{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:4NZDS6MMAYAB47JKDD2JJGP7RJ","short_pith_number":"pith:4NZDS6MM","schema_version":"1.0","canonical_sha256":"e37239798c06001e7d2a18f49499ff8a671b3f9d8ebf888b169c52b7d09f012d","source":{"kind":"arxiv","id":"1710.08994","version":1},"attestation_state":"computed","paper":{"title":"Variable Partitioning for Distributed Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"math.OC","authors_text":"Ilbin Lee, Nicoleta Serban, Yuchen Zheng","submitted_at":"2017-10-24T21:15:44Z","abstract_excerpt":"This paper is about how to partition decision variables while decomposing a large-scale optimization problem for the best performance of distributed solution methods. Solving a large-scale optimization problem sequen- tially can be computationally challenging. One classic approach is to decompose the problem into smaller sub-problems and solve them in a distributed fashion. However, there is little discussion in the literature on which variables should be grouped together to form the sub-problems, especially when the optimization formulation involves complex constraints. We focus on one of the"},"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":"1710.08994","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-10-24T21:15:44Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"853f239d23e12a707556b972305f8e313c8d5f33e1faa9cd3819922531a2dfaa","abstract_canon_sha256":"04a9b80886b2597352089e834910948210243f955ce916d1d42c99729b09234e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:00.623951Z","signature_b64":"gRXD9hNb3jj2vhUiPKwTRdkzfUm0AMxkcVLxEe/Z9MMqdU15D4ifHteDLHdvCzNQS3weFZ+UdalurUFCEogPDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e37239798c06001e7d2a18f49499ff8a671b3f9d8ebf888b169c52b7d09f012d","last_reissued_at":"2026-05-18T00:32:00.623439Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:00.623439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Variable Partitioning for Distributed Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"math.OC","authors_text":"Ilbin Lee, Nicoleta Serban, Yuchen Zheng","submitted_at":"2017-10-24T21:15:44Z","abstract_excerpt":"This paper is about how to partition decision variables while decomposing a large-scale optimization problem for the best performance of distributed solution methods. Solving a large-scale optimization problem sequen- tially can be computationally challenging. One classic approach is to decompose the problem into smaller sub-problems and solve them in a distributed fashion. However, there is little discussion in the literature on which variables should be grouped together to form the sub-problems, especially when the optimization formulation involves complex constraints. We focus on one of the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08994","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":"1710.08994","created_at":"2026-05-18T00:32:00.623511+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.08994v1","created_at":"2026-05-18T00:32:00.623511+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08994","created_at":"2026-05-18T00:32:00.623511+00:00"},{"alias_kind":"pith_short_12","alias_value":"4NZDS6MMAYAB","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"4NZDS6MMAYAB47JK","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"4NZDS6MM","created_at":"2026-05-18T12:31:00.734936+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/4NZDS6MMAYAB47JKDD2JJGP7RJ","json":"https://pith.science/pith/4NZDS6MMAYAB47JKDD2JJGP7RJ.json","graph_json":"https://pith.science/api/pith-number/4NZDS6MMAYAB47JKDD2JJGP7RJ/graph.json","events_json":"https://pith.science/api/pith-number/4NZDS6MMAYAB47JKDD2JJGP7RJ/events.json","paper":"https://pith.science/paper/4NZDS6MM"},"agent_actions":{"view_html":"https://pith.science/pith/4NZDS6MMAYAB47JKDD2JJGP7RJ","download_json":"https://pith.science/pith/4NZDS6MMAYAB47JKDD2JJGP7RJ.json","view_paper":"https://pith.science/paper/4NZDS6MM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.08994&json=true","fetch_graph":"https://pith.science/api/pith-number/4NZDS6MMAYAB47JKDD2JJGP7RJ/graph.json","fetch_events":"https://pith.science/api/pith-number/4NZDS6MMAYAB47JKDD2JJGP7RJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4NZDS6MMAYAB47JKDD2JJGP7RJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4NZDS6MMAYAB47JKDD2JJGP7RJ/action/storage_attestation","attest_author":"https://pith.science/pith/4NZDS6MMAYAB47JKDD2JJGP7RJ/action/author_attestation","sign_citation":"https://pith.science/pith/4NZDS6MMAYAB47JKDD2JJGP7RJ/action/citation_signature","submit_replication":"https://pith.science/pith/4NZDS6MMAYAB47JKDD2JJGP7RJ/action/replication_record"}},"created_at":"2026-05-18T00:32:00.623511+00:00","updated_at":"2026-05-18T00:32:00.623511+00:00"}