Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression. CVPR2020.

PyTorch implementation of Proximal Gradient Algorithms a la Parikh and Boyd (2014). Useful for Auto-Sizing (Murray and Chiang 2015, Murray et al. 2019).

Implementation and analysis of convex optimization algorithms

Pyoneer is a Python 3 package for the continuous recovery of non-bandlimited periodic signals with finite rates of innovation (e.g. Dirac streams) from generalised measurements.

An Image Reconstructor that applies fast proximal gradient method (FISTA) to the wavelet transform of an image using L1 and Total Variation (TV) regularizations

Use Ridge Regression and Lasso Regression in prostate cancer data

Proximal-point methods for machine learning

A package for fitting regularized models from scikit-learn via proximal gradient descent

An efficient GPU-compatible library built on PyTorch, offering a wide range of proximal operators and constraints for optimization and machine learning tasks.

Python Implementations of proximal GD, Accelerated proximal GD and ADMM for solving lasso regression

Blind Image Deconvolution and Frank-Wolfe's algorithm to deblur a license plate for Crime Scene Investigation (CSI)

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.

Implementation and brief comparison of different First Order and different Proximal gradient methods, comparison of their convergence rates

Unified implementation of MGProx.

Pomodoro: Progressive Decomposition Methods with Acceleration

Optimization methods for lasso penalized logistic regression.

[Optimization algorithms] Study of the Proximal Gradient Method, Stochastic Gradient Descent method and Adam optimizer

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