Mathematical Optimization for Business Problems

Mathematical Programming is a powerful technique used to model and solve optimization problems. This training provides the necessary fundamentals of mathematical programming and useful tips for good modeling practice in order to construct simple optimization models.

Syllabus

In this training, you will explore several aspects of mathematical programing to start learning more about constructing optimization models using IBM Decision Optimization technology, including:

Basic terminology: operations research, mathematical optimization, and mathematical programming
Basic elements of optimization models: data, decision variables, objective functions, and constraints
Different types of solution: feasible, optimal, infeasible, and unbounded
Mathematical programming techniques for optimization: Linear Programming, Integer Programming, Mixed Integer Programming, and Quadratic Programming
Algorithms used for solving continuous linear programming problems: simplex, dual simplex, and barrier
Important mathematical programming concepts: sparsity, uncertainty, periodicity, network structure, convexity, piecewise linear and nonlinear

These concepts are illustrated by concrete examples, including a production problem and different network models.