Exploring JAX-compatible sparse Cholesky decomposition, focusing on symbolic factorization and JAX's control flow challenges.
Part 6 of a series on making sparse linear algebra differentiable in JAX, focusing on implementing Jacobian-vector products for custom primitives.
Part five of a series on implementing differentiable sparse linear algebra in JAX, focusing on registering new JAX-traceable primitives.
Explores design options for implementing autodifferentiable sparse matrices in JAX to accelerate statistical models, focusing on avoiding redundant computations.
Explores challenges integrating sparse Cholesky factorizations with JAX for faster statistical inference in PyMC.
Explores the Cholesky factorization algorithm for sparse matrices, detailing its mathematical derivation and computational considerations.
Explores using sparse linear algebra to speed up Bayesian inference for linear mixed models and generalizations, with a focus on Python/JAX prototyping.
A developer's final report on a Google Summer of Code project to integrate sparse and dense matrix support in QuTiP's core Qobj data type.
Explores using sparse matrix techniques in R to efficiently calibrate survey weights for large-scale population data.
