Alternatives and similar repositories for andersoncd

Users that are interested in andersoncd are comparing it to the libraries listed below

• TheoGuyard / El0ps
  El0ps: An Exact L0-Problem Solver
  ☆10Updated 2 weeks ago
• josephsalmon / OrganizationFiles
  ☆13Updated 4 months ago
• QB3 / sparse-ho
  Fast hyperparameter settings for non-smooth estimators:
  ☆40Updated 2 years ago
• mathurinm / libsvmdata
  Python package to fetch data from the LIBSVM website.
  ☆20Updated last month
• cvxgrp / a2dr
  Anderson accelerated Douglas-Rachford splitting
  ☆29Updated 4 years ago
• f-dangel / phd-thesis
  Source code for my PhD thesis: Backpropagation Beyond the Gradient
  ☆20Updated 2 years ago
• eepperly / Randomly-Pivoted-Cholesky
  Code for the paper "Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations"
  ☆29Updated 8 months ago
• pnkraemer / tornadox
  Probabilistic ODE solvers are fun, but are they fast? See also: https://github.com/pnkraemer/probdiffeq for JAX code or https://github.c…
  ☆21Updated 11 months ago
• pmelchior / proxmin
  Proximal optimization in pure python
  ☆118Updated 3 years ago
• aterenin / SparseGPAX
  An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.
  ☆22Updated 4 years ago
• alisaab / l0bnb
  Solves the best subset selection problem
  ☆44Updated 3 years ago
• ylefay / bayesianSDEsolver
  Efficient SDE samplers including Gaussian-based probabilistic solvers. Written in JAX.
  ☆10Updated 4 months ago
• benchopt / benchmark_lasso
  Benchopt benchmark for Lasso
  ☆14Updated last year
• gpeyre / 2021-NonCvxPro
  ☆11Updated 4 years ago
• aterenin / SparseGaussianProcesses.jl
  A Julia implementation of sparse Gaussian processes via path-wise doubly stochastic variational inference.
  ☆33Updated 4 years ago
• spdes / chirpgp
  Chirp instantaneous frequency estimation using stochastic differential equation Gaussian processes
  ☆12Updated 7 months ago
• jgorham / SteinDiscrepancy.jl
  Practical tools for quantifying how well a sample approximates a target distribution
  ☆27Updated 4 years ago
• probabilistic-numerics / probnum-spring-school
  Tutorial materials of the Probabilistic Numerics Spring School.
  ☆34Updated 2 years ago
• AaltoML / PeriodicBNN
  Code for 'Periodic Activation Functions Induce Stationarity' (NeurIPS 2021)
  ☆19Updated 3 years ago
• benchopt / template_benchmark
  ☆26Updated 5 months ago
• wesselb / varz
  Painless optimisation of constrained variables in AutoGrad, TensorFlow, PyTorch, and JAX
  ☆23Updated 2 years ago
• sremes / nssm-gp
  Non-stationary spectral mixture kernels implemented in GPflow
  ☆28Updated 6 years ago
• marvinpfoertner / linpde-gp
  Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"
  ☆30Updated last year
• sremes / nonstationary-spectral-kernels
  Implementation for Non-stationary Spectral Kernels (NIPS 2017)
  ☆20Updated 5 years ago
• gianlucadetommaso / Stein-variational-samplers
  Python and MATLAB code for Stein Variational sampling methods
  ☆25Updated 6 years ago
• JuliaFirstOrder / ProximalOperators.jl
  Proximal operators for nonsmooth optimization in Julia
  ☆134Updated last year
• carnotresearch / cr-sparse
  Functional models and algorithms for sparse signal processing
  ☆90Updated last year
• vr308 / Generalised-Gaussian-Processes
  Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
  ☆22Updated last year
• PerformanceEstimation / PEPit
  PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.
  ☆88Updated 5 months ago
• cvxgrp / aa
  Anderson Acceleration
  ☆20Updated 3 years ago