{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:V7VGSTGYQMSSMOAXKBLTBXEUNB","short_pith_number":"pith:V7VGSTGY","canonical_record":{"source":{"id":"1908.08465","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-22T15:50:32Z","cross_cats_sorted":["cs.GT","cs.LG"],"title_canon_sha256":"ca124ba8e29aeb41715934bf0206e6f4957e5be8d9906846f2ac16783bfe0a0f","abstract_canon_sha256":"a9e099eaec097b98bc8cef94ceef915c49e533280c146d232bb2b4e49abe1057"},"schema_version":"1.0"},"canonical_sha256":"afea694cd88325263817505730dc9468402f4136107945a9a06dd5a5c3fd0d63","source":{"kind":"arxiv","id":"1908.08465","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.08465","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"arxiv_version","alias_value":"1908.08465v2","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08465","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"pith_short_12","alias_value":"V7VGSTGYQMSS","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"pith_short_16","alias_value":"V7VGSTGYQMSSMOAX","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"pith_short_8","alias_value":"V7VGSTGY","created_at":"2026-07-05T00:39:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:V7VGSTGYQMSSMOAXKBLTBXEUNB","target":"record","payload":{"canonical_record":{"source":{"id":"1908.08465","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-22T15:50:32Z","cross_cats_sorted":["cs.GT","cs.LG"],"title_canon_sha256":"ca124ba8e29aeb41715934bf0206e6f4957e5be8d9906846f2ac16783bfe0a0f","abstract_canon_sha256":"a9e099eaec097b98bc8cef94ceef915c49e533280c146d232bb2b4e49abe1057"},"schema_version":"1.0"},"canonical_sha256":"afea694cd88325263817505730dc9468402f4136107945a9a06dd5a5c3fd0d63","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:39:32.262562Z","signature_b64":"VsGYYo9dNuX65oisu6qPfd1VNAG1Gfuiy/DhqG4XI66TN4Zb05iJisURe0ChrLRRtc9246yU4nQ6uynf3zr8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afea694cd88325263817505730dc9468402f4136107945a9a06dd5a5c3fd0d63","last_reissued_at":"2026-07-05T00:39:32.262133Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:39:32.262133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.08465","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:39:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aAggwCigKEMcAyEuIYkyIec/ulK55ux4x27YnbtoGwPatiw1J9VRsL3npO3i7ehJ+lZDFlzLDFw4Oh3I9MseAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T05:23:13.106043Z"},"content_sha256":"3c1296dfdb662f5d371315d3f6518dc12f696df033358b49cb4900ed2b2026c1","schema_version":"1.0","event_id":"sha256:3c1296dfdb662f5d371315d3f6518dc12f696df033358b49cb4900ed2b2026c1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:V7VGSTGYQMSSMOAXKBLTBXEUNB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the convergence of single-call stochastic extra-gradient methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT","cs.LG"],"primary_cat":"math.OC","authors_text":"Franck Iutzeler, J\\'er\\^ome Malick, Panayotis Mertikopoulos, Yu-Guan Hsieh","submitted_at":"2019-08-22T15:50:32Z","abstract_excerpt":"Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems). In this setting, the optimal $\\mathcal{O}(1/t)$ convergence rate for solving smooth monotone variational inequalities is achieved by the Extra-Gradient (EG) algorithm and its variants. Aiming to alleviate the cost of an extra gradient step per iteration (which can become quite substantial in deep learning applications), several algorithms have b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08465","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/1908.08465/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":"2026-07-05T00:39:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mcsN/nwDffzjIpa1Pk2S4zaF4gbYfN9keYmlDyOiwpBQ29GyCn58Bn7Uc2lb1+7gC8JrHpJDx4BKBuN2xxHYBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T05:23:13.106583Z"},"content_sha256":"2555a0c5f3e83631781c5632e1e6db509c72a34e35145d1df700a49b63d82a26","schema_version":"1.0","event_id":"sha256:2555a0c5f3e83631781c5632e1e6db509c72a34e35145d1df700a49b63d82a26"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V7VGSTGYQMSSMOAXKBLTBXEUNB/bundle.json","state_url":"https://pith.science/pith/V7VGSTGYQMSSMOAXKBLTBXEUNB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V7VGSTGYQMSSMOAXKBLTBXEUNB/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":"2026-08-16T05:23:13Z","links":{"resolver":"https://pith.science/pith/V7VGSTGYQMSSMOAXKBLTBXEUNB","bundle":"https://pith.science/pith/V7VGSTGYQMSSMOAXKBLTBXEUNB/bundle.json","state":"https://pith.science/pith/V7VGSTGYQMSSMOAXKBLTBXEUNB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V7VGSTGYQMSSMOAXKBLTBXEUNB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:V7VGSTGYQMSSMOAXKBLTBXEUNB","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":"a9e099eaec097b98bc8cef94ceef915c49e533280c146d232bb2b4e49abe1057","cross_cats_sorted":["cs.GT","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-22T15:50:32Z","title_canon_sha256":"ca124ba8e29aeb41715934bf0206e6f4957e5be8d9906846f2ac16783bfe0a0f"},"schema_version":"1.0","source":{"id":"1908.08465","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.08465","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"arxiv_version","alias_value":"1908.08465v2","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08465","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"pith_short_12","alias_value":"V7VGSTGYQMSS","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"pith_short_16","alias_value":"V7VGSTGYQMSSMOAX","created_at":"2026-07-05T00:39:32Z"},{"alias_kind":"pith_short_8","alias_value":"V7VGSTGY","created_at":"2026-07-05T00:39:32Z"}],"graph_snapshots":[{"event_id":"sha256:2555a0c5f3e83631781c5632e1e6db509c72a34e35145d1df700a49b63d82a26","target":"graph","created_at":"2026-07-05T00:39:32Z","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/1908.08465/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems). In this setting, the optimal $\\mathcal{O}(1/t)$ convergence rate for solving smooth monotone variational inequalities is achieved by the Extra-Gradient (EG) algorithm and its variants. Aiming to alleviate the cost of an extra gradient step per iteration (which can become quite substantial in deep learning applications), several algorithms have b","authors_text":"Franck Iutzeler, J\\'er\\^ome Malick, Panayotis Mertikopoulos, Yu-Guan Hsieh","cross_cats":["cs.GT","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-22T15:50:32Z","title":"On the convergence of single-call stochastic extra-gradient methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08465","kind":"arxiv","version":2},"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":"sha256:3c1296dfdb662f5d371315d3f6518dc12f696df033358b49cb4900ed2b2026c1","target":"record","created_at":"2026-07-05T00:39:32Z","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":"a9e099eaec097b98bc8cef94ceef915c49e533280c146d232bb2b4e49abe1057","cross_cats_sorted":["cs.GT","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-22T15:50:32Z","title_canon_sha256":"ca124ba8e29aeb41715934bf0206e6f4957e5be8d9906846f2ac16783bfe0a0f"},"schema_version":"1.0","source":{"id":"1908.08465","kind":"arxiv","version":2}},"canonical_sha256":"afea694cd88325263817505730dc9468402f4136107945a9a06dd5a5c3fd0d63","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afea694cd88325263817505730dc9468402f4136107945a9a06dd5a5c3fd0d63","first_computed_at":"2026-07-05T00:39:32.262133Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:39:32.262133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VsGYYo9dNuX65oisu6qPfd1VNAG1Gfuiy/DhqG4XI66TN4Zb05iJisURe0ChrLRRtc9246yU4nQ6uynf3zr8Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:39:32.262562Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.08465","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c1296dfdb662f5d371315d3f6518dc12f696df033358b49cb4900ed2b2026c1","sha256:2555a0c5f3e83631781c5632e1e6db509c72a34e35145d1df700a49b63d82a26"],"state_sha256":"8fa36ad5290fc31bbf089772b87616c4f41f13b8dd94a8b3c6eacf1fd7a192ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iDj4KiriUu8wLS5Wqqlt0njhGWisEs5527749s7d0IUw2mJaG31a39881VrSLwLMJ0wMhAOUV/t7bzc5Vy7ODg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T05:23:13.115181Z","bundle_sha256":"be84aecdabcece37c9e6dd9cda4b499e6946ff65b044db1c9a87ca38c3b0a1f1"}}