{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NBIFPDMVVHEN6BYRPDAFAKQKDZ","short_pith_number":"pith:NBIFPDMV","schema_version":"1.0","canonical_sha256":"6850578d95a9c8df071178c0502a0a1e7abdab012f70174191f258eb2b9675aa","source":{"kind":"arxiv","id":"2508.02299","version":1},"attestation_state":"computed","paper":{"title":"ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Nata\\v{s}a Kreji\\'c, Nata\\v{s}a Krklec Jerinki\\'c, Nemanja Vu\\v{c}i\\'cevi\\'c, Tijana Ostoji\\'c","submitted_at":"2025-08-04T11:10:28Z","abstract_excerpt":"We propose a novel algorithm for solving non-convex, nonlinear equality-constrained finite-sum optimization problems. The proposed algorithm incorporates an additional sampling strategy for sample size update into the well-known framework of quadratic penalty methods. Thus, depending on the problem at hand, the resulting method may exhibit a sample size strategy ranging from a mini-batch on one end, to increasing sample size that achieves the full sample eventually, on the other end of the spectrum. A non-monotone line search is used for the step size update, while the penalty parameter is als"},"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":"2508.02299","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2025-08-04T11:10:28Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"c86734cffc5e10096501d15ae0cae96335f701d902d131d57e521802f6f27c1d","abstract_canon_sha256":"73fc0190c899a1b6b2ce88a3eede75507520e0137996cddd203b5bb01a6df29f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:11.841668Z","signature_b64":"mKWcr9OJNEyEZRDVtkT/A5LwWTL4nYILRtwbEBKmPoDv6nYSe1BYvf9agjklHIEwlGYkoWCAjWcjs5wrLkrZBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6850578d95a9c8df071178c0502a0a1e7abdab012f70174191f258eb2b9675aa","last_reissued_at":"2026-07-05T11:48:11.841158Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:11.841158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"math.OC","authors_text":"Nata\\v{s}a Kreji\\'c, Nata\\v{s}a Krklec Jerinki\\'c, Nemanja Vu\\v{c}i\\'cevi\\'c, Tijana Ostoji\\'c","submitted_at":"2025-08-04T11:10:28Z","abstract_excerpt":"We propose a novel algorithm for solving non-convex, nonlinear equality-constrained finite-sum optimization problems. The proposed algorithm incorporates an additional sampling strategy for sample size update into the well-known framework of quadratic penalty methods. Thus, depending on the problem at hand, the resulting method may exhibit a sample size strategy ranging from a mini-batch on one end, to increasing sample size that achieves the full sample eventually, on the other end of the spectrum. A non-monotone line search is used for the step size update, while the penalty parameter is als"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.02299","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/2508.02299/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":"2508.02299","created_at":"2026-07-05T11:48:11.841225+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.02299v1","created_at":"2026-07-05T11:48:11.841225+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.02299","created_at":"2026-07-05T11:48:11.841225+00:00"},{"alias_kind":"pith_short_12","alias_value":"NBIFPDMVVHEN","created_at":"2026-07-05T11:48:11.841225+00:00"},{"alias_kind":"pith_short_16","alias_value":"NBIFPDMVVHEN6BYR","created_at":"2026-07-05T11:48:11.841225+00:00"},{"alias_kind":"pith_short_8","alias_value":"NBIFPDMV","created_at":"2026-07-05T11:48:11.841225+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16263","citing_title":"A Projected Stochastic Gradient Method for Finite-Sum Problems with Linear Equality Constraints","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ","json":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ.json","graph_json":"https://pith.science/api/pith-number/NBIFPDMVVHEN6BYRPDAFAKQKDZ/graph.json","events_json":"https://pith.science/api/pith-number/NBIFPDMVVHEN6BYRPDAFAKQKDZ/events.json","paper":"https://pith.science/paper/NBIFPDMV"},"agent_actions":{"view_html":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ","download_json":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ.json","view_paper":"https://pith.science/paper/NBIFPDMV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.02299&json=true","fetch_graph":"https://pith.science/api/pith-number/NBIFPDMVVHEN6BYRPDAFAKQKDZ/graph.json","fetch_events":"https://pith.science/api/pith-number/NBIFPDMVVHEN6BYRPDAFAKQKDZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ/action/storage_attestation","attest_author":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ/action/author_attestation","sign_citation":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ/action/citation_signature","submit_replication":"https://pith.science/pith/NBIFPDMVVHEN6BYRPDAFAKQKDZ/action/replication_record"}},"created_at":"2026-07-05T11:48:11.841225+00:00","updated_at":"2026-07-05T11:48:11.841225+00:00"}