{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OTXMAXDAN4KQ75ZQPUO3N2ELA2","short_pith_number":"pith:OTXMAXDA","schema_version":"1.0","canonical_sha256":"74eec05c606f150ff7307d1db6e88b06b549c930e42747644466e1c06b5961b0","source":{"kind":"arxiv","id":"1909.06724","version":1},"attestation_state":"computed","paper":{"title":"Communication-Censored Linearized ADMM for Decentralized Consensus Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"math.OC","authors_text":"Qing Ling, Weiyu Li, Yaohua Liu, Zhi Tian","submitted_at":"2019-09-15T03:39:46Z","abstract_excerpt":"In this paper, we propose a communication- and computation-efficient algorithm to solve a convex consensus optimization problem defined over a decentralized network. A remarkable existing algorithm to solve this problem is the alternating direction method of multipliers (ADMM), in which at every iteration every node updates its local variable through combining neighboring variables and solving an optimization subproblem. The proposed algorithm, called as COmmunication-censored Linearized ADMM (COLA), leverages a linearization technique to reduce the iteration-wise computation cost of ADMM and "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1909.06724","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-09-15T03:39:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c8c8dd9c6d5153c63be30075f106ebad3f58ff7a05fd828bd6227656e6e41b72","abstract_canon_sha256":"d2301850624f11f728ecc5fc825a9c5713fc1e2bd8969078740b736f7eabee96"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:53:51.223917Z","signature_b64":"1RJziumopM/JavfFSJFvYLmfOgWUdBBeUoppyoYuSg8R4PjuyE5FxWVWDCwdOXP1DdhFlb0WoMuLeshG72POBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74eec05c606f150ff7307d1db6e88b06b549c930e42747644466e1c06b5961b0","last_reissued_at":"2026-07-05T00:53:51.223518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:53:51.223518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Communication-Censored Linearized ADMM for Decentralized Consensus Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"math.OC","authors_text":"Qing Ling, Weiyu Li, Yaohua Liu, Zhi Tian","submitted_at":"2019-09-15T03:39:46Z","abstract_excerpt":"In this paper, we propose a communication- and computation-efficient algorithm to solve a convex consensus optimization problem defined over a decentralized network. A remarkable existing algorithm to solve this problem is the alternating direction method of multipliers (ADMM), in which at every iteration every node updates its local variable through combining neighboring variables and solving an optimization subproblem. The proposed algorithm, called as COmmunication-censored Linearized ADMM (COLA), leverages a linearization technique to reduce the iteration-wise computation cost of ADMM and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.06724","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.06724/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1909.06724","created_at":"2026-07-05T00:53:51.223580+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.06724v1","created_at":"2026-07-05T00:53:51.223580+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.06724","created_at":"2026-07-05T00:53:51.223580+00:00"},{"alias_kind":"pith_short_12","alias_value":"OTXMAXDAN4KQ","created_at":"2026-07-05T00:53:51.223580+00:00"},{"alias_kind":"pith_short_16","alias_value":"OTXMAXDAN4KQ75ZQ","created_at":"2026-07-05T00:53:51.223580+00:00"},{"alias_kind":"pith_short_8","alias_value":"OTXMAXDA","created_at":"2026-07-05T00:53:51.223580+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2","json":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2.json","graph_json":"https://pith.science/api/pith-number/OTXMAXDAN4KQ75ZQPUO3N2ELA2/graph.json","events_json":"https://pith.science/api/pith-number/OTXMAXDAN4KQ75ZQPUO3N2ELA2/events.json","paper":"https://pith.science/paper/OTXMAXDA"},"agent_actions":{"view_html":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2","download_json":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2.json","view_paper":"https://pith.science/paper/OTXMAXDA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.06724&json=true","fetch_graph":"https://pith.science/api/pith-number/OTXMAXDAN4KQ75ZQPUO3N2ELA2/graph.json","fetch_events":"https://pith.science/api/pith-number/OTXMAXDAN4KQ75ZQPUO3N2ELA2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2/action/storage_attestation","attest_author":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2/action/author_attestation","sign_citation":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2/action/citation_signature","submit_replication":"https://pith.science/pith/OTXMAXDAN4KQ75ZQPUO3N2ELA2/action/replication_record"}},"created_at":"2026-07-05T00:53:51.223580+00:00","updated_at":"2026-07-05T00:53:51.223580+00:00"}