{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:QXR3XDEVEOJDTNUY6HYVALJQUL","short_pith_number":"pith:QXR3XDEV","canonical_record":{"source":{"id":"2401.16164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T13:50:56Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"46beaf4a08e95f20f834b930f47a7b23860a770494aa4d49bdda867953a29f1c","abstract_canon_sha256":"790e109150cd030455035e846b4cb77321229e8d3a4d51ea1f8093d763a317f4"},"schema_version":"1.0"},"canonical_sha256":"85e3bb8c95239239b698f1f1502d30a2c9a2639ab109e906190c3e8b30c5309d","source":{"kind":"arxiv","id":"2401.16164","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.16164","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"arxiv_version","alias_value":"2401.16164v1","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.16164","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"pith_short_12","alias_value":"QXR3XDEVEOJD","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"pith_short_16","alias_value":"QXR3XDEVEOJDTNUY","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"pith_short_8","alias_value":"QXR3XDEV","created_at":"2026-07-05T07:38:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:QXR3XDEVEOJDTNUY6HYVALJQUL","target":"record","payload":{"canonical_record":{"source":{"id":"2401.16164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T13:50:56Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"46beaf4a08e95f20f834b930f47a7b23860a770494aa4d49bdda867953a29f1c","abstract_canon_sha256":"790e109150cd030455035e846b4cb77321229e8d3a4d51ea1f8093d763a317f4"},"schema_version":"1.0"},"canonical_sha256":"85e3bb8c95239239b698f1f1502d30a2c9a2639ab109e906190c3e8b30c5309d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:45.038784Z","signature_b64":"HxTdF8lz5OXJ9NH9y5Cx2bdj3djaoAkmK+H6ryx+by9ZFgC2DH4ocJ0/5GOT8uaoAtXsPhdRBMxEX3OaHnwADw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85e3bb8c95239239b698f1f1502d30a2c9a2639ab109e906190c3e8b30c5309d","last_reissued_at":"2026-07-05T07:38:45.038380Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:45.038380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.16164","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:38:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YR0/uF/zLKS4vKOb9tXwgD/B2saIfAjzMdFx7F08crknf5tblo5ueMeCRC6bann+bvPWkxL19RQsMV/JD2NFDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T07:11:20.578698Z"},"content_sha256":"78a5181f612f0358d383fa2c89072e5923755f8a81b5dd32bccb65eeaa35ac7f","schema_version":"1.0","event_id":"sha256:78a5181f612f0358d383fa2c89072e5923755f8a81b5dd32bccb65eeaa35ac7f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:QXR3XDEVEOJDTNUY6HYVALJQUL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Chengming Yu, Jin Zhang, Shangzhi Zeng, Wei Yao","submitted_at":"2024-01-29T13:50:56Z","abstract_excerpt":"This paper presents a new approach and algorithm for solving a class of constrained Bi-Level Optimization (BLO) problems in which the lower-level problem involves constraints coupling both upper-level and lower-level variables. Such problems have recently gained significant attention due to their broad applicability in machine learning. However, conventional gradient-based methods unavoidably rely on computationally intensive calculations related to the Hessian matrix. To address this challenge, we begin by devising a smooth proximal Lagrangian value function to handle the constrained lower-le"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.16164","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/2401.16164/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:38:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CuZp2Pf5U5JQO+f7sDNRQf88Q/LcOuN2xbEpQvsBngS0vZItCQnVH4c2AOTtLqQUsJl99/xaOLOfDHSGCg2UAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T07:11:20.579326Z"},"content_sha256":"5cf792488c47cb669c84e11d683f79a73d1963566ea6ac3efa19cb28b3bebbaa","schema_version":"1.0","event_id":"sha256:5cf792488c47cb669c84e11d683f79a73d1963566ea6ac3efa19cb28b3bebbaa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QXR3XDEVEOJDTNUY6HYVALJQUL/bundle.json","state_url":"https://pith.science/pith/QXR3XDEVEOJDTNUY6HYVALJQUL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QXR3XDEVEOJDTNUY6HYVALJQUL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T07:11:20Z","links":{"resolver":"https://pith.science/pith/QXR3XDEVEOJDTNUY6HYVALJQUL","bundle":"https://pith.science/pith/QXR3XDEVEOJDTNUY6HYVALJQUL/bundle.json","state":"https://pith.science/pith/QXR3XDEVEOJDTNUY6HYVALJQUL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QXR3XDEVEOJDTNUY6HYVALJQUL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QXR3XDEVEOJDTNUY6HYVALJQUL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"790e109150cd030455035e846b4cb77321229e8d3a4d51ea1f8093d763a317f4","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T13:50:56Z","title_canon_sha256":"46beaf4a08e95f20f834b930f47a7b23860a770494aa4d49bdda867953a29f1c"},"schema_version":"1.0","source":{"id":"2401.16164","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.16164","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"arxiv_version","alias_value":"2401.16164v1","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.16164","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"pith_short_12","alias_value":"QXR3XDEVEOJD","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"pith_short_16","alias_value":"QXR3XDEVEOJDTNUY","created_at":"2026-07-05T07:38:45Z"},{"alias_kind":"pith_short_8","alias_value":"QXR3XDEV","created_at":"2026-07-05T07:38:45Z"}],"graph_snapshots":[{"event_id":"sha256:5cf792488c47cb669c84e11d683f79a73d1963566ea6ac3efa19cb28b3bebbaa","target":"graph","created_at":"2026-07-05T07:38:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2401.16164/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a new approach and algorithm for solving a class of constrained Bi-Level Optimization (BLO) problems in which the lower-level problem involves constraints coupling both upper-level and lower-level variables. Such problems have recently gained significant attention due to their broad applicability in machine learning. However, conventional gradient-based methods unavoidably rely on computationally intensive calculations related to the Hessian matrix. To address this challenge, we begin by devising a smooth proximal Lagrangian value function to handle the constrained lower-le","authors_text":"Chengming Yu, Jin Zhang, Shangzhi Zeng, Wei Yao","cross_cats":["math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T13:50:56Z","title":"Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.16164","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:78a5181f612f0358d383fa2c89072e5923755f8a81b5dd32bccb65eeaa35ac7f","target":"record","created_at":"2026-07-05T07:38:45Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"790e109150cd030455035e846b4cb77321229e8d3a4d51ea1f8093d763a317f4","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-29T13:50:56Z","title_canon_sha256":"46beaf4a08e95f20f834b930f47a7b23860a770494aa4d49bdda867953a29f1c"},"schema_version":"1.0","source":{"id":"2401.16164","kind":"arxiv","version":1}},"canonical_sha256":"85e3bb8c95239239b698f1f1502d30a2c9a2639ab109e906190c3e8b30c5309d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85e3bb8c95239239b698f1f1502d30a2c9a2639ab109e906190c3e8b30c5309d","first_computed_at":"2026-07-05T07:38:45.038380Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:38:45.038380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HxTdF8lz5OXJ9NH9y5Cx2bdj3djaoAkmK+H6ryx+by9ZFgC2DH4ocJ0/5GOT8uaoAtXsPhdRBMxEX3OaHnwADw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:38:45.038784Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.16164","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:78a5181f612f0358d383fa2c89072e5923755f8a81b5dd32bccb65eeaa35ac7f","sha256:5cf792488c47cb669c84e11d683f79a73d1963566ea6ac3efa19cb28b3bebbaa"],"state_sha256":"37c452fd845301658fa6224f7df64705af451881f838a6db3a8b2d65c95ad870"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/azEqVUuF4O8eTaeDlgDOVQ/XUkJO9ijiqCPc+G1QLCrGnhxt52TRkTRPu1b0778T8r5FnmqaTsF2gyOKlK9Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T07:11:20.583296Z","bundle_sha256":"c4711bcac2882a153c869a4d904d649b6c0b2cc0df6dd1fe3f9f72e113a4afad"}}