Mathematical Programming for Economics and Business.

Iowa State University Press, 1976 first ed 462 pgs, Illust., Graphs, Appendices, Bibliography, Index.

Condition: Good+ overall, red cloth hardcover, no dustjacket; some nicks & dings to covers, red bookstore rubberstamp boldly on endpage and on 2 text pages toward the rear of the book, otherwise pages are clean & unmarked, binding is tight.

Price: $6.00

Item Description

Characteristics & types of models -- Mathematical programming problem, Model classification, Specific models of intrest;

Linear programming -- Linear programming model, Graphic solution of linear programming problems, Linear programming definitions and theorems, Simplex algorithm, Simpley theory, Duality, Sensitivity analysis, Application of linear programming, problems;

Nonlinear programming -- Preliminaries, Lagrangian multipliers & equality-constrained problems, Kuhn-Tucker conditions, Constraint qualification, Role of convexity, Examples, Problems;

Nonlinear programming alogrithms -- Steepest ascent methods, Separable programming, Penalty functions method, Search proceedures, Selection of a algorithm, problems,

Quadratic programming -- Method of Lagrangian multipliers, Quadratic forms, General quadratic problem, Wolfe's simplex method for quadratic programming, quadratic programming & duality, Applications of quadratic programming, Basic portfolio models; Integer programming-- Integer programming models, Cutting plane methods, Branch & bound algorithm, Mixed integer-continuous variable problem, Binary programming, Examples, Problems;

Dynamic programming -- Characteristics of a dynamic programming problem, Continuous variable case, Examples, Problems; Recursive programming-- Models of recursive programming, Applications of recursive programming, Recursive & dynamic programming contrasted, Problems;

Calculus of variations -- Definitions & properties of the problem, Techniques for finding solutions, Applications, Limitations, Problems;

Stochastic programming -- Uncertainty & certainty equivalence, Passive approach to Stochastic programming, Active approach to Stochastic programming, Chance-constrained programming & deterministic equivalents, Dynamics of optimizing under uncertainty, Problems; Appendixes.