openopt / coptLinks
A Python library for mathematical optimization
☆141Updated 10 months ago
Alternatives and similar repositories for copt
Users that are interested in copt are comparing it to the libraries listed below
Sorting:
- Large-scale, multi-GPU capable, kernel solver☆190Updated 3 weeks ago
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆100Updated 2 years ago
- Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.☆234Updated last year
- Example codes for the book Applied Stochastic Differential Equations☆195Updated 4 years ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆104Updated last year
- Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces☆199Updated 3 years ago
- ☆214Updated 3 years ago
- Proximal optimization in pure python☆118Updated 3 years ago
- CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.☆78Updated last year
- ☆169Updated last year
- PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.☆88Updated this week
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- Functional tensors for probabilistic programming☆242Updated last year
- This is a collection of code samples aimed at illustrating temporal parallelization methods for sequential data.☆33Updated last year
- Robust initialisation of inducing points in sparse variational GP regression models.☆33Updated 2 years ago
- Making your benchmark of optimization algorithms simple and open☆266Updated last week
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Gaussian process modelling in Python☆223Updated 7 months ago
- Documentation:☆121Updated 2 years ago
- A manifold optimization library for deep learning☆248Updated 3 years ago
- Package implementing various parametric and nonparametric methods for conditional density estimation☆196Updated 2 years ago
- Automated Scalable Bayesian Inference☆131Updated 3 years ago
- Sequential Neural Likelihood☆40Updated 5 years ago
- Manifold-learning flows (ℳ-flows)☆230Updated 4 years ago
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆127Updated 9 months ago
- Just a little MCMC☆227Updated last year
- Fast solver for L1-type problems: Lasso, sparse Logisitic regression, Group Lasso, weighted Lasso, Multitask Lasso, etc.☆229Updated last month