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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Stochastic Algorithms in Linear Algebra - beyond the Markov Chains and von Neumann - Ulam Scheme
verfasst von : Karl Sabelfeld
Erschienen in: Numerical Methods and Applications
Verlag: Springer Berlin Heidelberg
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Sparsified Randomization Monte Carlo (SRMC) algorithms for solving systems of linear algebraic equations introduced in our previous paper [34] are discussed here in a broader context. In particular, I present new randomized solvers for large systems of linear equations, randomized singular value (SVD) decomposition for large matrices and their use for solving inverse problems, and stochastic simulation of random fields. Stochastic projection methods, which I call here “random row action” algorithms, are extended to problems which involve systems of equations and constrains in the form of systems of linear inequalities.