{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:ZXT5FEKGKNHWWBPINFLMQKOQ44","short_pith_number":"pith:ZXT5FEKG","schema_version":"1.0","canonical_sha256":"cde7d29146534f6b05e86956c829d0e717db8664511c2b953c077422aef0e6ad","source":{"kind":"arxiv","id":"1607.06513","version":3},"attestation_state":"computed","paper":{"title":"Online First-Order Framework for Robust Convex Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Fatma Kilinc-Karzan, Nam Ho-Nguyen","submitted_at":"2016-07-21T21:50:39Z","abstract_excerpt":"Robust optimization (RO) has emerged as one of the leading paradigms to efficiently model parameter uncertainty. The recent connections between RO and problems in statistics and machine learning domains demand for solving RO problems in ever more larger scale. However, the traditional approaches for solving RO formulations based on building and solving robust counterparts or the iterative approaches utilizing nominal feasibility oracles can be prohibitively expensive and thus significantly hinder the scalability of RO paradigm. In this paper, we present a general and flexible iterative framewo"},"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":"1607.06513","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-07-21T21:50:39Z","cross_cats_sorted":[],"title_canon_sha256":"0f739f71f3e023ba155eb800ca4ff3bf9a4494708bcf6e1bd5595075693751bf","abstract_canon_sha256":"4be4987ac6a329f9389e9bbb90addbacac0d49cd10a5747552d59665b49e43e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:17.599067Z","signature_b64":"ekO0OjxbiweF9JUkf+ov2p8wYVck2AK4wpbHPITNA1tFWsXY5dlVz6W6TTBWCwHq43cCY5n+czHn96XQ2PtmDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cde7d29146534f6b05e86956c829d0e717db8664511c2b953c077422aef0e6ad","last_reissued_at":"2026-05-18T00:30:17.598344Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:17.598344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online First-Order Framework for Robust Convex Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Fatma Kilinc-Karzan, Nam Ho-Nguyen","submitted_at":"2016-07-21T21:50:39Z","abstract_excerpt":"Robust optimization (RO) has emerged as one of the leading paradigms to efficiently model parameter uncertainty. The recent connections between RO and problems in statistics and machine learning domains demand for solving RO problems in ever more larger scale. However, the traditional approaches for solving RO formulations based on building and solving robust counterparts or the iterative approaches utilizing nominal feasibility oracles can be prohibitively expensive and thus significantly hinder the scalability of RO paradigm. In this paper, we present a general and flexible iterative framewo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.06513","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":""},"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":"1607.06513","created_at":"2026-05-18T00:30:17.598441+00:00"},{"alias_kind":"arxiv_version","alias_value":"1607.06513v3","created_at":"2026-05-18T00:30:17.598441+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.06513","created_at":"2026-05-18T00:30:17.598441+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZXT5FEKGKNHW","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZXT5FEKGKNHWWBPI","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZXT5FEKG","created_at":"2026-05-18T12:30:55.937587+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/ZXT5FEKGKNHWWBPINFLMQKOQ44","json":"https://pith.science/pith/ZXT5FEKGKNHWWBPINFLMQKOQ44.json","graph_json":"https://pith.science/api/pith-number/ZXT5FEKGKNHWWBPINFLMQKOQ44/graph.json","events_json":"https://pith.science/api/pith-number/ZXT5FEKGKNHWWBPINFLMQKOQ44/events.json","paper":"https://pith.science/paper/ZXT5FEKG"},"agent_actions":{"view_html":"https://pith.science/pith/ZXT5FEKGKNHWWBPINFLMQKOQ44","download_json":"https://pith.science/pith/ZXT5FEKGKNHWWBPINFLMQKOQ44.json","view_paper":"https://pith.science/paper/ZXT5FEKG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1607.06513&json=true","fetch_graph":"https://pith.science/api/pith-number/ZXT5FEKGKNHWWBPINFLMQKOQ44/graph.json","fetch_events":"https://pith.science/api/pith-number/ZXT5FEKGKNHWWBPINFLMQKOQ44/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZXT5FEKGKNHWWBPINFLMQKOQ44/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZXT5FEKGKNHWWBPINFLMQKOQ44/action/storage_attestation","attest_author":"https://pith.science/pith/ZXT5FEKGKNHWWBPINFLMQKOQ44/action/author_attestation","sign_citation":"https://pith.science/pith/ZXT5FEKGKNHWWBPINFLMQKOQ44/action/citation_signature","submit_replication":"https://pith.science/pith/ZXT5FEKGKNHWWBPINFLMQKOQ44/action/replication_record"}},"created_at":"2026-05-18T00:30:17.598441+00:00","updated_at":"2026-05-18T00:30:17.598441+00:00"}