Learn how to use linear programming to simplify, explain, verify, and improve AI models, and make them more transparent and trustworthy.

Optimization and linear programming can be used to model and solve various types of problems, such as linear, nonlinear, discrete, continuous, convex, or non-convex.

Roughly, we will cover the following topics (some of them may be skipped depending on the time available). Linear Programming: Basics, Simplex Algorithm, and Duality. Applications of Linear ...

Use of linear programming models has grown so extensively in recent years that the whole concept for organizing a computer code has undergone a radical change. It no longer is adequate merely to ...

A linear programming formulation of Shell's distribution network with three products handled by three transportation systems between four sources of product and a large number of transshipment points ...

A linear programming model was developed in 1967 to determine an optimum balance between the available wood mix and the projected sales requirements for known production constraints. Since 1969 this ...

Feature selection represents a major challenge in the biomedical data mining problem, and numerous algorithms have been proposed to select an optimal subset of features with the best classification ...