{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:F2SUVR77UMJBQ7CMECUDE6KHOZ","short_pith_number":"pith:F2SUVR77","schema_version":"1.0","canonical_sha256":"2ea54ac7ffa312187c4c20a83279477641d94ad9a658b641cd5ae17ca02e503a","source":{"kind":"arxiv","id":"2508.01100","version":1},"attestation_state":"computed","paper":{"title":"Benders Decomposition using Graph Modeling and Multi-Parametric Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"David L. Cole, Parth Brahmbhatt, Styliani Avraamidou, Victor M. Zavala","submitted_at":"2025-08-01T22:31:29Z","abstract_excerpt":"Benders decomposition is a widely used method for solving large optimization problems, but its performance is often hindered by the repeated solution of subproblems. We propose a flexible and modular algorithmic framework for accelerating Benders decomposition by embedding multi-parametric programming (mp) surrogates for optimization subproblems. Our approach leverages the OptiGraph abstraction in Plasmo$.$jl to model and decompose graph-structured problems. By solving the subproblems associated with the graph nodes once using mp, we can extract explicit piecewise affine mappings for primal an"},"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":"2508.01100","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-08-01T22:31:29Z","cross_cats_sorted":[],"title_canon_sha256":"02b152b7db6c6555be395e4050335f6fd08f809ee2adfd70bc4537345bcb92cf","abstract_canon_sha256":"02b65ccce607adb30d536a5b2cc25dd3f09c0b0bc753b4afafaf106443cfc99b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:07.074885Z","signature_b64":"x7Dq/NMDbS4jZd2mRAbG0MAURZ2fR4UPQgd6lVGldAiR3ncWbG9MlwojAKdbr/lsciORk2rtvOPTWg+6u7i1CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ea54ac7ffa312187c4c20a83279477641d94ad9a658b641cd5ae17ca02e503a","last_reissued_at":"2026-07-05T11:48:07.074160Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:07.074160Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benders Decomposition using Graph Modeling and Multi-Parametric Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"David L. Cole, Parth Brahmbhatt, Styliani Avraamidou, Victor M. Zavala","submitted_at":"2025-08-01T22:31:29Z","abstract_excerpt":"Benders decomposition is a widely used method for solving large optimization problems, but its performance is often hindered by the repeated solution of subproblems. We propose a flexible and modular algorithmic framework for accelerating Benders decomposition by embedding multi-parametric programming (mp) surrogates for optimization subproblems. Our approach leverages the OptiGraph abstraction in Plasmo$.$jl to model and decompose graph-structured problems. By solving the subproblems associated with the graph nodes once using mp, we can extract explicit piecewise affine mappings for primal an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.01100","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/2508.01100/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":"2508.01100","created_at":"2026-07-05T11:48:07.074257+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.01100v1","created_at":"2026-07-05T11:48:07.074257+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.01100","created_at":"2026-07-05T11:48:07.074257+00:00"},{"alias_kind":"pith_short_12","alias_value":"F2SUVR77UMJB","created_at":"2026-07-05T11:48:07.074257+00:00"},{"alias_kind":"pith_short_16","alias_value":"F2SUVR77UMJBQ7CM","created_at":"2026-07-05T11:48:07.074257+00:00"},{"alias_kind":"pith_short_8","alias_value":"F2SUVR77","created_at":"2026-07-05T11:48:07.074257+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.15850","citing_title":"Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ","json":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ.json","graph_json":"https://pith.science/api/pith-number/F2SUVR77UMJBQ7CMECUDE6KHOZ/graph.json","events_json":"https://pith.science/api/pith-number/F2SUVR77UMJBQ7CMECUDE6KHOZ/events.json","paper":"https://pith.science/paper/F2SUVR77"},"agent_actions":{"view_html":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ","download_json":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ.json","view_paper":"https://pith.science/paper/F2SUVR77","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.01100&json=true","fetch_graph":"https://pith.science/api/pith-number/F2SUVR77UMJBQ7CMECUDE6KHOZ/graph.json","fetch_events":"https://pith.science/api/pith-number/F2SUVR77UMJBQ7CMECUDE6KHOZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ/action/storage_attestation","attest_author":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ/action/author_attestation","sign_citation":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ/action/citation_signature","submit_replication":"https://pith.science/pith/F2SUVR77UMJBQ7CMECUDE6KHOZ/action/replication_record"}},"created_at":"2026-07-05T11:48:07.074257+00:00","updated_at":"2026-07-05T11:48:07.074257+00:00"}