Newton and Quasi-Newton optimization with PyTorch
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Updated
Mar 10, 2024 - Python
Newton and Quasi-Newton optimization with PyTorch
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…
A next-gen solver for optimization with nonconvex objective and constraints. Reimplements filterSQP and IPOPT (barrier) in a modern and generic way, and unlocks a variety of novel methods. Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.
Drawing Newton's fractal using pure js, rust-wasm, SIMDs, threads and GPU
Basic Machine Learning implementation with python
Hessian-based stochastic optimization in TensorFlow and keras
Implementation and visualization (some demos) of search and optimization algorithms.
Python and MATLAB code for Stein Variational sampling methods
This package is dedicated to high-order optimization methods. All the methods can be used similarly to standard PyTorch optimizers.
ForSolver - linear and nonlinear solvers
If you find any errors in the work of algorithms, you can fix them by creating a pull request
Optimization course for MSAI at MIPT
Implementation and analysis of convex optimization algorithms
Prototyping of matrix free Newton methods in Julia
Collection of functions to find roots of functions float → float. Pure OCaml code.
This is a Numerical Analysis course project, implementing numerical analysis methods.
Newton’s second-order optimization methods in python
Optimization course assignments under the supervision of Dr. Maryam Amirmazlaghani
A Unified Pytorch Optimizer for Numerical Optimization
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