Subject: ANNOUNCEMENT: The TOMLAB 1.0 Optimization Environment in Matlab
From: Kenneth =?iso-8859-1?Q?Holmstr=F6m?= <hkh@acm.org>
Date: Sat, 12 Dec 1998 01:28:24 -0800
Newsgroups: sci.op-research,sci.math.num-analysis,comp.soft-sys.matlab,sci.stat.math

ANNOUNCEMENT: The TOMLAB 1.0 Optimization Environment in Matlab

The first official release of the Matlab 5 based optimization
environment
TOMLAB is available at http://www.ima.mdh.se/tom, the home page of the
Applied Optimization and Modeling group (TOM) at Malardalen University,
Vasteras, Sweden. A 160 page User's Guide in postscript format is
available.
TOMLAB 1.0 is free for academic use.

Much effort has been put on advanced design utilizing the power and
features of Matlab.  The design principle is:
*** Define your problem once, optimize using any suitable solver. ***
This is possible using general gateway and interface routines, global
variables, string evaluations and structure arrays.
A stack strategy for the global variables makes recursive calls
possible,
in e.g. separable nonlinear least squares algorithms. One structure
holds
all information about the problem and one holds the results.
TOMLAB should be seen as a proposal for a standard for optimization in
Matlab.

TOMLAB offers about 65 numerically robust algorithms for linear,
discrete,
nonlinear, global optimization and constrained nonlinear parameter
estimation.
It includes an advanced graphical user interface (GUI), menus, graphics
and
a lot of predefined test problems.  New user-defined problems are easily
added.
TOMLAB has interfaces to C, Fortran, MathWorks Optimization TB, CUTE and
AMPL.
Automatic differentiation is easy using an interface to ADMAT/ADMIT TB
and
four types of numerical differentiation are included.  Currently,
MEX-file
interfaces have been developed for the commercial solvers MINOS, NPSOL,
NPOPT,
NLSSOL and QPOPT. A problem is solved either by direct call to a solver
or a
general multi-solver driver routine, or interactively, using the
graphical user
interface (GUI) or a menu system.

TOMLAB is very easy to use for the occasional user and student, directly

giving access to large set of solvers and algorithms. For the
optimization
algorithm developer and the applied researcher in need of optimization
tools
it is very easy to compare different solvers or do test runs on
thousands of
problems.
A tool is provided to automate the tedious work of making computational
result
tables from test runs, directly making LaTeX tables for inclusion in
papers.

The TOMLAB solvers all explicitly handle bounds and linear constraints,
with an input model upper/lower bound format like NPSOL. Implemented
solver
algorithms for general NLP problem are SQP type algorithms like
Schittkowski
SQP, Fletcher-Leyffer Filter SQP and Han-Powell SQP. A structural trust
region algorithm for partially separable functions on convex sets (Conn
et.al)
is also implemented.

For nonlinear least squares, Gauss-Newton with subspace minimization,
Fletcher-Xu, Al-Baali-Fletcher and Huschens TSSM method are implemented,

together with an active set strategy to handle bounds and linear
constraints.
The most common unconstrained algorithms are implemented: Newton
algorithms,
and several Quasi-Newton and conjugate gradient methods. Global
optimization
problems without derivatives are solved using the DIRECT and EGO
algorithms
(Jones et.al). Quadratic programming problems are solved with a standard

active set method, using eigenvalue information for indefinite problems.

For linear programming, different types of simplex algorithms are
implemented
as well as the dual simplex algorithm. A branch and bound and a cutting
plane
algorithm are solving MIP problems. Solvers for different types of
network
programs and dynamic programs are available.

Happy computing with TOMLAB! (Feedback is welcome)

Kenneth Holmstrom