How to Analyze the Results of Linear Programs—Part 1: Preliminaries

In a four part series, I describe ways to analyze the results of linear programs beyond what is commonly described in textbooks. My intent is to capture the thought process in analysis with two objectives. First, I want to provide a guide to those getting started in applications of linear programming by suggesting useful ways of looking at the results. Second, I want to help create an artificially intelligent environment for the analysis of results by presenting a protocol that a knowledge engineer can use. The former has been in the folklore for decades; the latter is part of a project to develop an intelligent mathematical programming system. This first part of the series contains basic terms and concepts used in the other three parts: price interpretation, infeasibility diagnosis, and forcing substructures.