{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:FJ523REVEG5PNFB2J7DDROE35K","short_pith_number":"pith:FJ523REV","canonical_record":{"source":{"id":"1810.13387","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-10-31T16:35:09Z","cross_cats_sorted":[],"title_canon_sha256":"3cdf8b99357338b14111364fb53a69e2b41bc2c90103f1913e113fdc3baf60c8","abstract_canon_sha256":"b131181418aadceab8703cfbb331369bc686e176f7262d660b22bbf4f6a82ce3"},"schema_version":"1.0"},"canonical_sha256":"2a7badc49521baf6943a4fc638b89beab0ab92a2080ad3990e71ebbc0cc7e861","source":{"kind":"arxiv","id":"1810.13387","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.13387","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"arxiv_version","alias_value":"1810.13387v1","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13387","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"pith_short_12","alias_value":"FJ523REVEG5P","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"FJ523REVEG5PNFB2","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"FJ523REV","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:FJ523REVEG5PNFB2J7DDROE35K","target":"record","payload":{"canonical_record":{"source":{"id":"1810.13387","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-10-31T16:35:09Z","cross_cats_sorted":[],"title_canon_sha256":"3cdf8b99357338b14111364fb53a69e2b41bc2c90103f1913e113fdc3baf60c8","abstract_canon_sha256":"b131181418aadceab8703cfbb331369bc686e176f7262d660b22bbf4f6a82ce3"},"schema_version":"1.0"},"canonical_sha256":"2a7badc49521baf6943a4fc638b89beab0ab92a2080ad3990e71ebbc0cc7e861","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:49.452360Z","signature_b64":"QUE0b8jetYeSlcniv2E5nGxV664lDMMBUBUEvpyCLRaw7tk0SHj7fBAunN4VNa/t4jmfCUrFquEf85HgWQy3BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a7badc49521baf6943a4fc638b89beab0ab92a2080ad3990e71ebbc0cc7e861","last_reissued_at":"2026-05-18T00:01:49.451649Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:49.451649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.13387","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sVfGKPDjEOk+rofFpJR9XaigLQg5jHSt60g0mEWIHGmzmHgkvX37FKSWbYnkdsmkSeaLbODx97l2F62yBCGYBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T11:03:26.467483Z"},"content_sha256":"bc56df66aeeeb48bf4280bd75a2aeb726b1a2b10401ef252db4bd5fe3963aade","schema_version":"1.0","event_id":"sha256:bc56df66aeeeb48bf4280bd75a2aeb726b1a2b10401ef252db4bd5fe3963aade"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:FJ523REVEG5PNFB2J7DDROE35K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Stochastic Penalty Model for Convex and Nonconvex Optimization with Big Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Konstantin Mishchenko, Peter Richt\\'arik","submitted_at":"2018-10-31T16:35:09Z","abstract_excerpt":"The last decade witnessed a rise in the importance of supervised learning applications involving {\\em big data} and {\\em big models}. Big data refers to situations where the amounts of training data available and needed causes difficulties in the training phase of the pipeline. Big model refers to situations where large dimensional and over-parameterized models are needed for the application at hand. Both of these phenomena lead to a dramatic increase in research activity aimed at taming the issues via the design of new sophisticated optimization algorithms. In this paper we turn attention to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13387","kind":"arxiv","version":1},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m9vyzAJlQmAphMzJ755sH9PDcDZvfwdKLDlnX98uXwAU5QeAuzr292iaWdueEViynj6b4op4xuTsLXEtI+X6Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-24T11:03:26.468107Z"},"content_sha256":"b4680dcd3cd0e7e50211b7a6025044d55e58e0c5308fe6b197f17ebef18e9743","schema_version":"1.0","event_id":"sha256:b4680dcd3cd0e7e50211b7a6025044d55e58e0c5308fe6b197f17ebef18e9743"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FJ523REVEG5PNFB2J7DDROE35K/bundle.json","state_url":"https://pith.science/pith/FJ523REVEG5PNFB2J7DDROE35K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FJ523REVEG5PNFB2J7DDROE35K/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":"2026-05-24T11:03:26Z","links":{"resolver":"https://pith.science/pith/FJ523REVEG5PNFB2J7DDROE35K","bundle":"https://pith.science/pith/FJ523REVEG5PNFB2J7DDROE35K/bundle.json","state":"https://pith.science/pith/FJ523REVEG5PNFB2J7DDROE35K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FJ523REVEG5PNFB2J7DDROE35K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FJ523REVEG5PNFB2J7DDROE35K","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":"b131181418aadceab8703cfbb331369bc686e176f7262d660b22bbf4f6a82ce3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-10-31T16:35:09Z","title_canon_sha256":"3cdf8b99357338b14111364fb53a69e2b41bc2c90103f1913e113fdc3baf60c8"},"schema_version":"1.0","source":{"id":"1810.13387","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.13387","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"arxiv_version","alias_value":"1810.13387v1","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13387","created_at":"2026-05-18T00:01:49Z"},{"alias_kind":"pith_short_12","alias_value":"FJ523REVEG5P","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"FJ523REVEG5PNFB2","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"FJ523REV","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:b4680dcd3cd0e7e50211b7a6025044d55e58e0c5308fe6b197f17ebef18e9743","target":"graph","created_at":"2026-05-18T00:01:49Z","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"},"paper":{"abstract_excerpt":"The last decade witnessed a rise in the importance of supervised learning applications involving {\\em big data} and {\\em big models}. Big data refers to situations where the amounts of training data available and needed causes difficulties in the training phase of the pipeline. Big model refers to situations where large dimensional and over-parameterized models are needed for the application at hand. Both of these phenomena lead to a dramatic increase in research activity aimed at taming the issues via the design of new sophisticated optimization algorithms. In this paper we turn attention to ","authors_text":"Konstantin Mishchenko, Peter Richt\\'arik","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-10-31T16:35:09Z","title":"A Stochastic Penalty Model for Convex and Nonconvex Optimization with Big Constraints"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13387","kind":"arxiv","version":1},"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:bc56df66aeeeb48bf4280bd75a2aeb726b1a2b10401ef252db4bd5fe3963aade","target":"record","created_at":"2026-05-18T00:01:49Z","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":"b131181418aadceab8703cfbb331369bc686e176f7262d660b22bbf4f6a82ce3","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-10-31T16:35:09Z","title_canon_sha256":"3cdf8b99357338b14111364fb53a69e2b41bc2c90103f1913e113fdc3baf60c8"},"schema_version":"1.0","source":{"id":"1810.13387","kind":"arxiv","version":1}},"canonical_sha256":"2a7badc49521baf6943a4fc638b89beab0ab92a2080ad3990e71ebbc0cc7e861","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2a7badc49521baf6943a4fc638b89beab0ab92a2080ad3990e71ebbc0cc7e861","first_computed_at":"2026-05-18T00:01:49.451649Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:49.451649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QUE0b8jetYeSlcniv2E5nGxV664lDMMBUBUEvpyCLRaw7tk0SHj7fBAunN4VNa/t4jmfCUrFquEf85HgWQy3BA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:49.452360Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.13387","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc56df66aeeeb48bf4280bd75a2aeb726b1a2b10401ef252db4bd5fe3963aade","sha256:b4680dcd3cd0e7e50211b7a6025044d55e58e0c5308fe6b197f17ebef18e9743"],"state_sha256":"d7a06741c3c9bdbeed1b5b3a09a8478387165c6a4fe6a9c794ca6bad20f8ae6d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PdPRDBN3DB4YL92MqxcOb5FG/F0Iush40kMAmUGFbE7ENgw2HK6miZnqd0wqsyp8IGLeOpllfiySOhWdG2ZcCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-24T11:03:26.471971Z","bundle_sha256":"ce7fb4453789c26e8e1336df42c47044244d7eb7f174ac7760c32b51dec38b0c"}}