{"paper":{"title":"Online Submodular Welfare Maximization: Greedy Beats 1/2 in Random Order","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Morteza Zadimoghaddam, Nitish Korula, Vahab Mirrokni","submitted_at":"2017-12-14T20:47:22Z","abstract_excerpt":"In the Submodular Welfare Maximization (SWM) problem, the input consists of a set of $n$ items, each of which must be allocated to one of $m$ agents. Each agent $\\ell$ has a valuation function $v_\\ell$, where $v_\\ell(S)$ denotes the welfare obtained by this agent if she receives the set of items $S$. The functions $v_\\ell$ are all submodular; as is standard, we assume that they are monotone and $v_\\ell(\\emptyset) = 0$. The goal is to partition the items into $m$ disjoint subsets $S_1, S_2, \\ldots S_m$ in order to maximize the social welfare, defined as $\\sum_{\\ell = 1}^m v_\\ell(S_\\ell)$.\n  In "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.05450","kind":"arxiv","version":1},"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"}