{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:2IAFZKJ3P2HPWGQ7HZD56DYW4H","short_pith_number":"pith:2IAFZKJ3","schema_version":"1.0","canonical_sha256":"d2005ca93b7e8efb1a1f3e47df0f16e1e07804270b8f9fae086c9b79f8506e19","source":{"kind":"arxiv","id":"1502.03508","version":2},"attestation_state":"computed","paper":{"title":"Adding vs. Averaging in Distributed Primal-Dual Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chenxin Ma, Martin Jaggi, Martin Tak\\'a\\v{c}, Michael I. Jordan, Peter Richt\\'arik, Virginia Smith","submitted_at":"2015-02-12T01:51:08Z","abstract_excerpt":"Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accurately aggregating partial work from different machines. In this paper, we present a novel generalization of the recent communication-efficient primal-dual framework (CoCoA) for distributed optimization. Our framework, CoCoA+, allows for additive combination of local updates to the global parameters at each iteration, whereas previous schemes with convergence guarantees only allow conservative averaging. We give stro"},"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":"1502.03508","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-12T01:51:08Z","cross_cats_sorted":[],"title_canon_sha256":"038951159c07b0248989302bc723e2d4d2f289ff4dbdadf31036a93ac124f05d","abstract_canon_sha256":"5362244313d23ab90c38d345159a4251c12738fe639f8debc347e23d926ebb35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:23.189856Z","signature_b64":"kKZIjfNzFkoCZDrDMMHoEhQZedd8WGol9w3RS4NNhqAigTFJolVjY2hcT7ondm1dnt6An8gvfn2wEJajwMciAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2005ca93b7e8efb1a1f3e47df0f16e1e07804270b8f9fae086c9b79f8506e19","last_reissued_at":"2026-05-18T01:37:23.189157Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:23.189157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adding vs. Averaging in Distributed Primal-Dual Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chenxin Ma, Martin Jaggi, Martin Tak\\'a\\v{c}, Michael I. Jordan, Peter Richt\\'arik, Virginia Smith","submitted_at":"2015-02-12T01:51:08Z","abstract_excerpt":"Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accurately aggregating partial work from different machines. In this paper, we present a novel generalization of the recent communication-efficient primal-dual framework (CoCoA) for distributed optimization. Our framework, CoCoA+, allows for additive combination of local updates to the global parameters at each iteration, whereas previous schemes with convergence guarantees only allow conservative averaging. We give stro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.03508","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1502.03508","created_at":"2026-05-18T01:37:23.189255+00:00"},{"alias_kind":"arxiv_version","alias_value":"1502.03508v2","created_at":"2026-05-18T01:37:23.189255+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.03508","created_at":"2026-05-18T01:37:23.189255+00:00"},{"alias_kind":"pith_short_12","alias_value":"2IAFZKJ3P2HP","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_16","alias_value":"2IAFZKJ3P2HPWGQ7","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_8","alias_value":"2IAFZKJ3","created_at":"2026-05-18T12:28:59.999130+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/2IAFZKJ3P2HPWGQ7HZD56DYW4H","json":"https://pith.science/pith/2IAFZKJ3P2HPWGQ7HZD56DYW4H.json","graph_json":"https://pith.science/api/pith-number/2IAFZKJ3P2HPWGQ7HZD56DYW4H/graph.json","events_json":"https://pith.science/api/pith-number/2IAFZKJ3P2HPWGQ7HZD56DYW4H/events.json","paper":"https://pith.science/paper/2IAFZKJ3"},"agent_actions":{"view_html":"https://pith.science/pith/2IAFZKJ3P2HPWGQ7HZD56DYW4H","download_json":"https://pith.science/pith/2IAFZKJ3P2HPWGQ7HZD56DYW4H.json","view_paper":"https://pith.science/paper/2IAFZKJ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1502.03508&json=true","fetch_graph":"https://pith.science/api/pith-number/2IAFZKJ3P2HPWGQ7HZD56DYW4H/graph.json","fetch_events":"https://pith.science/api/pith-number/2IAFZKJ3P2HPWGQ7HZD56DYW4H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2IAFZKJ3P2HPWGQ7HZD56DYW4H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2IAFZKJ3P2HPWGQ7HZD56DYW4H/action/storage_attestation","attest_author":"https://pith.science/pith/2IAFZKJ3P2HPWGQ7HZD56DYW4H/action/author_attestation","sign_citation":"https://pith.science/pith/2IAFZKJ3P2HPWGQ7HZD56DYW4H/action/citation_signature","submit_replication":"https://pith.science/pith/2IAFZKJ3P2HPWGQ7HZD56DYW4H/action/replication_record"}},"created_at":"2026-05-18T01:37:23.189255+00:00","updated_at":"2026-05-18T01:37:23.189255+00:00"}