{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HDUQPJWQYRJJUJFYFBQQZABUG4","short_pith_number":"pith:HDUQPJWQ","schema_version":"1.0","canonical_sha256":"38e907a6d0c4529a24b828610c803437333598b14836271122bf5c4e1cb9fbe3","source":{"kind":"arxiv","id":"2311.08827","version":2},"attestation_state":"computed","paper":{"title":"A Deep Reinforcement Learning Approach to Efficient Distributed Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Daokuan Zhu, Jie Lu, Tianqi Xu","submitted_at":"2023-11-15T10:02:42Z","abstract_excerpt":"In distributed optimization, the practical problem-solving performance is essentially sensitive to algorithm selection, parameter setting, problem type and data pattern. Thus, it is often laborious to acquire a highly efficient method for a given specific problem. In this paper, we propose a learning-based method to achieve efficient distributed optimization over networked systems. Specifically, a deep reinforcement learning (DRL) framework is developed for adaptive configuration within a parameterized unifying algorithmic form, which incorporates an abundance of decentralized first-order 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":"2311.08827","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2023-11-15T10:02:42Z","cross_cats_sorted":[],"title_canon_sha256":"7a395d7100be5bf2a6160a374e10e1f015c2af840d7aeec1976b5babbceb55f5","abstract_canon_sha256":"e87695bf2194942c965948e6c2802ecfeb5f579e5f450a594b5eda6a21f75264"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:29:54.673514Z","signature_b64":"UAGTxR4l3lCXJE5qWeknFvRZ7qEAt57BQBCeEyStmZxL3Ri1k6cDMUagvw+BU9Yh/VjpPgoBa6SvxJtGRwUNCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38e907a6d0c4529a24b828610c803437333598b14836271122bf5c4e1cb9fbe3","last_reissued_at":"2026-07-05T07:29:54.673028Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:29:54.673028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deep Reinforcement Learning Approach to Efficient Distributed Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Daokuan Zhu, Jie Lu, Tianqi Xu","submitted_at":"2023-11-15T10:02:42Z","abstract_excerpt":"In distributed optimization, the practical problem-solving performance is essentially sensitive to algorithm selection, parameter setting, problem type and data pattern. Thus, it is often laborious to acquire a highly efficient method for a given specific problem. In this paper, we propose a learning-based method to achieve efficient distributed optimization over networked systems. Specifically, a deep reinforcement learning (DRL) framework is developed for adaptive configuration within a parameterized unifying algorithmic form, which incorporates an abundance of decentralized first-order and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.08827","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.08827/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":"2311.08827","created_at":"2026-07-05T07:29:54.673087+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.08827v2","created_at":"2026-07-05T07:29:54.673087+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.08827","created_at":"2026-07-05T07:29:54.673087+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDUQPJWQYRJJ","created_at":"2026-07-05T07:29:54.673087+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDUQPJWQYRJJUJFY","created_at":"2026-07-05T07:29:54.673087+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDUQPJWQ","created_at":"2026-07-05T07:29:54.673087+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.12156","citing_title":"Deep Distributed Optimization for Large-Scale Quadratic Programming","ref_index":60,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4","json":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4.json","graph_json":"https://pith.science/api/pith-number/HDUQPJWQYRJJUJFYFBQQZABUG4/graph.json","events_json":"https://pith.science/api/pith-number/HDUQPJWQYRJJUJFYFBQQZABUG4/events.json","paper":"https://pith.science/paper/HDUQPJWQ"},"agent_actions":{"view_html":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4","download_json":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4.json","view_paper":"https://pith.science/paper/HDUQPJWQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.08827&json=true","fetch_graph":"https://pith.science/api/pith-number/HDUQPJWQYRJJUJFYFBQQZABUG4/graph.json","fetch_events":"https://pith.science/api/pith-number/HDUQPJWQYRJJUJFYFBQQZABUG4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4/action/storage_attestation","attest_author":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4/action/author_attestation","sign_citation":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4/action/citation_signature","submit_replication":"https://pith.science/pith/HDUQPJWQYRJJUJFYFBQQZABUG4/action/replication_record"}},"created_at":"2026-07-05T07:29:54.673087+00:00","updated_at":"2026-07-05T07:29:54.673087+00:00"}