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Abstrak :
Unlike other books on theoretical computer science, this textbook organizes approximation algorithms into chapters based on the design techniques for the algorithms. based on the design techniques for the algorithms. It helps the reader to better understand the design and analysis techniques for approximation algorithms.

Abstrak :
This book follows the “semidefinite side” of these developments, presenting some of the main ideas behind approximation algorithms based on semidefinite programming. It develops the basic theory of semidefinite programming, presents one of the known efficient algorithms in detail, and describes the principles of some others. It also includes applications, focusing on approximation algorithms.

Abstrak :
This book constitutes the thoroughly refereed post-proceedings of the 9th International Workshop on Approximation and Online Algorithms, WAOA 2011, held in Saarbrücken, Germany, in September 2011. The 21 papers presented were carefully reviewed and selected from 48 submissions. The volume also contains an extended abstract of the invited talk of Prof. Klaus Jansen. The Workshop on Approximation and Online Algorithms focuses on the design and analysis of algorithms for online and computationally hard problems. Both kinds of problems have a large number of applications in a wide variety of fields. Topics of interest for WAOA 2011 were: algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, geometric problems, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, parameterized complexity, randomization techniques and scheduling problems.

Abstrak :
Data approximation by low-complexity models details the theory, algorithms, and applications of structured low-rank approximation. Efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. Much of the text is devoted to describing the applications of the theory including, system and control theory, signal processing, computer algebra for approximate factorization and common divisor computation, computer vision for image deblurring and segmentation, machine learning for information retrieval and clustering, bioinformatics for microarray data analysis, chemometrics for multivariate calibration, and psychometrics for factor analysis.

Abstrak :
In this brief the authors discuss some important subclasses of polynomial optimization models arising from various applications, with a focus on approximations algorithms with guaranteed worst case performance analysis. The brief presents a clear view of the basic ideas underlying the design of such algorithms and the benefits are highlighted by illustrative examples showing the possible applications.