{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:O2E7MJQNMDVOKUKTAG7JWNMEAB","short_pith_number":"pith:O2E7MJQN","schema_version":"1.0","canonical_sha256":"7689f6260d60eae5515301be9b3584004d5c105da4b012316d667d1aad732644","source":{"kind":"arxiv","id":"2608.14336","version":1},"attestation_state":"computed","paper":{"title":"A Shrinkage Path Heuristic for Wasserstein Distributionally Robust Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Lingjun Meng, Ryan Cory-Wright, Wolfram Wiesemann","submitted_at":"2026-08-14T14:23:03Z","abstract_excerpt":"Wasserstein distributionally robust optimization (DRO) is a versatile and widely adopted framework for decision-making under uncertainty, yet its standard deterministic reformulations generally contain non-convex inner subproblems that are challenging to solve. To address this issue, we propose a shrinkage path heuristic that reduces the solution of a DRO problem to a one-dimensional search over the line segment connecting the (typically benign) sample average approximation (SAA) and the (more demanding but practically solvable) classical robust optimization solution. We derive a priori subopt"},"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":"2608.14336","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-08-14T14:23:03Z","cross_cats_sorted":[],"title_canon_sha256":"d6657741e647e84aee89ee0691ec8dc8e0eb4531fff389e0ba509ff38d992ca4","abstract_canon_sha256":"8d4e1478a1a19f3646099a06d3684e167b2ca8d3c3a6aaece23775cc632b7530"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-17T01:13:23.450501Z","signature_b64":"22OmuPHlFvpmNjVD0iv1I5ASZcAqeTRKqKjLfmD0pGiVqPXnE5O+Qyx++XqgYvSc7cdhJsNJ26NLtiMV9BejDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7689f6260d60eae5515301be9b3584004d5c105da4b012316d667d1aad732644","last_reissued_at":"2026-08-17T01:13:23.448192Z","signature_status":"signed_v1","first_computed_at":"2026-08-17T01:13:23.448192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Shrinkage Path Heuristic for Wasserstein Distributionally Robust Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Lingjun Meng, Ryan Cory-Wright, Wolfram Wiesemann","submitted_at":"2026-08-14T14:23:03Z","abstract_excerpt":"Wasserstein distributionally robust optimization (DRO) is a versatile and widely adopted framework for decision-making under uncertainty, yet its standard deterministic reformulations generally contain non-convex inner subproblems that are challenging to solve. To address this issue, we propose a shrinkage path heuristic that reduces the solution of a DRO problem to a one-dimensional search over the line segment connecting the (typically benign) sample average approximation (SAA) and the (more demanding but practically solvable) classical robust optimization solution. We derive a priori subopt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.14336","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/2608.14336/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":"2608.14336","created_at":"2026-08-17T01:13:23.448909+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.14336v1","created_at":"2026-08-17T01:13:23.448909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.14336","created_at":"2026-08-17T01:13:23.448909+00:00"},{"alias_kind":"pith_short_12","alias_value":"O2E7MJQNMDVO","created_at":"2026-08-17T01:13:23.448909+00:00"},{"alias_kind":"pith_short_16","alias_value":"O2E7MJQNMDVOKUKT","created_at":"2026-08-17T01:13:23.448909+00:00"},{"alias_kind":"pith_short_8","alias_value":"O2E7MJQN","created_at":"2026-08-17T01:13:23.448909+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/O2E7MJQNMDVOKUKTAG7JWNMEAB","json":"https://pith.science/pith/O2E7MJQNMDVOKUKTAG7JWNMEAB.json","graph_json":"https://pith.science/api/pith-number/O2E7MJQNMDVOKUKTAG7JWNMEAB/graph.json","events_json":"https://pith.science/api/pith-number/O2E7MJQNMDVOKUKTAG7JWNMEAB/events.json","paper":"https://pith.science/paper/O2E7MJQN"},"agent_actions":{"view_html":"https://pith.science/pith/O2E7MJQNMDVOKUKTAG7JWNMEAB","download_json":"https://pith.science/pith/O2E7MJQNMDVOKUKTAG7JWNMEAB.json","view_paper":"https://pith.science/paper/O2E7MJQN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.14336&json=true","fetch_graph":"https://pith.science/api/pith-number/O2E7MJQNMDVOKUKTAG7JWNMEAB/graph.json","fetch_events":"https://pith.science/api/pith-number/O2E7MJQNMDVOKUKTAG7JWNMEAB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O2E7MJQNMDVOKUKTAG7JWNMEAB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O2E7MJQNMDVOKUKTAG7JWNMEAB/action/storage_attestation","attest_author":"https://pith.science/pith/O2E7MJQNMDVOKUKTAG7JWNMEAB/action/author_attestation","sign_citation":"https://pith.science/pith/O2E7MJQNMDVOKUKTAG7JWNMEAB/action/citation_signature","submit_replication":"https://pith.science/pith/O2E7MJQNMDVOKUKTAG7JWNMEAB/action/replication_record"}},"created_at":"2026-08-17T01:13:23.448909+00:00","updated_at":"2026-08-17T01:13:23.448909+00:00"}