Combinatorial Optimization

The major purpose of this book is to introduce the main concepts of discrete optimization problems which have a finite number of feasible solutions. Following common practice, we term this topic combinatorial optimization. There are now a number of excellent graduate-level textbooks on combina­ torial optimization. However, there does not seem to exist an undergraduate text in this area. This book is designed to fill this need. The book is intended for undergraduates in mathematics, engineering, business, or the physical or social sciences. It may also be useful as a reference text for practising engineers and scientists.

This book highlights new and original contributions on Graph Theory and Combinatorial Optimization both from the theoretical point of view and from applications in all fields. The book chapters describe models and methods based on graphs, structural properties, discrete optimization, network optimization, mixed-integer programming, heuristics, meta-heuristics, math-heuristics, and exact methods as well as applications.

This book constitutes the refereed proceedings of the 23rd International Conference on Integer Programming and Combinatorial Optimization, IPCO 2022, held in Eindhoven, The Netherlands, in June 2022. The 33 full papers presented were carefully reviewed and selected from 93 submissions addressing key techniques of document analysis. IPCO is under the auspices of the Mathematical Optimization Society, and it is an important forum for presenting the latest results of theory and practice of the various aspects of discrete optimization.

This book constitutes the refereed proceedings of the 23rd International Conference on Integer Programming and Combinatorial Optimization, IPCO 2022, held in Eindhoven, The Netherlands, in June 2022. The 33 full papers presented were carefully reviewed and selected from 93 submissions addressing key techniques of document analysis. IPCO is under the auspices of the Mathematical Optimization Society, and it is an important forum for presenting the latest results of theory and practice of the various aspects of discrete optimization.

Are you looking for new lectures for your course on algorithms, combinatorial optimization, or algorithmic game theory? Maybe you need a convenient source of relevant, current topics for a graduate student or advanced undergraduate student seminar? Or perhaps you just want an enjoyable look at some beautiful mathematical and algorithmic results, ideas, proofs, concepts, and techniques in discrete mathematics and theoretical computer science?

The conference proceeding LNCS 11346 constitutes the refereed proceedings of the 12th International Conference on Combinatorial Optimization and Applications, COCOA 2018, held in Atlanta, GA, USA, in December 2018.

Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Heuristics and Meta-Heuristics

The practical and theoretical relevance of problems to the NP-complete degree vf complexity are widely known. From the practical point of view it is sufficient to remember that in this class we find most of the combinatorial and optimization problems which b,,.·e the widest range of applications, for example scheduling problems, optimization problems on graphs, integer programming etc. As far as the theoretical relevance is concerned ;;.•e should remember that one of the most outstanding problems in Computer Science, the problem of deciding whether any NP-complete set can be recognized in polynomial time, coincides with the problem of knowing whether the computation power of a nondeterministic Turing machine which accepts a set in polynomial time is strictly stronger than the power of ordinary polynomially bounded Turing machines or not. Until recen#y the design of algorithms for finding exact approximate solutions to practical instances of hard combinatorial and optimization problems was the main concern of experts in Operations Research while the study of the complexity of these problems with respect to various computation models and the analysis of general solution techniques was the main interest of computer scientists.