Iterative Algorithms for Solution of Large Sparse Systems of Linear Equations on Hypercubes
IEEE Transactions on Computers
Introduction to Parallel & Vector Solution of Linear Systems
Introduction to Parallel & Vector Solution of Linear Systems
Sparse matrix computations on parallel processor arrays
SIAM Journal on Scientific Computing
Data distributions for sparse matrix vector multiplication
Parallel Computing
Parallelization techniques for sparse matrix applications
Journal of Parallel and Distributed Computing - Special issue on compilation techniques for distributed memory systems
Conjugate gradient and Lanczos methods for sparse matrices on distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
An Efficient Parallel Algorithm for the Solution of a Tridiagonal Linear System of Equations
Journal of the ACM (JACM)
Fast parallel direct solvers for Coarse Grid problems
Journal of Parallel and Distributed Computing
Experimental evaluation of automatic array alignment in parallelized Matlab
Journal of Parallel and Distributed Computing
A parallel solver for large-scale Markov chains
Applied Numerical Mathematics - Developments and trends in iterative methods for large systems of equations—in memoriam Rüdiger Weiss
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A faster algorithm for solving linear algebraic equations on the star graph
Journal of Parallel and Distributed Computing
Parallel scheduling of the PCG method for banded matrices rising from FDM/FEM
Journal of Parallel and Distributed Computing
Efficient parallel solutions of linear algebraic circuits
Journal of Parallel and Distributed Computing
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This study proposes an algorithm capable of working in parallel for solving variable statistics with large and sparse linear equations under given right hand side ranges. A comparative study to the direct linear programming method is conducted under a main central processor and up to four parallel processors. The studied results are reported computationally and discussed. Moreover, the approach can be adapted for the system under domain decompositions structure leading to a better efficiency experimentally in a case example.