Titlebook: Linear Programming; Foundations and Exte Robert J. Vanderbei Book 20083rd edition Springer-Verlag US 2008 Simplex method.algorithms.ants.du

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RegressionIn this chapter, we shall study an application of linear programming to an area of statistics called regression. As a specific example, we shall use size and iterationcount data collected from a standard suite of linear programming problems to derive a regression estimate of the number of iterations needed to solve problems of a given size.

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ApplicationsIn this chapter, we discuss briefly the most important applications of network flow problems.

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Convex AnalysisIn this chapter, we shall give a brief introduction to this broader subject. In particular, we shall prove a few of the fundamental results of convex analysis and see that their proofs depend on some of the theory of linear programming that we have already developed.

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Sensitivity and Parametric Analysesn: having found an optimal solution to a given linear programming problem, how much can we change the data and have the current partition into basic and nonbasic variables remain optimal? The second subject addresses situations in which one wishes to solve not just one linear program, but a whole fa

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Network Flow Problemsnds for material at various locations given sources of material at other locations. Such problems are called .. They form the most important special class of linear programming problems. Transportation, electric, and communication networks provide obvious examples of application areas. Less obvious,