Analysis and design of algorithms. A critical comparison of different works on algorithms

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Design and Analysis of Algorithms : modern Study of Computer Algorithms

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Algorithms: Design And Analysis

Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer science>Mathematical theory of computation>Maths for comput...

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The Design and Analysis of Computer Algorithms

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Basics in Design and Analysis of Algorithms

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Computer Algorithms: Design, Analysis and Applications

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Neurological Algorithms for Brain MRI of Neonate and Premature Infants : Analysis Design

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Design and Analysis of Algorithms

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Design and Analysis of Computer Algorithms

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Computer Algorithms, Introduction to Design and Analysis

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Design and Analysis of Algorithms

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Experimental Methods for the Analysis of Optimization Algorithms Springer-Verlag Berlin and Heidelberg GmbH & Co....

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In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. §§This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributors include leading scientists in algorithm design, statistical design, optimization and heuristics, and every chapter is enriched with case studies.

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Mathematical Theory of Design: Foundations, Algorithms and Applications Springer-Verlag New York Inc.

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This book is extremely important to the field because it presents a unified framework for theory and practice, which is proven theoretically and by many cases from various engineering fields. Specifically, there are four major aspects that make the book unique: 1) The book develops a formal systems approach to design (including theorems, analysis of principles of design and information theory). 2) Methods for efficient design are rigorously derived from the theory, based on AI and optimization. 3) The scope of the book covers many engineering disciplines under a unified framework, including mechanical, electrical, civil, industrial, manufacturing systems, and apparel industry. Thus, it applies to readers from various disciplines, theoreticians and practitioners. DFM and DFA are also covered. 4) The book provides the methodology for developing the next generation computer software for advanced intelligent CAD (Computer Aided Design). Audience: The book is especially appropriate for courses in engineering (mechanical, industrial, electrical, and civil). It is also a useful reference for practicing engineers interested in design and optimization, and for applied mathematicians and computer software developers engaged in smart CAD applications.

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Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques Springer, Berlin

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This book constitutes the joint refereed proceedings of the 10th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2007 and the 11th International Workshop on Randomization and Computation, RANDOM 2007, held in Princeton, NJ, USA, in August 2007.§The 44 revised full papers presented were carefully reviewed and selected from 99 submissions. Topics of interest covered by the papers are design and analysis of approximation algorithms, hardness of approximation, small space and data streaming algorithms, sub-linear time algorithms, embeddings and metric space methods, mathematical programming methods, coloring and partitioning, cuts and connectivity, geometric problems, game theory and applications, network design and routing, packing and covering, scheduling, design and analysis of randomized algorithms, randomized complexity theory, pseudorandomness and derandomization, random combinatorial structures, random walks/Markov chains, expander graphs and randomness extractors, probabilistic proof systems, random projections and embeddings, error-correcting codes, average-case analysis, property testing, computational learning theory, and other applications of approximation and randomness.

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Data Structures and Algorithms in Python John Wiley & Sons

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Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects.

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