{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CM7VWUSYLWJS5ZNZ64PDFSHCXR","short_pith_number":"pith:CM7VWUSY","schema_version":"1.0","canonical_sha256":"133f5b52585d932ee5b9f71e32c8e2bc6153a32213f606575cc5b41b2e5c81f3","source":{"kind":"arxiv","id":"2406.10406","version":1},"attestation_state":"computed","paper":{"title":"Methods of Nonconvex Optimization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"A. M. Gupal, V. I. Norkin, V. S. Mikhalevich","submitted_at":"2024-06-14T20:21:12Z","abstract_excerpt":"This book is devoted to finite-dimensional problems of non-convex non-smooth optimization and numerical methods for their solution. The problem of nonconvexity is studied in the book on two main models of nonconvex dependencies: these are the so-called generalized differentiable functions and locally Lipschitz functions. Non-smooth functions naturally arise in various applications. In addition, they often appear in the theory of extremal problems itself due to the operations of taking the maximum and minimum, decomposition techniques, exact non-smooth penalties, and duality. The considered mod"},"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":"2406.10406","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.OC","submitted_at":"2024-06-14T20:21:12Z","cross_cats_sorted":[],"title_canon_sha256":"401f70d09ff608799f8fe7544d69e6894745f44889e5f2371adfc9fecdc9a93f","abstract_canon_sha256":"4fb2fd304a75140b5f3df4baa7f96874024f1a369f3427ecf7d1efd8d1500a6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:16.290338Z","signature_b64":"ge6rInDDIUUJXnRpq7ZQzFfe/9J35iJ18JBIFMqWGpVDmiqOubIuCKn+5ByoXfRI5s5/PuRxkJEDCkLNgZZQCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"133f5b52585d932ee5b9f71e32c8e2bc6153a32213f606575cc5b41b2e5c81f3","last_reissued_at":"2026-07-05T08:32:16.289864Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:16.289864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Methods of Nonconvex Optimization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"A. M. Gupal, V. I. Norkin, V. S. Mikhalevich","submitted_at":"2024-06-14T20:21:12Z","abstract_excerpt":"This book is devoted to finite-dimensional problems of non-convex non-smooth optimization and numerical methods for their solution. The problem of nonconvexity is studied in the book on two main models of nonconvex dependencies: these are the so-called generalized differentiable functions and locally Lipschitz functions. Non-smooth functions naturally arise in various applications. In addition, they often appear in the theory of extremal problems itself due to the operations of taking the maximum and minimum, decomposition techniques, exact non-smooth penalties, and duality. The considered mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10406","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/2406.10406/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":"2406.10406","created_at":"2026-07-05T08:32:16.289912+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.10406v1","created_at":"2026-07-05T08:32:16.289912+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10406","created_at":"2026-07-05T08:32:16.289912+00:00"},{"alias_kind":"pith_short_12","alias_value":"CM7VWUSYLWJS","created_at":"2026-07-05T08:32:16.289912+00:00"},{"alias_kind":"pith_short_16","alias_value":"CM7VWUSYLWJS5ZNZ","created_at":"2026-07-05T08:32:16.289912+00:00"},{"alias_kind":"pith_short_8","alias_value":"CM7VWUSY","created_at":"2026-07-05T08:32:16.289912+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2408.16286","citing_title":"Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR","json":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR.json","graph_json":"https://pith.science/api/pith-number/CM7VWUSYLWJS5ZNZ64PDFSHCXR/graph.json","events_json":"https://pith.science/api/pith-number/CM7VWUSYLWJS5ZNZ64PDFSHCXR/events.json","paper":"https://pith.science/paper/CM7VWUSY"},"agent_actions":{"view_html":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR","download_json":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR.json","view_paper":"https://pith.science/paper/CM7VWUSY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.10406&json=true","fetch_graph":"https://pith.science/api/pith-number/CM7VWUSYLWJS5ZNZ64PDFSHCXR/graph.json","fetch_events":"https://pith.science/api/pith-number/CM7VWUSYLWJS5ZNZ64PDFSHCXR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR/action/storage_attestation","attest_author":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR/action/author_attestation","sign_citation":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR/action/citation_signature","submit_replication":"https://pith.science/pith/CM7VWUSYLWJS5ZNZ64PDFSHCXR/action/replication_record"}},"created_at":"2026-07-05T08:32:16.289912+00:00","updated_at":"2026-07-05T08:32:16.289912+00:00"}