This Fourth Edition of Linear Programming presents a contemporary introduction to optimization theory with a strong emphasis on constrained optimization. It begins with an extensive treatment of linear programming and progressively covers topics such as convex analysis, network flows, integer programming, quadratic programming, and convex optimization. The book bridges the gap between theory and practice, offering numerous detailed numerical examples that illustrate both business and non-business applications. Designed with clarity in mind, this edition introduces specific algorithms and tangible examples before moving on to more abstract concepts. It features a collection of free C programs demonstrating key algorithms, including the two-phase simplex method, primal-dual simplex method, path-following interior-point method, and homogeneous self-dual methods. Moreover, online JAVA applets are provided to illustrate various pivot rules and variants of the simplex method, enhancing the reader’s understanding of the underlying techniques.
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