Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods by John W. Chinneck, published by Springer US on November 23, 2010, is a softcover reprint of the hardcover first edition from 2008, consisting of 274 pages. This book presents practical algorithms that address issues of feasibility and infeasibility in optimization, focusing on methods that can be readily applied by practitioners and researchers in various fields.
Readers will find a comprehensive exploration of algorithms designed for quickly seeking feasibility, particularly in challenging nonlinear and mixed-integer programs. The book also delves into techniques for analyzing infeasibility, including isolating minimal infeasible subsets of constraints and identifying optimal repairs for infeasibility. Additionally, it highlights unexpected applications in fields such as classification, computational biology, and medicine, while establishing connections to constraint programming. This work is relevant for those engaged in mathematics, optimization, and decision-making across the applied sciences.
Official synopsis Publisher
Feasibility and Infeasibility in Optimization is an expository book focused on practical algorithms related to feasibility and infeasibility in optimization. Part I addresses algorithms for seeking feasibility quickly, including recent algorithms for the difficult cases of nonlinear and mixed-integer programs. Part II provides algorithms for analyzing infeasibility by isolating minimal infeasible (or maximum feasible) subsets of constraints, or by finding the best repair for the infeasibility. Part III describes surprising applications in areas such as classification, computational biology, and medicine. Connections to constraint programming are shown. A main goal is to impart an understanding of the methods so that practitioners can make immediate use of existing algorithms and software, and so that researchers can extend the state of the art and find new applications. The book is of interest to researchers, students, and practitioners across the applied sciences who are working on optimization problems.
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