This book provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. It describes optimization problems involving commonly used sparsity-enforcing approaches as well as sparse recovery problems that extend the basic formulation to more sophisticated forms of structured sparsity and to different loss functions. The book also covers sparse graphical models, dictionary learning, and sparse matrix factorizations.

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Irina Rish, Genady Grabarnik

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