Basic Machine Learning implementation with python
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Updated
Jul 1, 2020 - Jupyter Notebook
Basic Machine Learning implementation with python
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…
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Newton and Quasi-Newton optimization with PyTorch
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.
Optimization course assignments under the supervision of Dr. Maryam Amirmazlaghani
Optimization course for MSAI at MIPT
This package is dedicated to high-order optimization methods. All the methods can be used similarly to standard PyTorch optimizers.
If you find any errors in the work of algorithms, you can fix them by creating a pull request
Drawing Newton's fractal using pure js, rust-wasm, SIMDs, threads and GPU
Hessian-based stochastic optimization in TensorFlow and keras
A Unified Pytorch Optimizer for Numerical Optimization
Polynomial essentials for Golang including real root isolation, complex root solving methods, root bounds, and derivatives.
Python and MATLAB code for Stein Variational sampling methods
Solving problems from the course on the basics of computational physics
Implementation and visualization (some demos) of search and optimization algorithms.
ForSolver - linear and nonlinear solvers
Optimization Techniques Lab Dump
Implementation of Unconstrained minimization algorithms. These are listed below:
Collection of methods for numerical analysis and scientific computing, including numerical root-finders, numerical integration, linear algebra, and data visualization. Created for APPM4600 at CU Boulder.
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