{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GMX5F5RYF6CFFFLOVTPPQU7IKM","short_pith_number":"pith:GMX5F5RY","schema_version":"1.0","canonical_sha256":"332fd2f6382f8452956eacdef853e85319bac2ffd6b6deb053a542de519a67a5","source":{"kind":"arxiv","id":"1908.01086","version":3},"attestation_state":"computed","paper":{"title":"A Stochastic Primal-Dual Method for Optimization with Conditional Value at Risk Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Avinash N. Madavan, Subhonmesh Bose","submitted_at":"2019-08-02T22:54:41Z","abstract_excerpt":"We study a first-order primal-dual subgradient method to optimize risk-constrained risk-penalized optimization problems, where risk is modeled via the popular conditional value at risk (CVaR) measure. The algorithm processes independent and identically distributed samples from the underlying uncertainty in an online fashion, and produces an $\\eta/\\sqrt{K}$-approximately feasible and $\\eta/\\sqrt{K}$-approximately optimal point within $K$ iterations with constant step-size, where $\\eta$ increases with tunable risk-parameters of CVaR. We find optimized step sizes using our bounds and precisely ch"},"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":"1908.01086","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-02T22:54:41Z","cross_cats_sorted":[],"title_canon_sha256":"45b437a283272183eff516c75345dd7f434190d056aaa22a8371d7a637c22d19","abstract_canon_sha256":"2375c70ff5056dfc17363e87818f9ac39571f555307355e8c96fc740c522cb47"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:10:48.836034Z","signature_b64":"AadgiFGX/T2fK/D+kFFk0oZyaXa/Yt3bXrTMMhzzYmXV7ig83YN+IFw3XN5kXB7wfG/OWTdBALU9D79cev3ICQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"332fd2f6382f8452956eacdef853e85319bac2ffd6b6deb053a542de519a67a5","last_reissued_at":"2026-07-05T03:10:48.835543Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:10:48.835543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Stochastic Primal-Dual Method for Optimization with Conditional Value at Risk Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Avinash N. Madavan, Subhonmesh Bose","submitted_at":"2019-08-02T22:54:41Z","abstract_excerpt":"We study a first-order primal-dual subgradient method to optimize risk-constrained risk-penalized optimization problems, where risk is modeled via the popular conditional value at risk (CVaR) measure. The algorithm processes independent and identically distributed samples from the underlying uncertainty in an online fashion, and produces an $\\eta/\\sqrt{K}$-approximately feasible and $\\eta/\\sqrt{K}$-approximately optimal point within $K$ iterations with constant step-size, where $\\eta$ increases with tunable risk-parameters of CVaR. We find optimized step sizes using our bounds and precisely ch"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.01086","kind":"arxiv","version":3},"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/1908.01086/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":"1908.01086","created_at":"2026-07-05T03:10:48.835603+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.01086v3","created_at":"2026-07-05T03:10:48.835603+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.01086","created_at":"2026-07-05T03:10:48.835603+00:00"},{"alias_kind":"pith_short_12","alias_value":"GMX5F5RYF6CF","created_at":"2026-07-05T03:10:48.835603+00:00"},{"alias_kind":"pith_short_16","alias_value":"GMX5F5RYF6CFFFLO","created_at":"2026-07-05T03:10:48.835603+00:00"},{"alias_kind":"pith_short_8","alias_value":"GMX5F5RY","created_at":"2026-07-05T03:10:48.835603+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.00510","citing_title":"Adaptive Kernel Learning in Heterogeneous Networks","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM","json":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM.json","graph_json":"https://pith.science/api/pith-number/GMX5F5RYF6CFFFLOVTPPQU7IKM/graph.json","events_json":"https://pith.science/api/pith-number/GMX5F5RYF6CFFFLOVTPPQU7IKM/events.json","paper":"https://pith.science/paper/GMX5F5RY"},"agent_actions":{"view_html":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM","download_json":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM.json","view_paper":"https://pith.science/paper/GMX5F5RY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.01086&json=true","fetch_graph":"https://pith.science/api/pith-number/GMX5F5RYF6CFFFLOVTPPQU7IKM/graph.json","fetch_events":"https://pith.science/api/pith-number/GMX5F5RYF6CFFFLOVTPPQU7IKM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM/action/storage_attestation","attest_author":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM/action/author_attestation","sign_citation":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM/action/citation_signature","submit_replication":"https://pith.science/pith/GMX5F5RYF6CFFFLOVTPPQU7IKM/action/replication_record"}},"created_at":"2026-07-05T03:10:48.835603+00:00","updated_at":"2026-07-05T03:10:48.835603+00:00"}