{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:I3A4MEASA46DPQU5UYMK4JVHZG","short_pith_number":"pith:I3A4MEAS","schema_version":"1.0","canonical_sha256":"46c1c61012073c37c29da618ae26a7c98d7336f74872afc08c067f0fd39fc457","source":{"kind":"arxiv","id":"2208.00799","version":2},"attestation_state":"computed","paper":{"title":"An interior proximal gradient method for nonconvex optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alberto De Marchi, Andreas Themelis","submitted_at":"2022-08-01T12:32:14Z","abstract_excerpt":"We consider structured minimization problems subject to smooth inequality constraints and present a flexible algorithm that combines interior point (IP) and proximal gradient schemes. While traditional IP methods cannot cope with nonsmooth objective functions and proximal algorithms cannot handle complicated constraints, their combined usage is shown to successfully compensate the respective shortcomings. We provide a theoretical characterization of the algorithm and its asymptotic properties, deriving convergence results for fully nonconvex problems, thus bridging the gap with previous works "},"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":"2208.00799","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2022-08-01T12:32:14Z","cross_cats_sorted":[],"title_canon_sha256":"2c0ec5a0b0acabae18a899df11da8f78008a309b6dbb98d98a368245f39e2a13","abstract_canon_sha256":"9064fae715ab87ee8fd60686d4777db47f8d9a26a690a4f5ebacba075c4153dc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:57.926562Z","signature_b64":"nNy3kP+T/YcICmZF0FWY1n+sR7hW3Q4wbc0EYPJN6HgBya1dTnwE6yuWtO0CBbJRyN0gUBoZH/CzVVUcq1FPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46c1c61012073c37c29da618ae26a7c98d7336f74872afc08c067f0fd39fc457","last_reissued_at":"2026-07-05T08:41:57.926086Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:57.926086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An interior proximal gradient method for nonconvex optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alberto De Marchi, Andreas Themelis","submitted_at":"2022-08-01T12:32:14Z","abstract_excerpt":"We consider structured minimization problems subject to smooth inequality constraints and present a flexible algorithm that combines interior point (IP) and proximal gradient schemes. While traditional IP methods cannot cope with nonsmooth objective functions and proximal algorithms cannot handle complicated constraints, their combined usage is shown to successfully compensate the respective shortcomings. We provide a theoretical characterization of the algorithm and its asymptotic properties, deriving convergence results for fully nonconvex problems, thus bridging the gap with previous works "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00799","kind":"arxiv","version":2},"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/2208.00799/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":"2208.00799","created_at":"2026-07-05T08:41:57.926145+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.00799v2","created_at":"2026-07-05T08:41:57.926145+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00799","created_at":"2026-07-05T08:41:57.926145+00:00"},{"alias_kind":"pith_short_12","alias_value":"I3A4MEASA46D","created_at":"2026-07-05T08:41:57.926145+00:00"},{"alias_kind":"pith_short_16","alias_value":"I3A4MEASA46DPQU5","created_at":"2026-07-05T08:41:57.926145+00:00"},{"alias_kind":"pith_short_8","alias_value":"I3A4MEAS","created_at":"2026-07-05T08:41:57.926145+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.08558","citing_title":"Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG","json":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG.json","graph_json":"https://pith.science/api/pith-number/I3A4MEASA46DPQU5UYMK4JVHZG/graph.json","events_json":"https://pith.science/api/pith-number/I3A4MEASA46DPQU5UYMK4JVHZG/events.json","paper":"https://pith.science/paper/I3A4MEAS"},"agent_actions":{"view_html":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG","download_json":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG.json","view_paper":"https://pith.science/paper/I3A4MEAS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.00799&json=true","fetch_graph":"https://pith.science/api/pith-number/I3A4MEASA46DPQU5UYMK4JVHZG/graph.json","fetch_events":"https://pith.science/api/pith-number/I3A4MEASA46DPQU5UYMK4JVHZG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG/action/storage_attestation","attest_author":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG/action/author_attestation","sign_citation":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG/action/citation_signature","submit_replication":"https://pith.science/pith/I3A4MEASA46DPQU5UYMK4JVHZG/action/replication_record"}},"created_at":"2026-07-05T08:41:57.926145+00:00","updated_at":"2026-07-05T08:41:57.926145+00:00"}