Hierarchical solvers is an approximate sparse direct solver, written entirely in Julia.
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Updated
Aug 4, 2022 - Julia
Hierarchical solvers is an approximate sparse direct solver, written entirely in Julia.
Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian learning, and basis pursuit.
Algebraic Multigrid in Julia
Algorithms from circuit theory to predict connectivity in heterogeneous landscapes
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