{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:H2HQ43JZP3PVHPYTMEW7D2GN2G","short_pith_number":"pith:H2HQ43JZ","schema_version":"1.0","canonical_sha256":"3e8f0e6d397edf53bf13612df1e8cdd1996a9b34e4bcf5e46733b1000529f5fc","source":{"kind":"arxiv","id":"2601.20076","version":2},"attestation_state":"computed","paper":{"title":"Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Abhishek Chakraborty, Angelia Nedi\\'c","submitted_at":"2026-01-27T21:40:10Z","abstract_excerpt":"We consider minimizing an objective function subject to constraints defined by the intersection of lower-level sets of convex functions. We study two cases: (i) strongly convex and Lipschitz-smooth objective function and (ii) convex but possibly nonsmooth objective function. To deal with the constraints that are not easy to project on, we use a randomized feasibility algorithm with Polyak steps and a random number of sampled constraints per iteration, while taking (sub)gradient steps to minimize the objective function. For case (i), we prove linear convergence in expectation of the objective f"},"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":"2601.20076","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-01-27T21:40:10Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d849573d09fe39f52abf2b238161de1a63271ef78311a60b05cfaedd96aad616","abstract_canon_sha256":"2e881caa896960ae129a972b6cdf8c56451ebd5d3ce0e7b07a0b0813b093a45b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:02:30.546843Z","signature_b64":"OmngIyUkQ0VuFYkAGJGrzQLDM6jZTj8pPN6gHqTos9lPVKYIfxpZA177HrBClYR/5xHgcBizdrgpt3PwAIIZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e8f0e6d397edf53bf13612df1e8cdd1996a9b34e4bcf5e46733b1000529f5fc","last_reissued_at":"2026-06-01T01:02:30.545841Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:02:30.545841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Abhishek Chakraborty, Angelia Nedi\\'c","submitted_at":"2026-01-27T21:40:10Z","abstract_excerpt":"We consider minimizing an objective function subject to constraints defined by the intersection of lower-level sets of convex functions. We study two cases: (i) strongly convex and Lipschitz-smooth objective function and (ii) convex but possibly nonsmooth objective function. To deal with the constraints that are not easy to project on, we use a randomized feasibility algorithm with Polyak steps and a random number of sampled constraints per iteration, while taking (sub)gradient steps to minimize the objective function. For case (i), we prove linear convergence in expectation of the objective f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.20076","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/2601.20076/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":"2601.20076","created_at":"2026-06-01T01:02:30.545989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.20076v2","created_at":"2026-06-01T01:02:30.545989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.20076","created_at":"2026-06-01T01:02:30.545989+00:00"},{"alias_kind":"pith_short_12","alias_value":"H2HQ43JZP3PV","created_at":"2026-06-01T01:02:30.545989+00:00"},{"alias_kind":"pith_short_16","alias_value":"H2HQ43JZP3PVHPYT","created_at":"2026-06-01T01:02:30.545989+00:00"},{"alias_kind":"pith_short_8","alias_value":"H2HQ43JZ","created_at":"2026-06-01T01:02:30.545989+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.18999","citing_title":"Distance-Aware Muon: Adaptive Step Scaling for Normalized Optimization","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G","json":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G.json","graph_json":"https://pith.science/api/pith-number/H2HQ43JZP3PVHPYTMEW7D2GN2G/graph.json","events_json":"https://pith.science/api/pith-number/H2HQ43JZP3PVHPYTMEW7D2GN2G/events.json","paper":"https://pith.science/paper/H2HQ43JZ"},"agent_actions":{"view_html":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G","download_json":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G.json","view_paper":"https://pith.science/paper/H2HQ43JZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.20076&json=true","fetch_graph":"https://pith.science/api/pith-number/H2HQ43JZP3PVHPYTMEW7D2GN2G/graph.json","fetch_events":"https://pith.science/api/pith-number/H2HQ43JZP3PVHPYTMEW7D2GN2G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G/action/storage_attestation","attest_author":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G/action/author_attestation","sign_citation":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G/action/citation_signature","submit_replication":"https://pith.science/pith/H2HQ43JZP3PVHPYTMEW7D2GN2G/action/replication_record"}},"created_at":"2026-06-01T01:02:30.545989+00:00","updated_at":"2026-06-01T01:02:30.545989+00:00"}