https://github.com/pylat/adaptive-proximal-algorithms

A Julia package for adaptive proximal gradient and primal-dual algorithms
https://github.com/pylat/adaptive-proximal-algorithms

adaptive-learning-rate convex-optimization linesearch-free-methods machine-learning optimization primal-dual-algorithms proximal-gradient-method

Last synced: about 1 month ago
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A Julia package for adaptive proximal gradient and primal-dual algorithms

• Host: GitHub
• URL: https://github.com/pylat/adaptive-proximal-algorithms
• Owner: pylat
• Created: 2023-01-09T11:47:54.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-01-18T06:16:05.000Z (11 months ago)
• Last Synced: 2024-01-29T11:47:30.664Z (11 months ago)
• Topics: adaptive-learning-rate, convex-optimization, linesearch-free-methods, machine-learning, optimization, primal-dual-algorithms, proximal-gradient-method
• Language: Julia
• Homepage:
• Size: 883 KB
• Stars: 8
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Adaptive Proximal Algorithms

This repository contains Julia code for the paper

> Latafat, Themelis, Stella, Patrinos, *Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient*, [arXiv:2301.04431](https://arxiv.org/abs/2301.04431) (2023).

Algorithms are implemented [here](./src/AdaProx.jl).

## Running experiments

Run the following from the repository root:

```sh

# set up environment

julia --project=./experiments -e 'using Pkg; Pkg.develop(path="."); Pkg.instantiate()'

# download datasets for experiments

julia --project=./experiments experiments/download_datasets.jl

```

Then run the scripts from the subfolders.

For example, run the lasso experiments as follows:

```sh

julia --project=./experiments experiments/lasso/runme.jl

```

This will generate plots in the same subfolder.