Matrix computations (3rd ed.)
Parallel resolvent Monte Carlo algorithms for linear algebra problems
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Mixed Monte Carlo Parallel Algorithms for Matrix Computation
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
Parallel Monte Carlo Algorithms for Sparse SLAE Using MPI
Proceedings of the 6th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Coarse Grained Parallel Monte Carlo Algorithms for Solving SLAE Using PVM
Proceedings of the 5th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Advanced scalable algorithms for advanced architectures
CompSysTech '09 Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
On scalability behaviour of Monte Carlo sparse approximate inverse for matrix computations
ScalA '13 Proceedings of the Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems
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In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on hybrid Monte Carlo algorithms for matrix inversion and solving systems of linear algebraic equations. This algorithm consists of two parts, approximate inversion by Monte Carlo and iterative refinement using a deterministic method. Here we present a parallel hybrid Monte Carlo algorithm, which uses Monte Carlo to generate an approximate inverse and that improves the accuracy of the inverse with an iterative refinement. The new algorithm is applied efficiently to sparse non-singular matrices. When we are solving a system of linear algebraic equations, Bx = b, the inverse matrix is used to compute the solution vector x = B−1b. We present results that show the efficiency of the parallel hybrid Monte Carlo algorithm in the case of sparse matrices.