MATLAB implementations of a variety of machine learning/signal processing algorithms.
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Updated
Aug 24, 2016 - MATLAB
MATLAB implementations of a variety of machine learning/signal processing algorithms.
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
PNPG algorithm implementation and examples
A Python implementation of Goldstein et. al's FASTA algorithm for convex optimization.
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
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"
Newton-type accelerated proximal gradient method in Julia
A Python implementation of Generalized Fused Lasso from https://arxiv.org/pdf/1801.05413.pdf
Selected machine learning projects from courses I have taken during my PhD
Implementation of Collective Matrix Completion by Mokhtar Z. Alaya and Olga Klopp https://arxiv.org/abs/1807.09010
A small library implementing phase retrieval algorithms for 2D images.
Q. Yao, J. Xu, W. Tu, Z. Zhu. Efficient Neural Architecture Search via Proximal Iterations. AAAI 2020.
Cut-pursuit with preconditioned forward-Douglas-Rachford for regularization of classical functionals by graph total variation
A Julia package that solves Linearly Constrained Separable Optimization Problems using ADMM.
Modeling language and tools for constrained, structured optimization problems
Proximal optimization in pure python
New Matrix Factorization Algorithms based on Bregman Proximal Gradient: BPG-MF, CoCaIn BPG-MF, BPG-MF-WB
Fast Inertial Algorithm for Phase Retrieval
CoCaIn BPG escapes Spurious Stationary Points
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