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With emphasis on computation, this book is a real breakthrough in the field of LP. In addition to conventional topics, such as the simplex method, duality, and interior-point methods, all deduced in a fresh and clear manner, it introduces the state of the art by highlighting brand-new and advanced results, including efficient pivot rules, Phase-I approaches, reduced simplex methods, deficient-basis methods, face methods, and pivotal interior-point methods. In particular, it covers the determination of the optimal solution set, feasible-point simplex method, decomposition principle for solving large-scale problems, controlled-branch method based on generalized reduced simplex framework for solving integer LP problems.
List of contents
Introduction.- Geometry of the Feasible Region.- Simplex Method.- Duality principle and dual simplex method.- Implementation of the Simplex Method.- Sensitivity Analysis and Parametric LP.- Variants of the Simplex Method.- Decomposition Method.- Interior Point Method.- Integer Linear Programming (ILP).- Pivot Rule.- Dual Pivot Rule.- Simplex Phase-I Method.- Dual Simplex Phase-l Method.- Reduced Simplex Method.- Improved Reduced Simplex Method.- D-Reduced Simplex Method.- Criss-Cross Simplex Method.- Generalizing Reduced Simplex Method.- Deficient-Basis Method.- Dual Deficient-Basis Method.- Face Method.- Dual Face Method.- Pivotal interior-point Method.- Special Topics.- Appendix.- References.
Summary
With emphasis on computation, this book is a real breakthrough in the field of LP. In addition to conventional topics, such as the simplex method, duality, and interior-point methods, all deduced in a fresh and clear manner, it introduces the state of the art by highlighting brand-new and advanced results, including efficient pivot rules, Phase-I approaches, reduced simplex methods, deficient-basis methods, face methods, and pivotal interior-point methods. In particular, it covers the determination of the optimal solution set, feasible-point simplex method, decomposition principle for solving large-scale problems, controlled-branch method based on generalized reduced simplex framework for solving integer LP problems.
Additional text
“Evidenced by superior performance in computational experiments, the author’s work has refreshed the state of the art of LP, and by its originality, breadth and depth, is having a major impact on the field of mathematical optimization.” (EJOR, European Journal of Operational Research, Vol. 267 (3), June, 2018)
“The book seems to be mainly addressed to scientists who already possess some expertise in LP. The kind of presentation, however, also allows using parts of it as a basis for a course on the topic. In fact, a special feature of the book is that an algorithm typically is accompanied by some example for which the results of all computational steps needed to find a solution are written down.” (Rembert Reemtsen, zbMATH, Vol. 1302, 2015)
“This book is a research monograph focusing on computational techniques in the simplex method for linear programming. … It may be of interest to researchers and developers of simplex method codes for linear programming.” (B. Borchers, Choice, Vol. 52 (3), November, 2014)
Report
"Evidenced by superior performance in computational experiments, the author's work has refreshed the state of the art of LP, and by its originality, breadth and depth, is having a major impact on the field of mathematical optimization." (EJOR, European Journal of Operational Research, Vol. 267 (3), June, 2018)
"The book seems to be mainly addressed to scientists who already possess some expertise in LP. The kind of presentation, however, also allows using parts of it as a basis for a course on the topic. In fact, a special feature of the book is that an algorithm typically is accompanied by some example for which the results of all computational steps needed to find a solution are written down." (Rembert Reemtsen, zbMATH, Vol. 1302, 2015)
"This book is a research monograph focusing on computational techniques in the simplex method for linear programming. ... It may be of interest to researchers and developers of simplex method codes for linear programming." (B. Borchers, Choice, Vol. 52 (3), November, 2014)
