitable TOMLAB solver.

## linRatSolve

Finds a linearly constrained solution of a function of the ratio of two linear functions with the use of any suitable TOMLAB solver. Binary and integer variables are not supported.

## lpSimplex

Solve general linear programming problems.

## L1Solve

Find a constrained L1 solution of a function of several variables with the use of any suitable nonlinear TOMLAB solver.

## MilpSolve

Solve mixed integer linear programming problems (MILP).

## minlpSolve

Branch & Bound algorithm for Mixed-Integer Nonlinear Programming (MINLP) with convex or nonconvex sub problems using NLP relaxation (Formulated as minlp-IP).

## mipSolve

Solve mixed integer linear programming problems (MIP).

## multiMin

multiMin solves general constrained mixed-integer global optimization problems. It tries to find all local minima by a multi-start method using a suitable nonlinear programming subsolver.

## multiMINLP

multiMINLP solves general constrained mixed-integer global nonlinear optimization problems.

## nlpSolve

Solve general constrained nonlinear optimization problems.

## pdcoTL

*pdcoTL *solves linearly constrained convex nonlinear optimization problems.

## pdscoTL

*pdscoTL* solves linearly constrained convex nonlinear optimization problems.

## qpSolve

Solve general quadratic programming problems.

## slsSolve

Find a Sparse Least Squares (sls) solution to a constrained least squares problem with the use of any suitable TOMLAB NLP solver.

## sTrustr

Solve optimization problems constrained by a convex feasible region.

## Tfmin

Minimize function of one variable. Find miniumum x in [x_L, x_U] for function Func within tolerance xTol. Solves using Brents minimization algorithm.

## Tfzero

Tfzero, TOMLAB fzero routine.

## ucSolve

Solve unconstrained nonlinear optimization problems with simple bounds on the variables.

## Additional solvers

Documentation for the following solvers is only available at http://tomopt.com and in the m-file help.

- goalSolve - For sparse multi-objective goal attainment problems, with linear and nonlinear constraints.

- Tlsqr - Solves large, sparse linear least squares problem, as well as unsymmetric linear systems.

- lsei - For linearly constrained least squares problem with both equality and inequality constraints.

- Tnnls - Also for linearly constrained least squares problem with both equality and inequality constraints.

- qld - For convex quadratic programming problem.