Newton and Quasi-Newton optimization with PyTorch
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Updated
Mar 10, 2024 - Python
Newton and Quasi-Newton optimization with PyTorch
A next-gen Lagrange-Newton solver for nonconvex optimization. It unifies barrier and SQP methods in a modern and generic way, and implements different globalization flavors (line search/trust region and merit function/filter method/funnel method). Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.
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…
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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.
Implementation and analysis of convex optimization algorithms
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
ForSolver - linear and nonlinear solvers
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.
Optimization course assignments under the supervision of Dr. Maryam Amirmazlaghani
C++11 implementation of numerical algorithms described in Numerical Analysis by Richard L. Burden and J. Douglas Faires
Newton’s second-order optimization methods in python
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