{"paper":{"title":"Online Convex Optimization with Time-Varying Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Hao Yu, Michael J. Neely","submitted_at":"2017-02-15T21:27:06Z","abstract_excerpt":"This paper considers online convex optimization with time-varying constraint functions. Specifically, we have a sequence of convex objective functions $\\{f_t(x)\\}_{t=0}^{\\infty}$ and convex constraint functions $\\{g_{t,i}(x)\\}_{t=0}^{\\infty}$ for $i \\in \\{1, ..., k\\}$. The functions are gradually revealed over time. For a given $\\epsilon>0$, the goal is to choose points $x_t$ every step $t$, without knowing the $f_t$ and $g_{t,i}$ functions on that step, to achieve a time average at most $\\epsilon$ worse than the best fixed-decision that could be chosen with hindsight, subject to the time aver"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.04783","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"}