Stochastic iterative projection methods for large linear systems
-
Karl Sabelfeld
and
Nadja Loshchina
Abstract
We suggest a randomized version of the projection methods belonging to the class of a “row-action” methods which work well both for systems with quadratic nonsingular matrices and for overdetermined systems. These methods belong to a type known as Projection on Convex Sets methods. Here we present a method beyond the conventional Markov chain based Neumann–Ulam scheme. The main idea is in a random choice of blocks of rows in the projection method so that in average, the convergence is improved compared to the conventional periodic choice of the rows. We suggest an acceleration of the row projection method by using the Johnson–Lindenstrauss (J–L) theorem to find, among the randomly chosen rows, in a sense an optimal row. We extend this randomized method for solving linear systems coupled with systems of linear inequalities. Applied to finite-difference approximations of boundary value problems, the method appears to be an extremely efficient Random Walk algorithm whose convergence is exponential, and the cost does not depend on the dimension of the matrix. In addition, the algorithm calculates the solution in all grid points, and is easily parallelizable.
© de Gruyter 2010
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Articles in the same Issue
- Editiorial
- Random packing of hyperspheres and Marsaglia's parking lot test
- Diffusion in a nonhomogeneous medium: quasi-random walk on a lattice
- Improved Halton sequences and discrepancy bounds
- Generalizing Sudoku to three dimensions
- Adaptive integration and approximation over hyper-rectangular regions with applications to basket option pricing
- Exact simulation of Bessel diffusions
- A good permutation for one-dimensional diaphony
- Error bounds for computing the expectation by Markov chain Monte Carlo
- Stochastic iterative projection methods for large linear systems
- Increasing the number of inner replications of multifactor portfolio credit risk simulation in the t-copula model
- A genetic algorithm approach to estimate lower bounds of the star discrepancy
- Random and deterministic fragmentation models
- MCMC imputation in autologistic model
