Nonnegative Tensor Decomposition
-
Updated
Jun 12, 2023 - MATLAB
Nonnegative Tensor Decomposition
Self-concordant Smoothing for Large-Scale Convex Composite Optimization
Modeling language and tools for constrained, structured optimization problems
Repository for the MVA Optimization courses.
Selected machine learning projects from courses I have taken during my PhD
Provides proximal operator evaluation routines and proximal optimization algorithms, such as (accelerated) proximal gradient methods and alternating direction method of multipliers (ADMM), for non-smooth/non-differentiable objective functions.
Symbolic code (using symbolic computation toolbox of matlab) for verifying the analytical convergence rate for the proximal gradient method of the preprint "Exact worst-case convergence rates of the proximal gradient method for composite convex minimization"
A Python implementation of Generalized Fused Lasso from https://arxiv.org/pdf/1801.05413.pdf
CoCaIn BPG escapes Spurious Stationary Points
This repository provides Matlab codes related to the paper "Fast reconstruction of sparse relative impulse responses via second-order cone programming" presented at WASPAA 2017 workshop
Q. Yao, J. Xu, W. Tu, Z. Zhu. Efficient Neural Architecture Search via Proximal Iterations. AAAI 2020.
PNPG algorithm implementation and examples
Solving inverse problems with Proximal Markov Chain Monte Carlo
Fast Inertial Algorithm for Phase Retrieval
Implementation of Collective Matrix Completion by Mokhtar Z. Alaya and Olga Klopp https://arxiv.org/abs/1807.09010
Second-Order Convergence of Alternating Minimizations
Bazinga.jl: a toolbox for constrained composite optimization
Proximal operators for use with RegularizedOptimization
Test Cases for Regularized Optimization
Add a description, image, and links to the proximal-algorithms topic page so that developers can more easily learn about it.
To associate your repository with the proximal-algorithms topic, visit your repo's landing page and select "manage topics."