{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:KD6RE4MPGZDP4OL777335OSTLF","short_pith_number":"pith:KD6RE4MP","schema_version":"1.0","canonical_sha256":"50fd12718f3646fe397ffff7beba53594a3664519b8d7e3f7b584b6ab4e1e54d","source":{"kind":"arxiv","id":"1607.03566","version":2},"attestation_state":"computed","paper":{"title":"Polyhedral approximation in mixed-integer convex optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Emre Yamangil, Juan Pablo Vielma, Miles Lubin, Russell Bent","submitted_at":"2016-07-13T02:01:06Z","abstract_excerpt":"Generalizing both mixed-integer linear optimization and convex optimization, mixed-integer convex optimization possesses broad modeling power but has seen relatively few advances in general-purpose solvers in recent years. In this paper, we intend to provide a broadly accessible introduction to our recent work in developing algorithms and software for this problem class. Our approach is based on constructing polyhedral outer approximations of the convex constraints, resulting in a global solution by solving a finite number of mixed-integer linear and continuous convex subproblems. The key adva"},"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":"1607.03566","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-07-13T02:01:06Z","cross_cats_sorted":[],"title_canon_sha256":"dafa0dfc885e614fe86cc4843f707fff2d854139c56f98110ddd0c521ca2b2d6","abstract_canon_sha256":"4f6fe243ce55b2e01b582caaf57dc77cd081b8a992bdc4792e1caac08675fbc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:09.451182Z","signature_b64":"2lPvnHWk487bKugE1qL14vW+KdHg8pYTNj3UNNvB6B2chRdo9CYGLkltZqqZrBs0TcfxkUgnMREB6hP7UBk1AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50fd12718f3646fe397ffff7beba53594a3664519b8d7e3f7b584b6ab4e1e54d","last_reissued_at":"2026-05-18T00:35:09.450645Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:09.450645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Polyhedral approximation in mixed-integer convex optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Emre Yamangil, Juan Pablo Vielma, Miles Lubin, Russell Bent","submitted_at":"2016-07-13T02:01:06Z","abstract_excerpt":"Generalizing both mixed-integer linear optimization and convex optimization, mixed-integer convex optimization possesses broad modeling power but has seen relatively few advances in general-purpose solvers in recent years. In this paper, we intend to provide a broadly accessible introduction to our recent work in developing algorithms and software for this problem class. Our approach is based on constructing polyhedral outer approximations of the convex constraints, resulting in a global solution by solving a finite number of mixed-integer linear and continuous convex subproblems. The key adva"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.03566","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":"1607.03566","created_at":"2026-05-18T00:35:09.450730+00:00"},{"alias_kind":"arxiv_version","alias_value":"1607.03566v2","created_at":"2026-05-18T00:35:09.450730+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.03566","created_at":"2026-05-18T00:35:09.450730+00:00"},{"alias_kind":"pith_short_12","alias_value":"KD6RE4MPGZDP","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_16","alias_value":"KD6RE4MPGZDP4OL7","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_8","alias_value":"KD6RE4MP","created_at":"2026-05-18T12:30:25.849896+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/KD6RE4MPGZDP4OL777335OSTLF","json":"https://pith.science/pith/KD6RE4MPGZDP4OL777335OSTLF.json","graph_json":"https://pith.science/api/pith-number/KD6RE4MPGZDP4OL777335OSTLF/graph.json","events_json":"https://pith.science/api/pith-number/KD6RE4MPGZDP4OL777335OSTLF/events.json","paper":"https://pith.science/paper/KD6RE4MP"},"agent_actions":{"view_html":"https://pith.science/pith/KD6RE4MPGZDP4OL777335OSTLF","download_json":"https://pith.science/pith/KD6RE4MPGZDP4OL777335OSTLF.json","view_paper":"https://pith.science/paper/KD6RE4MP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1607.03566&json=true","fetch_graph":"https://pith.science/api/pith-number/KD6RE4MPGZDP4OL777335OSTLF/graph.json","fetch_events":"https://pith.science/api/pith-number/KD6RE4MPGZDP4OL777335OSTLF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KD6RE4MPGZDP4OL777335OSTLF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KD6RE4MPGZDP4OL777335OSTLF/action/storage_attestation","attest_author":"https://pith.science/pith/KD6RE4MPGZDP4OL777335OSTLF/action/author_attestation","sign_citation":"https://pith.science/pith/KD6RE4MPGZDP4OL777335OSTLF/action/citation_signature","submit_replication":"https://pith.science/pith/KD6RE4MPGZDP4OL777335OSTLF/action/replication_record"}},"created_at":"2026-05-18T00:35:09.450730+00:00","updated_at":"2026-05-18T00:35:09.450730+00:00"}