{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ZKSWBLSL4QKXU2DAWX2O2UTMMD","short_pith_number":"pith:ZKSWBLSL","canonical_record":{"source":{"id":"2510.16468","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-18T12:26:28Z","cross_cats_sorted":[],"title_canon_sha256":"324819c9c870dcb17eba9ffc5dff056b33cbf1e04dac2eaeb1520025e6c1a4b4","abstract_canon_sha256":"68e4402496ee81ed2d3e59ae8b673502998b2970a0b4e6a88a6fefaa6c53a889"},"schema_version":"1.0"},"canonical_sha256":"caa560ae4be4157a6860b5f4ed526c60fa3dc69e9f8a60d2e22e82b19fb8ab6e","source":{"kind":"arxiv","id":"2510.16468","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.16468","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"arxiv_version","alias_value":"2510.16468v4","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.16468","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"pith_short_12","alias_value":"ZKSWBLSL4QKX","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"pith_short_16","alias_value":"ZKSWBLSL4QKXU2DA","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"pith_short_8","alias_value":"ZKSWBLSL","created_at":"2026-05-21T01:05:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ZKSWBLSL4QKXU2DAWX2O2UTMMD","target":"record","payload":{"canonical_record":{"source":{"id":"2510.16468","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-18T12:26:28Z","cross_cats_sorted":[],"title_canon_sha256":"324819c9c870dcb17eba9ffc5dff056b33cbf1e04dac2eaeb1520025e6c1a4b4","abstract_canon_sha256":"68e4402496ee81ed2d3e59ae8b673502998b2970a0b4e6a88a6fefaa6c53a889"},"schema_version":"1.0"},"canonical_sha256":"caa560ae4be4157a6860b5f4ed526c60fa3dc69e9f8a60d2e22e82b19fb8ab6e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:05:11.072293Z","signature_b64":"W7qRNRWKOQapr4lLwYGEuUpMhY13OqKeRSg/3bSgAEtTIoPNS4cgswu0hUL+ZvjsXiCr72IONWCrZglxy/qRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"caa560ae4be4157a6860b5f4ed526c60fa3dc69e9f8a60d2e22e82b19fb8ab6e","last_reissued_at":"2026-05-21T01:05:11.071362Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:05:11.071362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.16468","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-21T01:05:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U2dYUpW6vF7OnaiqBSU2jFp6lKAUPnjWBglZdX+L8JBPgC4d5HD/QTSHrypIPx486Qs04LE00LMZSVqwqRkLDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T02:42:07.336489Z"},"content_sha256":"8b178c7d061df30d8a4b64e04a7d0f63b62c55ab6c45697f371fac66dad43d64","schema_version":"1.0","event_id":"sha256:8b178c7d061df30d8a4b64e04a7d0f63b62c55ab6c45697f371fac66dad43d64"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ZKSWBLSL4QKXU2DAWX2O2UTMMD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Frank-Wolfe Algorithms for (L0, L1)-smooth functions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A new Frank-Wolfe algorithm for (L0, L1)-smooth objectives achieves superior convergence rates.","cross_cats":[],"primary_cat":"math.OC","authors_text":"A.A. Vyguzov, F.S. Stonyakin","submitted_at":"2025-10-18T12:26:28Z","abstract_excerpt":"We propose a new version of the Frank-Wolfe method, called the (L0, L1)-Frank-Wolfe algorithm, developed for optimization problems with (L0, L1)-smooth objectives. We establish that this algorithm achieves superior theoretical convergence rates compared to the classical Frank-Wolfe method. In addition, we introduce a novel adaptive procedure, termed the Adaptive (L0, L1)-Frank-Wolfe algorithm, which dynamically adjusts the smoothness parameters to further improve performance and stability. Comprehensive numerical experiments confirm the theoretical results and demonstrate the clear practical a"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We establish that this algorithm achieves superior theoretical convergence rates compared to the classical Frank-Wolfe method.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The objective functions satisfy the (L0, L1)-smoothness condition that the new algorithm is designed to exploit for its improved rates (as stated in the abstract).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A new (L0, L1)-Frank-Wolfe algorithm and its adaptive version are proposed for (L0, L1)-smooth optimization, with claims of better theoretical convergence rates and practical advantages over standard Frank-Wolfe methods.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A new Frank-Wolfe algorithm for (L0, L1)-smooth objectives achieves superior convergence rates.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f69488c153b3381d1262d104e41cb24dd5f3c7a03104f40775dde4e564f9e371"},"source":{"id":"2510.16468","kind":"arxiv","version":4},"verdict":{"id":"cfd172e3-73aa-42bf-a8b3-266c147a7798","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T06:15:35.242813Z","strongest_claim":"We establish that this algorithm achieves superior theoretical convergence rates compared to the classical Frank-Wolfe method.","one_line_summary":"A new (L0, L1)-Frank-Wolfe algorithm and its adaptive version are proposed for (L0, L1)-smooth optimization, with claims of better theoretical convergence rates and practical advantages over standard Frank-Wolfe methods.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The objective functions satisfy the (L0, L1)-smoothness condition that the new algorithm is designed to exploit for its improved rates (as stated in the abstract).","pith_extraction_headline":"A new Frank-Wolfe algorithm for (L0, L1)-smooth objectives achieves superior convergence rates."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.16468/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":2,"snapshot_sha256":"f6aae87b4addc9041ad5739415c2be3cd33ab177968fcf0a87322f24fdeb2c43"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"cfd172e3-73aa-42bf-a8b3-266c147a7798"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-21T01:05:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d3rLpdPEONcpIV9KInZC7UE04pxQ8qpmLNfEvYXCi+PhUUVoXgc1bUAQHfVHTbiC5kW9f7Oj23Nbtk6WkxaTCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T02:42:07.336947Z"},"content_sha256":"33cc13478899255635fa1b03db7229fb613910295db2fab56f9e5ae107140f54","schema_version":"1.0","event_id":"sha256:33cc13478899255635fa1b03db7229fb613910295db2fab56f9e5ae107140f54"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZKSWBLSL4QKXU2DAWX2O2UTMMD/bundle.json","state_url":"https://pith.science/pith/ZKSWBLSL4QKXU2DAWX2O2UTMMD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZKSWBLSL4QKXU2DAWX2O2UTMMD/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":"2026-05-21T02:42:07Z","links":{"resolver":"https://pith.science/pith/ZKSWBLSL4QKXU2DAWX2O2UTMMD","bundle":"https://pith.science/pith/ZKSWBLSL4QKXU2DAWX2O2UTMMD/bundle.json","state":"https://pith.science/pith/ZKSWBLSL4QKXU2DAWX2O2UTMMD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZKSWBLSL4QKXU2DAWX2O2UTMMD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZKSWBLSL4QKXU2DAWX2O2UTMMD","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":"68e4402496ee81ed2d3e59ae8b673502998b2970a0b4e6a88a6fefaa6c53a889","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-18T12:26:28Z","title_canon_sha256":"324819c9c870dcb17eba9ffc5dff056b33cbf1e04dac2eaeb1520025e6c1a4b4"},"schema_version":"1.0","source":{"id":"2510.16468","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.16468","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"arxiv_version","alias_value":"2510.16468v4","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.16468","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"pith_short_12","alias_value":"ZKSWBLSL4QKX","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"pith_short_16","alias_value":"ZKSWBLSL4QKXU2DA","created_at":"2026-05-21T01:05:11Z"},{"alias_kind":"pith_short_8","alias_value":"ZKSWBLSL","created_at":"2026-05-21T01:05:11Z"}],"graph_snapshots":[{"event_id":"sha256:33cc13478899255635fa1b03db7229fb613910295db2fab56f9e5ae107140f54","target":"graph","created_at":"2026-05-21T01:05:11Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We establish that this algorithm achieves superior theoretical convergence rates compared to the classical Frank-Wolfe method."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The objective functions satisfy the (L0, L1)-smoothness condition that the new algorithm is designed to exploit for its improved rates (as stated in the abstract)."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A new (L0, L1)-Frank-Wolfe algorithm and its adaptive version are proposed for (L0, L1)-smooth optimization, with claims of better theoretical convergence rates and practical advantages over standard Frank-Wolfe methods."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A new Frank-Wolfe algorithm for (L0, L1)-smooth objectives achieves superior convergence rates."}],"snapshot_sha256":"f69488c153b3381d1262d104e41cb24dd5f3c7a03104f40775dde4e564f9e371"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"f6aae87b4addc9041ad5739415c2be3cd33ab177968fcf0a87322f24fdeb2c43"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2510.16468/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a new version of the Frank-Wolfe method, called the (L0, L1)-Frank-Wolfe algorithm, developed for optimization problems with (L0, L1)-smooth objectives. We establish that this algorithm achieves superior theoretical convergence rates compared to the classical Frank-Wolfe method. In addition, we introduce a novel adaptive procedure, termed the Adaptive (L0, L1)-Frank-Wolfe algorithm, which dynamically adjusts the smoothness parameters to further improve performance and stability. Comprehensive numerical experiments confirm the theoretical results and demonstrate the clear practical a","authors_text":"A.A. Vyguzov, F.S. Stonyakin","cross_cats":[],"headline":"A new Frank-Wolfe algorithm for (L0, L1)-smooth objectives achieves superior convergence rates.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-18T12:26:28Z","title":"Frank-Wolfe Algorithms for (L0, L1)-smooth functions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.16468","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-18T06:15:35.242813Z","id":"cfd172e3-73aa-42bf-a8b3-266c147a7798","model_set":{"reader":"grok-4.3"},"one_line_summary":"A new (L0, L1)-Frank-Wolfe algorithm and its adaptive version are proposed for (L0, L1)-smooth optimization, with claims of better theoretical convergence rates and practical advantages over standard Frank-Wolfe methods.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A new Frank-Wolfe algorithm for (L0, L1)-smooth objectives achieves superior convergence rates.","strongest_claim":"We establish that this algorithm achieves superior theoretical convergence rates compared to the classical Frank-Wolfe method.","weakest_assumption":"The objective functions satisfy the (L0, L1)-smoothness condition that the new algorithm is designed to exploit for its improved rates (as stated in the abstract)."}},"verdict_id":"cfd172e3-73aa-42bf-a8b3-266c147a7798"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8b178c7d061df30d8a4b64e04a7d0f63b62c55ab6c45697f371fac66dad43d64","target":"record","created_at":"2026-05-21T01:05:11Z","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":"68e4402496ee81ed2d3e59ae8b673502998b2970a0b4e6a88a6fefaa6c53a889","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-18T12:26:28Z","title_canon_sha256":"324819c9c870dcb17eba9ffc5dff056b33cbf1e04dac2eaeb1520025e6c1a4b4"},"schema_version":"1.0","source":{"id":"2510.16468","kind":"arxiv","version":4}},"canonical_sha256":"caa560ae4be4157a6860b5f4ed526c60fa3dc69e9f8a60d2e22e82b19fb8ab6e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"caa560ae4be4157a6860b5f4ed526c60fa3dc69e9f8a60d2e22e82b19fb8ab6e","first_computed_at":"2026-05-21T01:05:11.071362Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-21T01:05:11.071362Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W7qRNRWKOQapr4lLwYGEuUpMhY13OqKeRSg/3bSgAEtTIoPNS4cgswu0hUL+ZvjsXiCr72IONWCrZglxy/qRBQ==","signature_status":"signed_v1","signed_at":"2026-05-21T01:05:11.072293Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.16468","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b178c7d061df30d8a4b64e04a7d0f63b62c55ab6c45697f371fac66dad43d64","sha256:33cc13478899255635fa1b03db7229fb613910295db2fab56f9e5ae107140f54"],"state_sha256":"b0cfa744c4e5cf2a21843e6d246f841ed395cc4cf64edcdf008f660b0c94ad46"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"51HS14HicZdE4ge9uv5sf1I6EDJM0EAJ9Ob+M97c95uKSzTIrmBHgiPgD85gCfzYR90I+UF9BHu0q0LPkTWnDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T02:42:07.340718Z","bundle_sha256":"4df484c1e7dbb990b0e86a64588e49995850667ccd8c794c6cc16425717eaf95"}}