Handbook of Combinatorial Optimization

Herausgegeben:Pardalos, Panos M.; Du, Ding-Zhu; Thai, My T.

• Gebundenes Buch

Produktbeschreibung

The third edition of this 5-volume handbook is intended to be a basic yet comprehensive reference work in combinatorial optimization that will benefit newcomers and researchers for years to come. This multi-volume work deals with several algorithmic approaches for discrete problems as well as with many combinatorial problems. The editors have brought together almost every aspect of this enormous field of combinatorial optimization, an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communications networks, and management science. An international team of 30-40 experts in the field form the editorial board.

The Handbook of Combinatorial Optimization, third edition is addressed to all scientists who use combinatorial optimization methods to model and solve problems. Experts in the field as well as non-specialists will find the material stimulating and useful.

Produktdetails

• Verlag: Springer / Springer New York / Springer Science+Business Media, LLC / Springer, Berlin
• Artikelnr. des Verlages: 89281297, 978-1-0716-5016-5
• 3. Aufl.
• Seitenzahl: 4100
• Erscheinungstermin: 18. Juni 2026
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9781071650165
• ISBN-10: 1071650165
• Artikelnr.: 75207595

Autorenporträt

Panos Pardalos was born in Greece and graduated from Athens University (Department of Mathematics). He received his PhD (Computes and Information Sciences) from the University of Minnesota. He is a Distinguished Emeritus Professor in the Department of Industrial and Systems Engineering at the University of Florida, and an affiliated faculty of Biomedical Engineering and Computer Science  Information  Engineering departments. Panos Pardalos is a world-renowned leader in Global Optimization, Mathematical Modeling, Energy Systems, Financial applications, and Data Sciences. He is a Fellow of AAAS, AAIA, AIMBE, EUROPT, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Panos  Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies.   Ding-Zhu Du received his M.S. degree in 1982 from Chinese Academy of Sciences major in Operations Research, and his Ph.D. degree in 1985 from University of California, Santa Barbara, major in Mathematics. After 1985, he had been working in Mathematical Sciences Research Institute at Berkeley as a postdoctor, in MIT as an assistant professor, in Chinese Academy of Sciences as a research professor, in Princeton University as a visiting scholar,  in University of Minnesota as a Professor, and now in University of Texas at Dallas as a Professor of Computer Science. During 2002-2005, he was a program director at National Science Foundation of USA. He is interested in teaching and doing research in area of Design and Analysis of Approximation Algorithms  for Combinatorial Optimization Problems  raised from Various Computer System and Networks, such as Wireless Sensor Networks and Social Networks. He already published more than 250 journal papers and  about 10 authored books. He is co-Editor-in-Chief of the journal “Discrete Mathematics, Algorithms and Applications”, and in addition, serves in editorial board of more than 15 journals. In 1998, he received CSTS Award from INFORMS.  In 1993, he received the second class National Nature Science Prize from China. In 1991, he received the first class Nature Science Prize from Chines Academy of Sciences.   My T. Thai is currently a University of Florida (UF) Research Foundation Professor in the Department of Computer & Information Sciences & Engineering and Associate Director of UF Nelms Institute for the Connected World. She is a Fellow of IEEE and AAIA.My T. Thai is a leading authority who has done transformative research in trustworthy machine learning and combinatorial optimization, especially forcomplex systems with applications to healthcare, social media, critical networking infrastructure, and cybersecurity. She has been working on various interdisciplinary topics, focusing on the underlying mathematical models, coupled with efficient computational techniques for dynamic, interdependent, and uncertainty systems. 

Inhaltsangabe

Introduction.- Part 1. General Methodology.- Analysis of Greedy Approximations.- Guillotine Partition in Geometric Optimization.- Mixed-Integer Nonlinear Optimization in Process Synthesis.- Connection between Nonlinear Programming and Discrete Optimization.- Interior Point Methods for Combinatorial Optimization.- Fractional Combinatorial Optimization.- Reformulation-Linearization Techniques for Discrete Optimization Problems.- Grobner Bases in Integer Programming.- Dynamical System Approaches to Combinatorial Optimization.- Semidefinite Relaxation, Multivariate Norma Distribution and Order Statistics.- Selected Algorithmic Techniques for Parallel Optimization.- Multispace Search for Combinatorial Optimization.- Randomized Parallel Algorithms for Combinatorial Optimization.- Tabu Search.- Neural Network Approach for Combinatorial Optimization.- Data Correcting Algorithms in Combinatorial Optimization.- Probabilistic Verification and Non-Approximability.- Part 2. Classic Problems.- The Steiner ratio of $L_p$-planes.- The Maximum Clique Problem.- The Generalized Assignment Problem and Extension.- Linear Assignment Problems and Extensions.- Bin Packing Approximation Algorithms: Combinatorial Analysis.- Feedback Set Problems.- The Equitable Coloring of Graphs.- Approximate Algorithms and Heuristics for MAX-SAT.- Knapsack Problems.- Steiner Minimum Trees in E^3.- The Graph Coloring Problem: A Bibliographic Survey.- Steiner Minimum Trees: An Introduction, Parallel Computation, and Future Work.- Resource Allocation Problems.- Efficient Algorithms for Geometric Shortest Path Query Problem.- On-line Dominating Set Problems for Graphs.- Minimum Weight Triangulation.- A review of Machine Scheduling: Complexity, Algorithms and Approximability.- Algorithmic Aspects of Domination in Graphs.- The Quadratic Assignment Problem.- A Cognitive Algorithm for solving the Equal Circles Packing Problem.- Optimal Rectangular Partition.- Weighted Dominating Set in Unit Disk Graphs.- Part 3. Applications.- Applications of Set Covering, Set Packing and Set Partitioning Models: A Survey.- Combinatorial Optimization in Clustering.- Combinatorial Optimization and Coalition Games.- Optimization Problems in Optical Networks.- Optimization Applications in the Airline Industry.- Routing and Topology Embedding in Lightwave Networks.- Steiner Tree in Industry.- Connected Dominating Sets in Sensor Networks.- Network-based Model and Algorithms in Data Mining and Knowledge Discovery.- Steiner Tree in VISL Designs.- Steiner Tree in Coal Mining Industry.- Coverage Problems in sensor Networks.- Packing, Dominating and Wireless Networking.- Group Testing in Molecular Biology.- Probabilistic set covering problems and applications.- Finite Gomory's group and its applications.- Branch and bound algorithms.- Hierarchies in clique problems.- Chapter title required.- Advances in Vehicle Routing Problems.- Cooperative networks.- Deep Learning for Influence Maximization.- Reinforcement Learningfor Combinatorial Optimization.- Facility Location Games.- Fair Allocation of Combinatorial Tasks.- Quantum computing for combinatorial optimization.- Graph-based diffusion solvers for combinatorial optimization.- Neural contextual bandits.- On k-Submodular Function Optimization.- Online Influence Maximization.- Facility location.- Popularity Prediction with Deep Learning.- The Metric Dimension of a Graph and its Variants.- Submodular and Nonsubmodular Optimization in Study of Social Networks.- Efficient Relaxation and Rounding Techniques for Submodular Optimization.- Monotone and Nonmonotone DR-submodular Maximization.- Index.