{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5BVHYCFIKU7VZ3B62WLRHFQAAW","short_pith_number":"pith:5BVHYCFI","schema_version":"1.0","canonical_sha256":"e86a7c08a8553f5cec3ed59713960005b72046d2ef5d309f251129794a152fb6","source":{"kind":"arxiv","id":"1809.02920","version":1},"attestation_state":"computed","paper":{"title":"Communication-Efficient Distributed Strongly Convex Stochastic Optimization: Non-Asymptotic Rates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Anit Kumar Sahu, Dragana Bajovic, Dusan Jakovetic, Soummya Kar","submitted_at":"2018-09-09T04:44:22Z","abstract_excerpt":"We examine fundamental tradeoffs in iterative distributed zeroth and first order stochastic optimization in multi-agent networks in terms of \\emph{communication cost} (number of per-node transmissions) and \\emph{computational cost}, measured by the number of per-node noisy function (respectively, gradient) evaluations with zeroth order (respectively, first order) methods. Specifically, we develop novel distributed stochastic optimization methods for zeroth and first order strongly convex optimization by utilizing a probabilistic inter-agent communication protocol that increasingly sparsifies c"},"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":"1809.02920","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-09-09T04:44:22Z","cross_cats_sorted":[],"title_canon_sha256":"9d6aafcacc39650408c5ddb63c95c4c3321f9c6f4835e2f16a05126c02afbc0a","abstract_canon_sha256":"14f9446543d618d9e1500199fecb193ecd7ba1fad1210644757660111a04ae0a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:10.626082Z","signature_b64":"tKUeLRR8AGMQQ0Pvy28PbUkFUsLR6CuVZinRhLhjKaRHMU//yCBHMlS48Y8JG6oRvzhNiW/gjLgU5GtOvvvnDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e86a7c08a8553f5cec3ed59713960005b72046d2ef5d309f251129794a152fb6","last_reissued_at":"2026-05-18T00:06:10.625337Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:10.625337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Communication-Efficient Distributed Strongly Convex Stochastic Optimization: Non-Asymptotic Rates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Anit Kumar Sahu, Dragana Bajovic, Dusan Jakovetic, Soummya Kar","submitted_at":"2018-09-09T04:44:22Z","abstract_excerpt":"We examine fundamental tradeoffs in iterative distributed zeroth and first order stochastic optimization in multi-agent networks in terms of \\emph{communication cost} (number of per-node transmissions) and \\emph{computational cost}, measured by the number of per-node noisy function (respectively, gradient) evaluations with zeroth order (respectively, first order) methods. Specifically, we develop novel distributed stochastic optimization methods for zeroth and first order strongly convex optimization by utilizing a probabilistic inter-agent communication protocol that increasingly sparsifies c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02920","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1809.02920","created_at":"2026-05-18T00:06:10.625451+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.02920v1","created_at":"2026-05-18T00:06:10.625451+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02920","created_at":"2026-05-18T00:06:10.625451+00:00"},{"alias_kind":"pith_short_12","alias_value":"5BVHYCFIKU7V","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5BVHYCFIKU7VZ3B6","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5BVHYCFI","created_at":"2026-05-18T12:32:08.215937+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2409.19567","citing_title":"Variance-Reduced Gradient Estimator for Nonconvex Zeroth-Order Distributed Optimization","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW","json":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW.json","graph_json":"https://pith.science/api/pith-number/5BVHYCFIKU7VZ3B62WLRHFQAAW/graph.json","events_json":"https://pith.science/api/pith-number/5BVHYCFIKU7VZ3B62WLRHFQAAW/events.json","paper":"https://pith.science/paper/5BVHYCFI"},"agent_actions":{"view_html":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW","download_json":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW.json","view_paper":"https://pith.science/paper/5BVHYCFI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.02920&json=true","fetch_graph":"https://pith.science/api/pith-number/5BVHYCFIKU7VZ3B62WLRHFQAAW/graph.json","fetch_events":"https://pith.science/api/pith-number/5BVHYCFIKU7VZ3B62WLRHFQAAW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW/action/storage_attestation","attest_author":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW/action/author_attestation","sign_citation":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW/action/citation_signature","submit_replication":"https://pith.science/pith/5BVHYCFIKU7VZ3B62WLRHFQAAW/action/replication_record"}},"created_at":"2026-05-18T00:06:10.625451+00:00","updated_at":"2026-05-18T00:06:10.625451+00:00"}