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LSQR is an iterative method for solving large, sparse, linear systems of equations and linear least-squares problems, including under- or over-determined and rank-deficient systems. It uses the Lanczos bidiagonalization process to provide a robust alternative to conjugate gradients, offering better numerical stability. Solver
The Conjugate Gradient (CG) method is an efficient iterative algorithm for solving large, sparse systems of linear equations where the matrix is symmetric and positive-definite. It finds the minimum of a quadratic function by generating conjugate search directions, ensuring convergence in at most steps for an matrix.Solver
Pure-Rust sparse direct solver (symmetric LDLᵀ + unsymmetric LU) and preconditioner. A PARDISO-style, type-agnostic, embeddable replacement with no native dependencies.
In this repository, you will find a serial, shared-memory parallel, distributed-memory parallel and hybrid implementations of the conjugate gradient optimization simulation. This is a sparse linear solver optimized using the second-order poisson equation.
An Go native implementation of the Map Reduce parallel framework for a sparse linear solver utilizing conjugate gradient to solve the Poisson equation.