{"paper":{"title":"Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Chaobing Song, Shaobo Cui, Shu-Tao Xia, Yong Jiang","submitted_at":"2017-02-25T07:06:09Z","abstract_excerpt":"In this paper we study the well-known greedy coordinate descent (GCD) algorithm to solve $\\ell_1$-regularized problems and improve GCD by the two popular strategies: Nesterov's acceleration and stochastic optimization. Firstly, we propose a new rule for greedy selection based on an $\\ell_1$-norm square approximation which is nontrivial to solve but convex, then an efficient algorithm called \"SOft ThreshOlding PrOjection (SOTOPO)\" is proposed to exactly solve the $\\ell_1$-regularized $\\ell_1$-norm square approximation problem, which is induced by the new rule. Based on the new rule and the SOTO"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.07842","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}