This book constitutes the refereed proceedings of the 10th International Conference on Combinatorial Optimization and Applications, COCOA 2016, held in Hong Kong, China, in December 2016. The 60 full papers included in the book were carefully reviewed and selected from 122 submissions. The papers are organized in topical sections such as graph theory, geometric optimization, complexity and data structure, combinatorial optimization, and miscellaneous.

Written by prominent experts in the field, this monograph provides the first comprehensive, unified presentation of the structural, algorithmic and applied aspects of the theory of Boolean functions. The book focuses on algebraic representations of Boolean functions, especially disjunctive and conjunctive normal form representations. This framework looks at the fundamental elements of the theory (Boolean equations and satisfiability problems, prime implicants and associated short representations, dualization), an in-depth study of special classes of Boolean functions (quadratic, Horn, shellable, regular, threshold, read-once functions and their characterization by functional equations) and two fruitful generalizations of the concept of Boolean functions (partially defined functions and pseudo-Boolean functions). Several topics are presented here in book form for the first time. Because of the depth and breadth and its emphasis on algorithms and applications, this monograph will have special appeal for researchers and graduate students in discrete mathematics, operations research, computer science, engineering and economics.

This book constitutes the joint refereed proceedings of the 17th International Workshop on Computer Science Logic, CSL 2003, held as the 12th Annual Conference of the EACSL and of the 8th Kurt Gödel Colloquium, KGC 2003 in Vienna, Austria, in August 2003. The 30 revised full papers presented together with abstracts of 9 invited presentations were carefully reviewed and selected from a total of 112 submissions. All current aspects of computer science logic are addressed ranging from mathematical logic and logical foundations to the application of logics in various computing aspects.

The topic of this book is the following optimisation problem: given a set of discrete variables and a set of functions, each depending on a subset of the variables, minimise the sum of the functions over all variables. This fundamental research problem has been studied within several different contexts of discrete mathematics, computer science and artificial intelligence under different names: Min-Sum problems, MAP inference in Markov random fields (MRFs) and conditional random fields (CRFs), Gibbs energy minimisation, valued constraint satisfaction problems (VCSPs), and, for two-state variables, pseudo-Boolean optimisation. In this book the author presents general techniques for analysing the structure of such functions and the computational complexity of the minimisation problem, and he gives a comprehensive list of tractable cases. Moreover, he demonstrates that the so-called algebraic approach to VCSPs can be used not only for the search for tractable VCSPs, but also for other questions such as finding the boundaries to the applicability of certain algorithmic techniques. The book is suitable for researchers interested in methods and results from the area of constraint programming and discrete optimisation.

The 31st International Colloquium on Automata, Languages, and Programming (ICALP 2004) was held from July 12 to July 16 in Turku, Finland. This volume contains all contributed papers presented at ICALP 2004, together with the invitedlecturesbyPhilippeFlajolet(INRIA), RobertHarper(CarnegieMellon), Monika Henzinger (Google), Martin Hofmann (Munich), Alexander Razborov (Princeton and Moscow), Wojciech Rytter (Warsaw and NJIT), and Mihalis Yannakakis (Stanford). ICALP is a series of annual conferences of the European Association for Theoretical Computer Science (EATCS). The?rst ICALP took place in 1972 and the ICALP program currently consists of track A (focusing on algorithms, automata, complexity, and cryptography) and track B (focusing on databases, logics, semantics, and principles of programming). Inresponsetothecallforpapers, theprogramcommitteereceived379papers, 272 for track A and 107 for track B. This is the highest number of submitted papersinthehistoryofICALPconferences. Theprogramcommitteesselected97 papersforinclusionintothescienti?cprogram. Theprogramcommitteefortrack A met on March 27 and 28 in Barcelona and selected 69 papers from track A. TheprogramcommitteefortrackBselected28papersfromtrackBinthecourse of an electronic discussion lasting for two weeks in the second half of March. The selections were based on originality, quality, and relevance to theor- ical computer science. We wish to thank all authors who submitted extended abstracts for consideration, the program committee for its hard work, and all referees who assisted the program committee in the evaluation process.

This book constitutes the refereed proceedings of the 21st International Symposium on Fundamentals of Computation Theory, FCT 2017, held in Bordeaux, France, in September 2017. The 29 revised full papers and 5 invited papers presented were carefully reviewed and selected from 99 submissions. The papers cover topics of all aspects of theoretical computer science, in particular algorithms, complexity, formal and logical methods.

The Annual Conference of the European Association for Computer Science Logic, CSL 2002, was held in the Old College of the University of Edinburgh on 22–25 September 2002. The conference series started as a programme of Int- national Workshops on Computer Science Logic, and then in its sixth meeting became the Annual Conference of the EACSL. This conference was the sixteenth meeting and eleventh EACSL conference; it was organized by the Laboratory for Foundations of Computer Science at the University of Edinburgh. The CSL 2002 Programme Committee considered 111 submissions from 28 countries during a two week electronic discussion; each paper was refereed by at least three reviewers. The Committee selected 37 papers for presentation at the conference and publication in these proceedings. The Programme Committee invited lectures from Susumu Hayashi, Frank Neven, and Damian Niwinski; ́ the papers provided by the invited speakers appear at the front of this volume. In addition to the main conference, two tutorials – ‘Introduction to Mu- Calculi’ (Julian Brad?eld) and ‘Parametrized Complexity’ (Martin Grohe) – were given on the previous day.

Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics. The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area. The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas. The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming. - Covers the whole field of constraint programming - Survey-style chapters - Five chapters on applications

Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.