Visualization in optimization with Mathematica

Nonlinear Optimization in Mathematica with MathOptimizer Professional

The MathOptimizer Professional software package combines Mathematica’s modeling capabilities with an external solver suite for general nonlinear – global and local – optimization. Optimization models formulated in Mathematica are directly transferred along to the solver engine; the results are seamlessly returned to the calling Mathematica document. This approach combines the advantages of a hi...

Fuzzy multiobjective optimization modeling with Mathematica

In the real situations, decision makers are often faced to a plurality of objectives and constraints in a world of imprecise data about the preferences of agents, the local constraints and the global environment. In a fuzzy environment, fuzzy linear programming (FLP) and fuzzy goal programming (FGP) problems incorporate fuzzy objective functions and constraints, fuzzy parameters and variable se...

Visualization of Graphs and Related Discrete Structures in Mathematica

Differential Evolution in Discrete and Combinatorial Optimization Mathematica tutorial notes

We will show a number of ways in which Differential Evolution, a member of the genetic/evolutionary family of optimization methods, can be used for the purpose of discrete optimization. Problems under consideration include standard integer linear programming, knapsack problems, set partitioning, set covering, quadratic assignment, mixed (discrete−continuous) optimization, and more. Introduction...

Clifford Algebra with Mathematica

The Clifford algebra of a n-dimensional Euclidean vector space provides a general language comprising vectors, complex numbers, quaternions, Grassman algebra, Pauli and Dirac matrices. In this work, we present an introduction to the main ideas of Clifford algebra, with the main goal to develop a package for Clifford algebra calculations for the computer algebra program Mathematica∗. The Cliffor...