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: The Design of Approximation AlgorithmsThe Design of Approximation Algorithms
by - Cambridge University Press
This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. It is organized around techniques for designing approximation algorithms, including greedy and local search algorithms.
(9135 views)
Book cover: Linear Optimisation and Numerical AnalysisLinear Optimisation and Numerical Analysis
by - University of Aberdeen
The book describes the simplex algorithm and shows how it can be used to solve real problems. It shows how previous results in linear algebra give a framework for understanding the simplex algorithm and describes other optimization algorithms.
(9599 views)
Book cover: An Introduction to Nonlinear Optimization TheoryAn Introduction to Nonlinear Optimization Theory
by - De Gruyter Open
Starting with the case of differentiable data and the classical results on constrained optimization problems, continuing with the topic of nonsmooth objects involved in optimization, the book concentrates on both theoretical and practical aspects.
(1663 views)
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.
(525 views)