{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6W4POCKDMJYAGQZW3IKZKECZJ6","short_pith_number":"pith:6W4POCKD","canonical_record":{"source":{"id":"1802.04310","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-12T19:13:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"57964cac3a8bc7078ebc674521f52027be722c0b84b812577ee1ff0fd35c4ed0","abstract_canon_sha256":"24f3d67b590dddff2a863b3035cd05e3b81bf51d649b8e4e4dc383751fb122e0"},"schema_version":"1.0"},"canonical_sha256":"f5b8f709436270034336da159510594fa90ddf74b47d012fb5233eff5d9037dd","source":{"kind":"arxiv","id":"1802.04310","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.04310","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"arxiv_version","alias_value":"1802.04310v1","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.04310","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"pith_short_12","alias_value":"6W4POCKDMJYA","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6W4POCKDMJYAGQZW","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6W4POCKD","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6W4POCKDMJYAGQZW3IKZKECZJ6","target":"record","payload":{"canonical_record":{"source":{"id":"1802.04310","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-12T19:13:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"57964cac3a8bc7078ebc674521f52027be722c0b84b812577ee1ff0fd35c4ed0","abstract_canon_sha256":"24f3d67b590dddff2a863b3035cd05e3b81bf51d649b8e4e4dc383751fb122e0"},"schema_version":"1.0"},"canonical_sha256":"f5b8f709436270034336da159510594fa90ddf74b47d012fb5233eff5d9037dd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:44.569775Z","signature_b64":"NfhcB+yU5YawRN/rKT+/0VkmXfmKWb/ZVD2OQOVvE8qk1P17/mS0Ub7TPGO5JMqEEJp6vRRTZQjLQolszf/5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5b8f709436270034336da159510594fa90ddf74b47d012fb5233eff5d9037dd","last_reissued_at":"2026-05-18T00:23:44.569307Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:44.569307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.04310","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":"2026-05-18T00:23:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mtKWZKyn5nLm4rzxooe4Yc7XOfej3nUNzp5S1AzuhIUgZDUwE+/cVp8411yxQVU3+xMuMVYsxg7Or6xEYoB2DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:40:57.841998Z"},"content_sha256":"ed31c9af3b47174edd81157c84f6e5c37dfba7b27d99baf2ac5660c831bd9c52","schema_version":"1.0","event_id":"sha256:ed31c9af3b47174edd81157c84f6e5c37dfba7b27d99baf2ac5660c831bd9c52"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6W4POCKDMJYAGQZW3IKZKECZJ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stochastic quasi-Newton with adaptive step lengths for large-scale problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Adrian Wills, Thomas Sch\\\"on","submitted_at":"2018-02-12T19:13:28Z","abstract_excerpt":"We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems. Our key innovation is an auxiliary variable construction coupled with an inverse Hessian approximation computed using a receding history of iterates and gradients. It is the Markov chain nature of the classic stochastic gradient algorithm that enables this development. The construction offers a mechanism for stochastic line search adapting the step length. We numerically evaluate and compare against current state-of-the-art with encouraging perform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.04310","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":"2026-05-18T00:23:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RDr2/YCia6MUEnulhIuu4EhDeU8c+uYgQgtZZsUNFTkNhuRqJXlVqvgdJdZ6gmJr1NwbWnj1bZzZEd0kkcRLDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:40:57.842358Z"},"content_sha256":"9fb3a2c8f605be653b3509b68010fa9fefa3c625ea7c54fbbcfb4a66325baf8e","schema_version":"1.0","event_id":"sha256:9fb3a2c8f605be653b3509b68010fa9fefa3c625ea7c54fbbcfb4a66325baf8e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6W4POCKDMJYAGQZW3IKZKECZJ6/bundle.json","state_url":"https://pith.science/pith/6W4POCKDMJYAGQZW3IKZKECZJ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6W4POCKDMJYAGQZW3IKZKECZJ6/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":"2026-06-06T19:40:57Z","links":{"resolver":"https://pith.science/pith/6W4POCKDMJYAGQZW3IKZKECZJ6","bundle":"https://pith.science/pith/6W4POCKDMJYAGQZW3IKZKECZJ6/bundle.json","state":"https://pith.science/pith/6W4POCKDMJYAGQZW3IKZKECZJ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6W4POCKDMJYAGQZW3IKZKECZJ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6W4POCKDMJYAGQZW3IKZKECZJ6","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":"24f3d67b590dddff2a863b3035cd05e3b81bf51d649b8e4e4dc383751fb122e0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-12T19:13:28Z","title_canon_sha256":"57964cac3a8bc7078ebc674521f52027be722c0b84b812577ee1ff0fd35c4ed0"},"schema_version":"1.0","source":{"id":"1802.04310","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.04310","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"arxiv_version","alias_value":"1802.04310v1","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.04310","created_at":"2026-05-18T00:23:44Z"},{"alias_kind":"pith_short_12","alias_value":"6W4POCKDMJYA","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6W4POCKDMJYAGQZW","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6W4POCKD","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:9fb3a2c8f605be653b3509b68010fa9fefa3c625ea7c54fbbcfb4a66325baf8e","target":"graph","created_at":"2026-05-18T00:23:44Z","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":"We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems. Our key innovation is an auxiliary variable construction coupled with an inverse Hessian approximation computed using a receding history of iterates and gradients. It is the Markov chain nature of the classic stochastic gradient algorithm that enables this development. The construction offers a mechanism for stochastic line search adapting the step length. We numerically evaluate and compare against current state-of-the-art with encouraging perform","authors_text":"Adrian Wills, Thomas Sch\\\"on","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-12T19:13:28Z","title":"Stochastic quasi-Newton with adaptive step lengths for large-scale problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.04310","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:ed31c9af3b47174edd81157c84f6e5c37dfba7b27d99baf2ac5660c831bd9c52","target":"record","created_at":"2026-05-18T00:23:44Z","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":"24f3d67b590dddff2a863b3035cd05e3b81bf51d649b8e4e4dc383751fb122e0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-12T19:13:28Z","title_canon_sha256":"57964cac3a8bc7078ebc674521f52027be722c0b84b812577ee1ff0fd35c4ed0"},"schema_version":"1.0","source":{"id":"1802.04310","kind":"arxiv","version":1}},"canonical_sha256":"f5b8f709436270034336da159510594fa90ddf74b47d012fb5233eff5d9037dd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f5b8f709436270034336da159510594fa90ddf74b47d012fb5233eff5d9037dd","first_computed_at":"2026-05-18T00:23:44.569307Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:44.569307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NfhcB+yU5YawRN/rKT+/0VkmXfmKWb/ZVD2OQOVvE8qk1P17/mS0Ub7TPGO5JMqEEJp6vRRTZQjLQolszf/5BA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:44.569775Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.04310","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ed31c9af3b47174edd81157c84f6e5c37dfba7b27d99baf2ac5660c831bd9c52","sha256:9fb3a2c8f605be653b3509b68010fa9fefa3c625ea7c54fbbcfb4a66325baf8e"],"state_sha256":"4b9dac05550909741a0e7d033dafc3444a2be81d2eb93f47fdaa2f5731d0d9c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4Ff1bAafK+Ndzt2SqIyG13IOG8ASofKyHLNKryxF0Rl1CEpt04qJ2YC/x/PlgTLAFZCa0Y1sPiGYqGR3V3/SBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T19:40:57.845748Z","bundle_sha256":"d40b9f29adc4bab0838ae01451e8da7d3374f46753ef35fe62202b67fc7ffb9b"}}