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Sensor networks. --- Algebra, Homological. --- Greedoids. --- Sparse matrices.

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Equations --- Iterative methods (Mathematics) --- Sparse matrices --- numerical solutions --- congresses --- Congresses --- Numerical solutions --- Equations - numerical solutions - congresses --- Iterative methods (Mathematics) - Congresses --- Sparse matrices - Congresses

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519.61 --- 519.61 Numerical methods of algebra --- Numerical methods of algebra --- Sparse matrices --- Data processing --- Data processing. --- Sparse matrices - Data processing

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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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The present collection is the very first contribution of this type in the field of sparse recovery. Compressed sensing is one of the important facets of the broader concept presented in the book, which by now has made connections with other branches such as mathematical imaging, inverse problems, numerical analysis and simulation. The book consists of four lecture notes of courses given at the Summer School on "Theoretical Foundations and Numerical Methods for Sparse Recovery" held at the Johann Radon Institute for Computational and Applied Mathematics in Linz, Austria, in September 2009. This unique collection will be of value for a broad community and may serve as a textbook for graduate courses. From the contents: "Compressive Sensing and Structured Random Matrices" by Holger Rauhut "Numerical Methods for Sparse Recovery" by Massimo Fornasier "Sparse Recovery in Inverse Problems" by Ronny Ramlau and Gerd Teschke "An Introduction to Total Variation for Image Analysis" by Antonin Chambolle, Vicent Caselles, Daniel Cremers, Matteo Novaga and Thomas Pock

Sparse matrices --- Equations --- Differential equations, Partial --- Numerical solutions --- Matrices éparses --- Equations aux dérivées partielles --- Solutions numériques