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The analysis is carried out for\ngeneral convex-concave saddle point problems and problems that are either\npartially smooth / strongly convex or fully smooth / strongly convex. We\nperform the analysis for arbitrary samplings of dual variables, and obtain\nknown deterministic results as a special case. Several variants of our\nstochastic method significantly outperform the deterministic variant on a\nvariety of imaging tasks.","url_abs":"http://arxiv.org/abs/1706.04957v2","url_pdf":"http://arxiv.org/pdf/1706.04957v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stochastic-primal-dual-hybrid-gradient","repo_url":"https://github.com/odlgroup/odl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MPL-2.0"}},{"paper_slug":"stochastic-primal-dual-hybrid-gradient","repo_url":"https://github.com/mehrhardt/spdhg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1706.04957","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.04957"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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