Introduction to Linear Optimization and Extensions with MATLAB Operations Research Series
Filling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, Introduction to Linear Optimization and Extensions with MATLAB® provides a concrete and intuitive yet rigorous introduction to modern linear optimization. In addition to fundamental topics, the book discusses current linear optimization technologies such as predictor-path following interior point methods for both linear and quadratic optimization as well as the inclusion of linear optimization of uncertainty i.e. stochastic programming with recourse and robust optimization.
The author introduces both stochastic programming and robust optimization as frameworks to deal with parameter uncertainty. The author?s unusual approach?developing these topics in an introductory book?highlights their importance. Since most applications require decisions to be made in the face of uncertainty, the early introduction of these topics facilitates decision making in real world environments. The author also includes applications and case studies from finance and supply chain management that involve the use of MATLAB.
Even though there are several LP texts in the marketplace, most do not cover data uncertainty using stochastic programming and robust optimization techniques. Most emphasize the use of MS Excel, while this book uses MATLAB which is the primary tool of many engineers, including financial engineers. The book focuses on state-of-the-art methods for dealing with parameter uncertainty in linear programming, rigorously developing theory and methods. But more importantly, the author?s meticulous attention to developing intuition before presenting theory makes the material come alive.
FUNDAMENTALS: Geometry of Linear Optimization. Simplex Method. Duality and Sensitivity Analysis. EXTENSIONS: Decomposition in Linear Optimization. Quadratic Optimization. Interior Point Methods. ROBUST STRATEGIES FOR LINEAR OPTIMIZATION: Stochastic Programming. Robust Linear Optimization.
Roy H Kwon is a professor at University of Toronto - St. George Campus, Canada.
Date de parution : 09-2013
15.6x23.4 cm
Mots-clés :
Interior Point Method; Basic Feasible Solution; Quadratic Optimization; Restricted Master Problem; Convex Analysis; Linear Program; Linear Optimization; Revised Simplex Method; Linear Algebra; Optimal Basic Feasible Solution; Non-basic Variable; Simplex Method; Initial Basic Feasible Solution; Dantzig Wolfe Decomposition; Master Problem; Pi5 Pi6 Pi7 Pi8; Descent Direction Generation; Standard Form; Dual Simplex Method; Mad Model; Primal Dual Interior Point Method; Direction D1; Minimum Cost Flow Problem; MVO Problem; Stochastic Programming; Complementary Slackness; Closed Halfspace; Feasible Set; X2 Subject