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This book provides a comprehensive, modern introduction to convex optimization, a field that is becoming increasingly important in applied mathematics, economics and finance, engineering, and computer science, notably in data science and machine learning. Written by a leading expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It provides readers with a full treatment of the smoothing technique, which has tremendously extended the abilities of gradient-type methods. Several powerful approaches in structural optimization, including optimization in relative scale and polynomial-time interior-point methods, are also discussed in detail. Researchers in theoretical optimization as well as professionals working on optimization problems will find this book very useful. It presents many successful examples of how to develop very fast specialized minimization algorithms. Based on the author’s lectures, it can naturally serve as the basis for introductory and advanced courses in convex optimization for students in engineering, economics, computer science and mathematics.

Complex analysis --- Numerical methods of optimisation --- Operational research. Game theory --- complexe analyse (wiskunde) --- bedrijfssoftware --- machine learning --- automatisering --- wiskunde --- algoritmen --- Mathematical optimization. --- Algorithms. --- Optimization. --- Algorithm Analysis and Problem Complexity.

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Intuitively, a sequence such as 101010101010101010… does not seem random, whereas 101101011101010100…, obtained using coin tosses, does. How can we reconcile this intuition with the fact that both are statistically equally likely? What does it mean to say that an individual mathematical object such as a real number is random, or to say that one real is more random than another? And what is the relationship between randomness and computational power. The theory of algorithmic randomness uses tools from computability theory and algorithmic information theory to address questions such as these. Much of this theory can be seen as exploring the relationships between three fundamental concepts: relative computability, as measured by notions such as Turing reducibility; information content, as measured by notions such as Kolmogorov complexity; and randomness of individual objects, as first successfully defined by Martin-Löf. Although algorithmic randomness has been studied for several decades, a dramatic upsurge of interest in the area, starting in the late 1990s, has led to significant advances. This is the first comprehensive treatment of this important field, designed to be both a reference tool for experts and a guide for newcomers. It surveys a broad section of work in the area, and presents most of its major results and techniques in depth. Its organization is designed to guide the reader through this large body of work, providing context for its many concepts and theorems, discussing their significance, and highlighting their interactions. It includes a discussion of effective dimension, which allows us to assign concepts like Hausdorff dimension to individual reals, and a focused but detailed introduction to computability theory. It will be of interest to researchers and students in computability theory, algorithmic information theory, and theoretical computer science.

Complex analysis --- Computer science --- Computer. Automation --- toegepaste informatica --- complexe analyse (wiskunde) --- computers --- algoritmen --- Computable functions. --- Computational complexity. --- Algorithms. --- Computers. --- Algorithm Analysis and Problem Complexity. --- Theory of Computation. --- Computation by Abstract Devices. --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Algorism --- Algebra --- Arithmetic --- Foundations

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This two-volume set (CCIS 873 and CCIS 874) constitutes the thoroughly refereed proceedings of the 9th International Symposium, ISICA 2017, held in Guangzhou, China, in November 2017. The 101 full papers presented in both volumes were carefully reviewed and selected from 181 submissions. This first volume is organized in topical sections on neural networks and statistical learning: neural architecture search, transfer of knowledge; evolutionary multi-objective and dynamic optimization: optimal control and design, hybrid methods; data mining: association rule learning, data management platforms; Cloud computing and multiagent systems: service models, Cloud engineering; everywhere connectivity: IoT solutions, wireless sensor networks.

Complex analysis --- Computer architecture. Operating systems --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- draadloze computernetwerken --- datamining --- complexe analyse (wiskunde) --- bedrijfssoftware --- cloud computing --- IoT (Internet of Things) --- KI (kunstmatige intelligentie) --- computernetwerken --- data acquisition --- Artificial intelligence. --- Algorithms. --- Data mining. --- Computer communication systems. --- Artificial Intelligence. --- Algorithm Analysis and Problem Complexity. --- Data Mining and Knowledge Discovery. --- Computer Communication Networks.

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In these early years of the 21st Century, researchers in the field of computing are delving ever further into the new possibilities of the science and to the primary tools that form its foundations. The theory behind computation has never been more important. Theory of Computation is a unique textbook that serves the dual purposes of covering core material in the foundations of computing, as well as providing an introduction to some more advanced contemporary topics. This innovative text focuses primarily, although by no means exclusively, on computational complexity theory: the classification of computational problems in terms of their inherent complexity. It incorporates rigorous treatment of computational models, such as deterministic, nondeterministic, and alternating Turing machines; circuits; probabilistic machines; interactive proof systems; automata on infinite objects; and logical formalisms. Although the complexity universe stops at polynomial space in most treatments, this work also examines higher complexity levels all the way up through primitive and partial recursive functions and the arithmetic and analytic hierarchies. Topics and features: • Provides in-depth coverage of both classical and contemporary approaches in one useful, concise volume • Organized into readily applicable, self-contained primary and secondary lectures • Contains more than 180 homework exercises of varying difficulty levels, many with hints and solutions • Includes approximation and inapproximation results, and some lower bounds • Treats complexity theory and classical recursion theory in a unified framework Advanced undergraduates and first-year graduates in Computer Science or Mathematics will receive a thorough grounding in the core theory of computation and computational complexity, as well as an introduction to advanced contemporary topics for further study. Computing professionals and other scientists interested in learning more about these topics will also find this text extremely useful. Prof. Dexter Kozen teaches at Cornell University, Ithaca, New York, and has comprehensively class-tested this book’s content. He authored the highly successful Automata and Computability, which offers an introduction to the basic theoretical models of computability, and The Design and Analysis of Algorithms.

Computer. Automation --- informatica --- informaticaonderzoek --- complexe analyse (wiskunde) --- wiskunde --- Complex analysis --- Computer science --- toegepaste informatica --- Computational complexity. --- Recursion theory. --- Mathematics. --- Information theory. --- Computer science. --- Computer software. --- Theory of Computation. --- Computational Mathematics and Numerical Analysis. --- Computational Science and Engineering. --- Computation by Abstract Devices. --- Algorithm Analysis and Problem Complexity. --- Software, Computer --- Computer systems --- Informatics --- Science --- Communication theory --- Communication --- Cybernetics --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Mathematics --- Computers. --- Computer mathematics. --- Algorithms. --- Algorism --- Algebra --- Arithmetic --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Machine theory --- Calculators --- Cyberspace --- Foundations --- Data processing.

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This book constitutes the refereed proceedings of the 14th International Workshop on Algorithms in Bioinformatics, WABI 2014, held in Wroclaw, Poland, in September 2014. WABI 2014 was one of seven conferences that were organized as part of ALGO 2014. WABI is an annual conference series on all aspects of algorithms and data structure in molecular biology, genomics and phylogeny data analysis. The 26 full papers presented together with a short abstract were carefully reviewed and selected from 61 submissions. The selected papers cover a wide range of topics from sequence and genome analysis through phylogeny reconstruction and networks to mass spectrometry data analysis.

Bioinformatics. --- Algorithms. --- Computer science—Mathematics. --- Discrete mathematics. --- Data mining. --- Computational and Systems Biology. --- Discrete Mathematics in Computer Science. --- Data Mining and Knowledge Discovery. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Algorism --- Algebra --- Arithmetic --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Discrete mathematical structures --- Mathematical structures, Discrete --- Structures, Discrete mathematical --- Numerical analysis --- Foundations --- Data processing --- Complex analysis --- Discrete mathematics --- Biomathematics. Biometry. Biostatistics --- Molecular biology --- Computer science --- Programming --- Computer architecture. Operating systems --- Information systems --- Computer. Automation --- complexiteit --- discrete wiskunde --- complexe analyse (wiskunde) --- bio-informatica --- bedrijfssoftware --- genomics --- computers --- informatica --- biometrie --- database management --- computerkunde --- moleculaire biologie --- data acquisition