Logo

Linear Programming by Jim Burke

Small book cover: Linear Programming

Linear Programming
by

Publisher: University of Washington

Description:
An introductory course in linear programming. The four basic components of the course are modeling, solution methodology, duality theory, and sensitivity analysis. We focus on the simplex algorithm due to George Dantzig since it offers a complete framework for discussing both the geometry and duality theory for linear programs.

Home page url

Download or read it online for free here:
Download link
(multiple PDF files)

Similar books

Book cover: Convex Optimization: Algorithms and ComplexityConvex Optimization: Algorithms and Complexity
by - arXiv.org
This text presents the main complexity theorems in convex optimization and their algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural and stochastic optimization.
(6744 views)
Book cover: Lectures on Optimization: Theory and AlgorithmsLectures on Optimization: Theory and Algorithms
by - Tata Institute of Fundamental Research
Contents: Differential Calculus in Normed Linear Spaces; Minimization of Functionals; Minimization Without Constraints; Minimization with Constraints; Duality and Its Applications; Elements of the Theory of Control and Elements of Optimal Design.
(11211 views)
Book cover: Decision Making and Productivity MeasurementDecision Making and Productivity Measurement
by - arXiv
I wrote this book as a self-teaching tool to assist every teacher, student, mathematician or non-mathematician, and to support their understanding of the elementary concepts on assessing the performance of a set of homogenous firms ...
(7129 views)
Book cover: Data Assimilation: A Mathematical IntroductionData Assimilation: A Mathematical Introduction
by - arXiv.org
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation. Authors develop a framework in which a Bayesian formulation of the problem provides the bedrock for the derivation and analysis of algorithms.
(6178 views)