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Numerical analysis --- Mathematical optimization --- Computational complexity

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Solving combinatorial optimization problems can often lead to runtime growing exponentially as a function of the input size. But important real-world problems, industrial applications, and academic research challenges, may demand exact optimal solutions. In such situations, parallel processing can reduce the runtime from days or months, typical when one workstation is used, to a few minutes or even seconds.Partners of the CEC-sponsored SCOOP Project (Solving Combinatorial Optimization Problems in Parallel) contributed, on invitation, to this book; much attention was paid to competent coverage of the topic and the style of writing. Readers will include students, scientists, engineers, and professionals interested in the design and implementation of parallel algorithms for solving combinatorial optimization problems.

Parallel processing (Electronic computers) --- Parallelle verwerking (Computers) --- Traitement parallèle (Ordinateurs) --- Combinatorial optimization --- Data processing. --- Parallel processing (Electronic computers). --- Data processing --- Combinatorial optimization - Data processing. --- Computer network architectures. --- Computer science. --- Numerical analysis. --- Combinatorics. --- Information technology. --- Engineering. --- Computer System Implementation. --- Computation by Abstract Devices. --- Numerical Analysis. --- IT in Business. --- Complexity. --- Construction --- Industrial arts --- Technology --- IT (Information technology) --- Telematics --- Information superhighway --- Knowledge management --- Combinatorics --- Algebra --- Mathematical analysis --- Informatics --- Science --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- Combinatorial optimization - Data processing

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Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book. Each contribution provides the fundamentals needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline. The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016. .

Mathematical optimization. --- Production management. --- Software engineering. --- Computer software engineering --- Engineering --- Manufacturing management --- Industrial management --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Computer science --- Optimization. --- Operations Management. --- Software Engineering/Programming and Operating Systems. --- Computational Mathematics and Numerical Analysis. --- Mathematics. --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Mathematics --- Computer mathematics.

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Business logistics. --- Delivery of goods.

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In 1995 the Handbook of Global Optimization (first volume), edited by R. Horst, and P.M. Pardalos, was published. This second volume of the Handbook of Global Optimization is comprised of chapters dealing with modern approaches to global optimization, including different types of heuristics. Together (available as a set, set ISBN 1-4020-0742-6), the two volumes of the handbook cover a complete and broad spectrum of approaches for dealing with global optimization problems.The goal of the editors is to provide a true handbook that does not focus on particular applications of the heuristics and algorithms, but rather describes the state of the art for the different methodologies. Topics covered in the handbook include various metaheuristics, such as simulated annealing, genetic algorithms, neural networks, taboo search, shake-and-bake methods, and deformation methods. In addition, the book contains chapters on new exact stochastic and deterministic approaches to continuous and mixed-integer global optimization, such as stochastic adaptive search, two-phase methods, branch-and-bound methods with new relaxation and branching strategies, algorithms based on local optimization, and dynamical search. Finally, the book contains chapters on experimental analysis of algorithms and software, test problems, and applications.Audience: Graduate students in engineering and operations research, academic research, as well as practitioners, who can tailor the general approaches described in the handbook to their specific needs and applications.

Mathematical optimization. --- Nonlinear programming.