{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:VXTXKXVXMECB6ADOG5JQSY2A27","short_pith_number":"pith:VXTXKXVX","canonical_record":{"source":{"id":"1805.10579","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-05-27T04:22:14Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"0f995cd1f680afb431a23c69030619ff5ddc368795958b38d22a195cf8793088","abstract_canon_sha256":"9312876946cdebbb6549ea0bec147357e1ba8a4cbd362fed32fafb0349f02a81"},"schema_version":"1.0"},"canonical_sha256":"ade7755eb761041f006e3753096340d7f254c0b9e6d0f75c93d64283a1fc33cf","source":{"kind":"arxiv","id":"1805.10579","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10579","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10579v4","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10579","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"pith_short_12","alias_value":"VXTXKXVXMECB","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"pith_short_16","alias_value":"VXTXKXVXMECB6ADO","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"pith_short_8","alias_value":"VXTXKXVX","created_at":"2026-07-05T00:17:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:VXTXKXVXMECB6ADOG5JQSY2A27","target":"record","payload":{"canonical_record":{"source":{"id":"1805.10579","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-05-27T04:22:14Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"0f995cd1f680afb431a23c69030619ff5ddc368795958b38d22a195cf8793088","abstract_canon_sha256":"9312876946cdebbb6549ea0bec147357e1ba8a4cbd362fed32fafb0349f02a81"},"schema_version":"1.0"},"canonical_sha256":"ade7755eb761041f006e3753096340d7f254c0b9e6d0f75c93d64283a1fc33cf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:17:24.625226Z","signature_b64":"w5W5xiy7ANC4eBSm7kUksRqaS9+YuNnfp4KsjJx+gIuWy3Rxw2ehxPREwcWF2E13/bEeoEn8RPd+y4LLH1o2AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ade7755eb761041f006e3753096340d7f254c0b9e6d0f75c93d64283a1fc33cf","last_reissued_at":"2026-07-05T00:17:24.624763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:17:24.624763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.10579","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:17:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1WLFiFZC7fw3n83WiAiFAUNi8zyLWqKU72w8r80vQQiC7DBHcKBsgd3s7pqFbPxZS8TAEeTqySEqMtogef7uBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T16:02:44.540714Z"},"content_sha256":"f525658a1ceda96d9f262344653302c02b7bc8574c48f6c12e0853a58932757b","schema_version":"1.0","event_id":"sha256:f525658a1ceda96d9f262344653302c02b7bc8574c48f6c12e0853a58932757b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:VXTXKXVXMECB6ADOG5JQSY2A27","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Alireza Fallah, Asuman Ozdaglar, Mert Gurbuzbalaban, Necdet Serhat Aybat","submitted_at":"2018-05-27T04:22:14Z","abstract_excerpt":"We study the trade-offs between convergence rate and robustness to gradient errors in designing a first-order algorithm. We focus on gradient descent (GD) and accelerated gradient (AG) methods for minimizing strongly convex functions when the gradient has random errors in the form of additive white noise. With gradient errors, the function values of the iterates need not converge to the optimal value; hence, we define the robustness of an algorithm to noise as the asymptotic expected suboptimality of the iterate sequence to input noise power. For this robustness measure, we provide exact expre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10579","kind":"arxiv","version":4},"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/1805.10579/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:17:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nx5bp3sf8RmOeGiVWPoQsyjZav5acu5oiTshAXAnPzOEw9fSH8IUWmk66qn1h5IU5GW1P8/Y7sxP59aFWBkoAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-11T16:02:44.541101Z"},"content_sha256":"0001f43cecbb557439f0dd2e4098c2c7f8096e97db99a615c94ae5ce492e149e","schema_version":"1.0","event_id":"sha256:0001f43cecbb557439f0dd2e4098c2c7f8096e97db99a615c94ae5ce492e149e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VXTXKXVXMECB6ADOG5JQSY2A27/bundle.json","state_url":"https://pith.science/pith/VXTXKXVXMECB6ADOG5JQSY2A27/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VXTXKXVXMECB6ADOG5JQSY2A27/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-11T16:02:44Z","links":{"resolver":"https://pith.science/pith/VXTXKXVXMECB6ADOG5JQSY2A27","bundle":"https://pith.science/pith/VXTXKXVXMECB6ADOG5JQSY2A27/bundle.json","state":"https://pith.science/pith/VXTXKXVXMECB6ADOG5JQSY2A27/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VXTXKXVXMECB6ADOG5JQSY2A27/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:VXTXKXVXMECB6ADOG5JQSY2A27","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9312876946cdebbb6549ea0bec147357e1ba8a4cbd362fed32fafb0349f02a81","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-05-27T04:22:14Z","title_canon_sha256":"0f995cd1f680afb431a23c69030619ff5ddc368795958b38d22a195cf8793088"},"schema_version":"1.0","source":{"id":"1805.10579","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10579","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10579v4","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10579","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"pith_short_12","alias_value":"VXTXKXVXMECB","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"pith_short_16","alias_value":"VXTXKXVXMECB6ADO","created_at":"2026-07-05T00:17:24Z"},{"alias_kind":"pith_short_8","alias_value":"VXTXKXVX","created_at":"2026-07-05T00:17:24Z"}],"graph_snapshots":[{"event_id":"sha256:0001f43cecbb557439f0dd2e4098c2c7f8096e97db99a615c94ae5ce492e149e","target":"graph","created_at":"2026-07-05T00:17:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1805.10579/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study the trade-offs between convergence rate and robustness to gradient errors in designing a first-order algorithm. We focus on gradient descent (GD) and accelerated gradient (AG) methods for minimizing strongly convex functions when the gradient has random errors in the form of additive white noise. With gradient errors, the function values of the iterates need not converge to the optimal value; hence, we define the robustness of an algorithm to noise as the asymptotic expected suboptimality of the iterate sequence to input noise power. For this robustness measure, we provide exact expre","authors_text":"Alireza Fallah, Asuman Ozdaglar, Mert Gurbuzbalaban, Necdet Serhat Aybat","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-05-27T04:22:14Z","title":"Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10579","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f525658a1ceda96d9f262344653302c02b7bc8574c48f6c12e0853a58932757b","target":"record","created_at":"2026-07-05T00:17:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9312876946cdebbb6549ea0bec147357e1ba8a4cbd362fed32fafb0349f02a81","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-05-27T04:22:14Z","title_canon_sha256":"0f995cd1f680afb431a23c69030619ff5ddc368795958b38d22a195cf8793088"},"schema_version":"1.0","source":{"id":"1805.10579","kind":"arxiv","version":4}},"canonical_sha256":"ade7755eb761041f006e3753096340d7f254c0b9e6d0f75c93d64283a1fc33cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ade7755eb761041f006e3753096340d7f254c0b9e6d0f75c93d64283a1fc33cf","first_computed_at":"2026-07-05T00:17:24.624763Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:17:24.624763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w5W5xiy7ANC4eBSm7kUksRqaS9+YuNnfp4KsjJx+gIuWy3Rxw2ehxPREwcWF2E13/bEeoEn8RPd+y4LLH1o2AA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:17:24.625226Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.10579","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f525658a1ceda96d9f262344653302c02b7bc8574c48f6c12e0853a58932757b","sha256:0001f43cecbb557439f0dd2e4098c2c7f8096e97db99a615c94ae5ce492e149e"],"state_sha256":"7b1f4b0e201d3c1dc850839c36d5b77f57a0afae059868524f5dc9c96e883ca6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lLDGe7EEr87ObNEfYm1DzAfyucSCyOnqPLY6rFR2iYKlmj1rm24TEJQkXt+hhlRRmx7rT7HvRkJqIJ2jlKxJBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-11T16:02:44.543184Z","bundle_sha256":"db97b4876ccd4219ce38d1a35bef100150f996182853a66f879215594abf30a8"}}