Wolfram Mathematica Comparative Analyses

Optimization Software

Optimization Software
Optimization has traditionally been the domain of expensive specialized packages. But over the past several years, Mathematica has progressively redefined these expectations—not only by bringing industrial-strength optimization capabilities into standard desktop software, but also by integrating optimization into Mathematica's complete modeling, programming, visualization and interface environment.
Built into Mathematica are state-of-the-art algorithms for linear and nonlinear, constrained and unconstrained, local and global as well as continuous and discrete optimization. Mathematica's unique architecture allows it to scale seamlessly from small interactive problems directly entered in the standard Mathematica language with traditional mathematical notation, to large-scale algorithmically constructed problems that can be deployed in a modern distributed computing environment.
The integration of optimization with the full symbolic Mathematica system makes possible a new level of algorithmic model construction and manipulation, and allows industrial-strength optimization to become part of the routine workflow. In addition, Mathematica's unique web of algorithmic capabilities—extending across continuous and discrete mathematics, as well as newer complex-systems-inspired methods—consistently allows Wolfram Research to extend the state of the art in optimization algorithms, and make the results immediately available through Mathematica's automatic algorithm selection mechanism.
Optimization Software Features in Mathematica:
Key Advantages of Mathematica for Optimization:
Interoperability with Optimization Software:
Interesting Tidbits:
  • Mathematica's symbolic program representation is used in genetic programming
  • Mathematica can find symbolic solutions to many standard optimization test problems
  • Wolfram's NKS methods are increasingly being studied for optimization
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