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This work introduces new developments in the construction, analysis, and implementation of parallel computing algorithms. This book presents 23 self-contained chapters, including surveys, written by distinguished researchers in the field of parallel computing. Each chapter is devoted to some aspects of the subject: parallel algorithms for matrix computations, parallel optimization, management of parallel programming models and data, with the largest focus on parallel scientific computing in industrial applications.Key features include: construction and analysis of parallel algorithms for linear algebra and optimization problems; different aspects of parallel architectures, including distributed memory computers with multicore processors; a wide range of industrial applications: parallel simulation of flows through oil filters as well as in porous and gas media, jet aerodynamics, heat conduction in electrical cables, nonlinear optics processes in tapered lasers, and molecular and cell dynamics.

519.22 --- Curve fitting --- -Estimation theory --- -Estimating techniques --- Least squares --- Mathematical statistics --- Stochastic processes --- Fitting, Curve --- Numerical analysis --- Smoothing (Numerical analysis) --- Statistics --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- Statistical theory. Statistical models. Mathematical statistics in general --- Congresses --- Graphic methods --- Estimation theory --- Industrial engineering --- Parallel algorithms --- Parallel processing (Electronic computers) --- Congresses. --- Mathematics --- Industrial applications --- Numerical approximation theory --- Approximation numérique. --- -Congresses --- Approximation numérique --- Numerical analysis. --- Statistique non paramétrique --- -Fitting, Curve --- Estimating techniques --- Approximation et developpements

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This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics. The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence. All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible. The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. "'From curve fitting to machine learning' is ... a useful book. ... It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results. All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code." Leslie A. Piegl (Review of the first edition, 2012).

Computer Science --- Mechanical Engineering - General --- Mechanical Engineering --- Engineering & Applied Sciences --- Science --- Curve fitting. --- Machine learning. --- Computational intelligence. --- Statistical methods. --- Intelligence, Computational --- Learning, Machine --- Fitting, Curve --- Artificial intelligence --- Machine theory --- Numerical analysis --- Least squares --- Smoothing (Numerical analysis) --- Statistics --- Soft computing --- Graphic methods --- Artificial intelligence. --- Engineering mathematics. --- Data mining. --- Big data. --- Mathematical optimization. --- Artificial Intelligence. --- Mathematical and Computational Engineering. --- Data Mining and Knowledge Discovery. --- Big Data/Analytics. --- Optimization. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Data sets, Large --- Large data sets --- Data sets --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Engineering --- Engineering analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Fifth generation computers --- Neural computers --- Mathematics --- Applied mathematics.

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Numerical approximation theory --- Computer. Automation --- Mathematical statistics --- Curve fitting --- Least squares --- Multivariate analysis --- Courbes empiriques --- Moindres carrés --- Analyse multivariée --- Data processing --- Informatique --- CURVE FITTING --- data processing --- Operations Research. --- Probability. --- Engineering. --- 517.9 --- wiskunde --- kanstheorie --- statistiek --- combinatieleer --- -Least squares --- -Multivariate analysis --- -#WWIS:IBM/STAT --- 517.518.8 --- 519.6 --- 681.3*G12 --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Matrices --- Method of least squares --- Squares, Least --- Geodesy --- Mathematics --- Probabilities --- Triangulation --- Fitting, Curve --- Numerical analysis --- Smoothing (Numerical analysis) --- Statistics --- Engineerings --- Research, Operations --- Decision Theory --- Game Theory --- Information Theory --- Differential equations. Integral equations. Other functional equations. Finite differences. Calculus of variations. Functional analysis --- Approximation of functions by polynomials and their generalizations --- Computational mathematics. Numerical analysis. Computer programming --- Approximation: chebyshev; elementary function; least squares; linear approximation; minimax approximation and algorithms; nonlinear and rational approximation; spline and piecewise polynomial approximation (Numerical analysis) --- Graphic methods --- Engineering --- Operations research --- Probability --- Data processing. --- Operations research. --- 681.3*G12 Approximation: chebyshev; elementary function; least squares; linear approximation; minimax approximation and algorithms; nonlinear and rational approximation; spline and piecewise polynomial approximation (Numerical analysis) --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- 517.518.8 Approximation of functions by polynomials and their generalizations --- 517.9 Differential equations. Integral equations. Other functional equations. Finite differences. Calculus of variations. Functional analysis --- Moindres carrés --- Analyse multivariée --- Operations Research --- #WWIS:IBM/STAT --- Operational Research --- Research, Operational --- Statistique mathématique --- Statistique mathématique --- Analyse factorielle --- Factor analysis --- Statistique mathématique. --- Simulation, Méthodes de --- Curve fitting - Data processing --- Least squares - data processing --- Multivariate analysis - data processing