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"The Global Optimization Toolbox for Maple provides a widely applicable,
fully integrated development environment that can support control engineering
applications and help realise the significant potential that global optimization
has as a valuable tool in control system design."
Dr. Didier Henrion, LAAS-CNRS, Toulouse, France
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here to read his full review
Global Optimization Toolbox for Maple
Optimisation is the science of finding decisions that satisfy given constraints
and meet a specific goal at its optimal value. In engineering, constraints
may arise from physical limitations and technical specifications; in business,
constraints are often related to resources, including manpower, equipment,
costs and time.
The objective of global optimisation is to find the "best possible"
solution in nonlinear decision models that frequently have a number of
sub-optimal (local) solutions. Multi-extremal optimisation problems can
be very difficult. To obtain a high quality numerical solution, a global
"exhaustive" search approach is necessary. In the absence of
global optimisation tools, engineers and researchers are often forced
to settle for feasible solutions, often neglecting the optimum values.
In practical terms, this implies inferior designs and operations, and
related expenses in terms of reliability, time, money and other resources.
Using the Global Optimization Toolbox, you can formulate optimisation
models easily inside the powerful Maple numeric and symbolic system and
then use world-class optimisation technology to return the best answer
robustly and efficiently.
Application Areas
Global optimization problems are prevalent in systems described by highly
nonlinear models. These areas include:
- Advanced engineering design,
- Econometrics and finance
- Management science
- Medical research and biotechnology
- Chemical and process industries
- Industrial engineering
- Scientific modeling
Key Features
- Incorporates the following solver modules for nonlinear optimization
problems.
• Branch-and-bound global search
• Global adaptive random search
• Multi-start based global random search
• Global solution further refined by local search using the reduced gradient
method
- Solves models with thousands of variables and constraints.
- Solvers take advantage of Maple arbitrary precision capabilities in
their calculations, to greatly reduce numerical instability problems.
- Supports arbitrary objective and constraint functions, including those
defined in terms of special functions (for example, Bessel, hypergeometric),
derivatives and integrals, and piecewise functions etc. Functions can
also be defined in terms of a Maple procedure rather than a formula.
- Interactive Maplet™ assistant for easy problem definition and
exploration.
- Built-in model visualization capabilities for viewing one or two-dimensional
subspace projections of the objective function, with visualization
of the constraints as planes or lines on the objective surface.
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here for system requirements
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