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Constrained Optimization with
Applications from Operations Research
 Operations Research 3.0 is a Mathematica
package for solving problems in linear optimization,
quadratic programming, shortest path tasks, and
combinatorial optimization and heuristics. With
Operations Research 3.0 and Mathematica,
you can solve a broad range of typical problems
encountered in university courses and in daily
practical work and can model and solve optimization
problems in a very convenient and interactive
manner.
Operations Research 3.0 contains five package
files that are called from the master package
OperationsResearch.m. These subpackages
include:
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Linear optimization:
revised simplex with sensitivity analysis, primal affine
scaling, primal-dual interior-point method, and
branch-and-bound methods.
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Quadratic programming:
affine scaling method with Karush-Kuhn-Tucker
improvement.
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Shortest path tasks:
one-to-all and all-to-all shortest connections on a
graph with substantial new features in Version 3.0,
including a new and very efficient point-to-point
routing algorithm and an improved Dijkstra's algorithm.
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Combinatorial optimization
and heuristics: several routines that can be used to
solve assignment, traveling salesman, and other problems
and implementation of additional effective heuristics,
notably tabu search.
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Reinforcement learning: a
relatively new method for stochastic optimization and
control that is quite powerful for some types of
problems.
An even larger selection of example notebooks
containing typical problems that can be solved with
Operations Research 3.0 is included in the
documentation. Each problem is explained, modeled,
and solved in full detail. The notebook collection
will serve as a guide to dealing with a broad class
of optimization tasks.
Developed and supported by SoftAS GmbH.
Special features:
Click here to visit official website
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