{"id":18968829,"url":"https://github.com/pylat/adaptive-proximal-algorithms","last_synced_at":"2026-04-05T12:30:20.286Z","repository":{"id":166879065,"uuid":"586855344","full_name":"pylat/adaptive-proximal-algorithms","owner":"pylat","description":"A Julia package for adaptive proximal gradient and primal-dual algorithms","archived":false,"fork":false,"pushed_at":"2024-01-18T06:16:05.000Z","size":904,"stargazers_count":10,"open_issues_count":1,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-14T09:17:12.396Z","etag":null,"topics":["adaptive-learning-rate","convex-optimization","linesearch-free-methods","machine-learning","optimization","primal-dual-algorithms","proximal-gradient-method"],"latest_commit_sha":null,"homepage":"","language":"Julia","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pylat.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-01-09T11:47:54.000Z","updated_at":"2025-01-09T17:53:51.000Z","dependencies_parsed_at":"2024-01-12T04:44:02.753Z","dependency_job_id":"b2dfc666-6367-4719-b446-8f518dd758e8","html_url":"https://github.com/pylat/adaptive-proximal-algorithms","commit_stats":{"total_commits":41,"total_committers":4,"mean_commits":10.25,"dds":0.4878048780487805,"last_synced_commit":"2eb4059c6dbf7a1574073f854b3b4bc9bc922587"},"previous_names":["pylat/adaptive-proximal-algorithms"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pylat%2Fadaptive-proximal-algorithms","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pylat%2Fadaptive-proximal-algorithms/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pylat%2Fadaptive-proximal-algorithms/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pylat%2Fadaptive-proximal-algorithms/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pylat","download_url":"https://codeload.github.com/pylat/adaptive-proximal-algorithms/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239965840,"owners_count":19726196,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["adaptive-learning-rate","convex-optimization","linesearch-free-methods","machine-learning","optimization","primal-dual-algorithms","proximal-gradient-method"],"created_at":"2024-11-08T14:48:35.550Z","updated_at":"2026-04-05T12:30:20.235Z","avatar_url":"https://github.com/pylat.png","language":"Julia","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Adaptive Proximal Algorithms\n\nThis repository contains Julia code for the paper\n\n\u003e 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).\n\nAlgorithms are implemented [here](./src/AdaProx.jl).\n\n## Running experiments\n\nRun the following from the repository root:\n\n```sh\n# set up environment\njulia --project=./experiments -e 'using Pkg; Pkg.develop(path=\".\"); Pkg.instantiate()'\n\n# download datasets for experiments\njulia --project=./experiments experiments/download_datasets.jl\n```\n\nThen run the scripts from the subfolders.\nFor example, run the lasso experiments as follows:\n\n```sh\njulia --project=./experiments experiments/lasso/runme.jl\n```\n\nThis will generate plots in the same subfolder.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpylat%2Fadaptive-proximal-algorithms","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpylat%2Fadaptive-proximal-algorithms","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpylat%2Fadaptive-proximal-algorithms/lists"}