subgradient

A reproduction of Learning Efficient Convolutional Networks through Network Slimming

Implementation of Convex Optimization algorithms

Subgradient methods for Multicommodity Network Design

Solving quadratic programming problem using subgradient optimizer

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

This repository contains the Python code and solutions for machine learning, including supervised and unsupervised learning, regularization, cross-validation, PCA, and support vector machines (SVM).

Minimax NMF

MCPy is a python library for McCormick relaxations with sub-gradients. This is quite useful for prototyping and testing new convex relaxation and global optimization algorithms.

Numerical optimization from scratch: Newton family, Heavy Ball & Nesterov, LASSO subgradients, PGD vs Frank-Wolfe, CG, and ILP with CVXPY

R package for SGD inference

Automatic differentiation for nonsmooth functions — abs, max, min, floor done right with Clarke subgradients

Dual-Based Procedure and Subgradient method implementations

Convex Optimization Algorithms

Non-linear topology identification using Deep Learning. Sparsity (lasso) is enforced in the sensor connections. The non-convex and non-differentiable function is solved using sub-gradient descent algorithm.

Some notes on convex optimization

In this work, we consider learning sparse models in large scale setting, where the number of samples and the feature dimension can grow as large as millions or billions. Two immediate issues occur under such challenging scenarios: (i) com- putational cost; (ii) memory overhead.

Optimization includes a class of methods to find global or local optima for discrete or continuous objectives; from evolutionary-based algorithms to swarm-based ones.