openopt / copt
A Python library for mathematical optimization
☆139Updated 6 months ago
Alternatives and similar repositories for copt:
Users that are interested in copt are comparing it to the libraries listed below
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆98Updated last year
- CHOP: An optimization library based on PyTorch, with applications to adversarial examples and structured neural network training.☆77Updated last year
- Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.☆232Updated last year
- PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.☆86Updated 2 months ago
- Example codes for the book Applied Stochastic Differential Equations☆189Updated 3 years ago
- ☆214Updated 3 years ago
- Large-scale, multi-GPU capable, kernel solver☆186Updated 9 months ago
- A generic interface for linear algebra backends☆73Updated last month
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆103Updated last year
- Parameter-Free Optimizers for Pytorch☆122Updated 11 months ago
- Proximal optimization in pure python☆118Updated 2 years ago
- Functional tensors for probabilistic programming☆239Updated last year
- Gaussian process modelling in Python☆222Updated 4 months ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 5 years ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆124Updated 6 months ago
- Documentation:☆120Updated last year
- Gaussian Processes for Sequential Data☆18Updated 4 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Differentiation through cone programs☆98Updated 2 months ago
- Normalizing Flows using JAX☆83Updated last year
- Bayesian algorithm execution (BAX)☆49Updated 3 years ago
- ☆166Updated 8 months ago
- Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"☆169Updated 3 years ago
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)☆117Updated last year
- Fast hyperparameter settings for non-smooth estimators:☆40Updated last year
- Manifold-learning flows (ℳ-flows)☆228Updated 4 years ago
- Differentiable and numerically stable implementation of the matrix exponential☆33Updated 4 years ago
- Manifold Markov chain Monte Carlo methods in Python☆230Updated 2 months ago