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This volume describes the principles and history behind the use of Krylov subspace methods in science and engineering. The outcome of the analysis is very practical and indicates what can and cannot be expected from the use of Krylov subspace methods challenging some common assumptions and justifications of standard approaches.

Sparse matrices. --- Mathematical optimization. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Spare matrix techniques --- Matrices --- Sparse matrices

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Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF). The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.

Mobile robots. --- Robots --- Sparse matrices. --- Robotics. --- Mappings (Mathematics) --- Maps (Mathematics) --- Functions --- Functions, Continuous --- Topology --- Transformations (Mathematics) --- Automation --- Machine theory --- Spare matrix techniques --- Matrices --- Robot control --- Robotics --- Control systems.

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Harry M Markowitz received the Nobel Prize in Economics in 1990 for his pioneering work in portfolio theory. He also received the von Neumann Prize from the Institute of Management Science and the Operations Research Institute of America in 1989 for his work in portfolio theory, sparse matrices and the SIMSCRIPT computer language. While Dr Markowitz is well-known for his work on portfolio theory, his work on sparse matrices remains an essential part of linear optimization calculations. In addition, he designed and developed SIMSCRIPT - a computer programming language. SIMSCRIPT has been widely

Investment analysis. --- Portfolio management. --- Sparse matrices. --- Analyse financière --- Gestion de portefeuille --- Matrices éparses --- Portfolio management --- -Investment analysis --- -Sparse matrices --- -330.9 --- Spare matrix techniques --- Matrices --- Analysis of investments --- Analysis of securities --- Security analysis --- Investment management --- Investment analysis --- Investments --- Securities --- Electronic information resources --- E-books --- AA / International- internationaal --- 305.91 --- 339.4 --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles. --- Vermogensbeheer. Financiële analyse. Verspreiding van de beleggingsrisico's. --- Analyse financière --- Matrices éparses --- Sparse matrices --- Econometrie van de financiële activa. Portfolio allocation en management. CAPM. Bubbles --- Vermogensbeheer. Financiële analyse. Verspreiding van de beleggingsrisico's

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Numerical analysis --- Sparse matrices --- data processing --- -519.6 --- 681.3*G13 --- Spare matrix techniques --- Matrices --- Data processing --- Computational mathematics. Numerical analysis. Computer programming --- Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- 681.3*G13 Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- 519.6 --- Sparse matrices - data processing

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Sparse matrices --- Matrices éparses --- Data processing --- Informatique --- -519.6 --- 681.3*G13 --- Spare matrix techniques --- Matrices --- Computational mathematics. Numerical analysis. Computer programming --- Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- Data processing. --- 681.3*G13 Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- Matrices éparses --- 519.6 --- Sparse matrices - Data processing