{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OEHJO6IYHDN3J3BMJP4VK5A6TA","short_pith_number":"pith:OEHJO6IY","schema_version":"1.0","canonical_sha256":"710e97791838dbb4ec2c4bf955741e981e07b38d3c752805f0083900d526590d","source":{"kind":"arxiv","id":"2606.17285","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Proximal Methods for Weakly Convex Optimization with Unknown Parameter: Deterministic and Stochastic Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"math.OC","authors_text":"Miaolan Xie","submitted_at":"2026-06-15T20:49:56Z","abstract_excerpt":"Many nonsmooth, nonconvex objectives in learning and signal recovery are $\\rho$-weakly convex. We minimize such a function in deterministic and stochastic settings when the weak-convexity parameter $\\rho$ is unknown. The objective is not required to be globally Lipschitz continuous or smooth. We propose the Adaptive Prox-Guided Scheme (APS), a one-trial proximal algorithm that adapts the proximal parameter online and bidirectionally through a descent test, allowing it to exploit favorable local structure.\n  In the deterministic setting, APS obtains an $O(\\varepsilon^{-2})$ iteration complexity"},"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":"2606.17285","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-06-15T20:49:56Z","cross_cats_sorted":["cs.DS"],"title_canon_sha256":"377fe0cb008a34e5362657dd52ea7c1beb1c94feb13e64e23986e84bdaf44f49","abstract_canon_sha256":"e2fa47cbe04fa916c0b5577e1e4bab58a0d58b1fd746946b0c364a8fe297e36d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:08.146974Z","signature_b64":"EiHG3Ci7skIg94WDwhNTkF4spJOGs2DtnVc6LSBQCUKfg4rncVhD2mAVCkw36znPtQZsigZHbeaH0fgz3+h2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"710e97791838dbb4ec2c4bf955741e981e07b38d3c752805f0083900d526590d","last_reissued_at":"2026-06-19T16:10:08.146547Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:08.146547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Proximal Methods for Weakly Convex Optimization with Unknown Parameter: Deterministic and Stochastic Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"math.OC","authors_text":"Miaolan Xie","submitted_at":"2026-06-15T20:49:56Z","abstract_excerpt":"Many nonsmooth, nonconvex objectives in learning and signal recovery are $\\rho$-weakly convex. We minimize such a function in deterministic and stochastic settings when the weak-convexity parameter $\\rho$ is unknown. The objective is not required to be globally Lipschitz continuous or smooth. We propose the Adaptive Prox-Guided Scheme (APS), a one-trial proximal algorithm that adapts the proximal parameter online and bidirectionally through a descent test, allowing it to exploit favorable local structure.\n  In the deterministic setting, APS obtains an $O(\\varepsilon^{-2})$ iteration complexity"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.17285","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/2606.17285/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":"2606.17285","created_at":"2026-06-19T16:10:08.146604+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.17285v1","created_at":"2026-06-19T16:10:08.146604+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.17285","created_at":"2026-06-19T16:10:08.146604+00:00"},{"alias_kind":"pith_short_12","alias_value":"OEHJO6IYHDN3","created_at":"2026-06-19T16:10:08.146604+00:00"},{"alias_kind":"pith_short_16","alias_value":"OEHJO6IYHDN3J3BM","created_at":"2026-06-19T16:10:08.146604+00:00"},{"alias_kind":"pith_short_8","alias_value":"OEHJO6IY","created_at":"2026-06-19T16:10:08.146604+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA","json":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA.json","graph_json":"https://pith.science/api/pith-number/OEHJO6IYHDN3J3BMJP4VK5A6TA/graph.json","events_json":"https://pith.science/api/pith-number/OEHJO6IYHDN3J3BMJP4VK5A6TA/events.json","paper":"https://pith.science/paper/OEHJO6IY"},"agent_actions":{"view_html":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA","download_json":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA.json","view_paper":"https://pith.science/paper/OEHJO6IY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.17285&json=true","fetch_graph":"https://pith.science/api/pith-number/OEHJO6IYHDN3J3BMJP4VK5A6TA/graph.json","fetch_events":"https://pith.science/api/pith-number/OEHJO6IYHDN3J3BMJP4VK5A6TA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA/action/storage_attestation","attest_author":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA/action/author_attestation","sign_citation":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA/action/citation_signature","submit_replication":"https://pith.science/pith/OEHJO6IYHDN3J3BMJP4VK5A6TA/action/replication_record"}},"created_at":"2026-06-19T16:10:08.146604+00:00","updated_at":"2026-06-19T16:10:08.146604+00:00"}