MathOptimizer 2
An Advanced Modeling and Optimization System for Mathematica Users
MathOptimizer enables
the global and local numerical
solution of a very general class of optimization
problems
defined by a finite number of real-valued, continuous functions
over a
finite n-dimensional interval
region.
Special emphasis is placed on nonlinear models, including those that typically
have an unknown number of local optima. Nonlinear and global optimization
problems are ubiquitous in the sciences, engineering, and economics.
Several
prominent examples are systems of nonlinear equations and inequalities,
nonlinear regression, forecasting models, data classification, minimal-energy
models,
various packing problems, risk management and other stochastic decision
problems, and the design and
operation of "black box" engineering systems (which
are often defined by a complicated, numerically intensive procedure).
MathOptimizer consists of two
core solver packages and a solver integrator package. The first core solver
package is
used for approximate global optimization of an aggregated merit
(exact penalty) function on a given interval range.
This package is based on a
globally convergent adaptive stochastic search procedure, and it also
incorporates statistical
estimation techniques.
The second core solver package is meant for precise local optimization. It is
based on the standard nonlinear (convex)
programming approach and refines a
given initial solution. The solver integrator package supports the individual or
combined use of the core solver packages, but both of the core packages can also
be used in standalone mode.
The MathOptimizer User
Guide includes concise mathematical background notes and useful modeling tips.
It also discusses a number of test problems and several nontrivial application
examples.
The guide can be accessed directly through Mathematica's
online help system.
About the Developers
János D. Pintér (PhD, DSc) is a researcher and software developer working mostly
in the area of nonlinear optimization.
He received the 2000 INFORMS Computing
Society Prize for Research Excellence for the book Global
Optimization in Action,
and he has also authored and edited other books and
numerous articles related to this field. Dr. Pintér serves on
the editorial
board of the Journal of Global
Optimization and of several
professional journals, and currently is
Global Optimization Vice Chair of the
INFORMS Optimization Society. He is the developer of LGO and of MathOptimizer,
a native Mathematica application
package for global and local optimization.
Frank J. Kampas (PhD, MBA) has exte
nsive experience related to programming,
model development, and optimization in Mathematica and
other languages.
He has usedMathematica in
the solar energy, aerospace, and supply chain management industries and is the
developer
of MathOptimizer
Professional, a link between Mathematica and
LGO, as well as co-developer of the most recent version
of MathOptimizer.
MathOptimizer 2
requires Mathematica 6,
7, or 8 and is available for Windows, Linux, and Mac OS X.
