{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EXZ4HOBUXHHYRNNR4CKHLLCKI7","short_pith_number":"pith:EXZ4HOBU","schema_version":"1.0","canonical_sha256":"25f3c3b834b9cf88b5b1e09475ac4a47ef1822aea3ef1ec24f054a5dfd8fd670","source":{"kind":"arxiv","id":"2606.01787","version":1},"attestation_state":"computed","paper":{"title":"Stochastic convergence of parallel asynchronous adaptive first-order methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.AI","authors_text":"Philippe L. Toint, Serge Gratton","submitted_at":"2026-06-01T07:08:32Z","abstract_excerpt":"A new class of asynchronous adaptive first-order optimization methods is introduced, comprising asynchronous variants of several popular algorithms. Versions of these methods using momentum and/or inexact normalization are also considered. The convergence of methods in the class on non-convex functions is analyzed in a fully stochastic setting, and is shown to be (up to logarithmic factors) of order O(1/sqrt{t}) under reasonable assumptions. Numerical experiments suggest that such asynchronous adaptive algorithms are very relevant in heterogeneous large-scale machine learning systems."},"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":"2606.01787","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-01T07:08:32Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"d295fba5a2c785b4a82ede2aa448625a5dd6ac6805c802b0fd7ae0dd903149f9","abstract_canon_sha256":"75c1f400371d311add6cf50b0423bb5f299821170bdad86e23b4d929ffad00c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:56.831241Z","signature_b64":"9t2ZYkIbibKHpGYentdzzdc+JIf4KKgvYQ0q+wNlxsLlj5H+ywBWWGeyl6tZhGU8y79NUYQ/BdBrIr7j3cR5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25f3c3b834b9cf88b5b1e09475ac4a47ef1822aea3ef1ec24f054a5dfd8fd670","last_reissued_at":"2026-06-02T02:04:56.830881Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:56.830881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stochastic convergence of parallel asynchronous adaptive first-order methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.AI","authors_text":"Philippe L. Toint, Serge Gratton","submitted_at":"2026-06-01T07:08:32Z","abstract_excerpt":"A new class of asynchronous adaptive first-order optimization methods is introduced, comprising asynchronous variants of several popular algorithms. Versions of these methods using momentum and/or inexact normalization are also considered. The convergence of methods in the class on non-convex functions is analyzed in a fully stochastic setting, and is shown to be (up to logarithmic factors) of order O(1/sqrt{t}) under reasonable assumptions. Numerical experiments suggest that such asynchronous adaptive algorithms are very relevant in heterogeneous large-scale machine learning systems."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01787","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.01787/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.01787","created_at":"2026-06-02T02:04:56.830939+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.01787v1","created_at":"2026-06-02T02:04:56.830939+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01787","created_at":"2026-06-02T02:04:56.830939+00:00"},{"alias_kind":"pith_short_12","alias_value":"EXZ4HOBUXHHY","created_at":"2026-06-02T02:04:56.830939+00:00"},{"alias_kind":"pith_short_16","alias_value":"EXZ4HOBUXHHYRNNR","created_at":"2026-06-02T02:04:56.830939+00:00"},{"alias_kind":"pith_short_8","alias_value":"EXZ4HOBU","created_at":"2026-06-02T02:04:56.830939+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/EXZ4HOBUXHHYRNNR4CKHLLCKI7","json":"https://pith.science/pith/EXZ4HOBUXHHYRNNR4CKHLLCKI7.json","graph_json":"https://pith.science/api/pith-number/EXZ4HOBUXHHYRNNR4CKHLLCKI7/graph.json","events_json":"https://pith.science/api/pith-number/EXZ4HOBUXHHYRNNR4CKHLLCKI7/events.json","paper":"https://pith.science/paper/EXZ4HOBU"},"agent_actions":{"view_html":"https://pith.science/pith/EXZ4HOBUXHHYRNNR4CKHLLCKI7","download_json":"https://pith.science/pith/EXZ4HOBUXHHYRNNR4CKHLLCKI7.json","view_paper":"https://pith.science/paper/EXZ4HOBU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.01787&json=true","fetch_graph":"https://pith.science/api/pith-number/EXZ4HOBUXHHYRNNR4CKHLLCKI7/graph.json","fetch_events":"https://pith.science/api/pith-number/EXZ4HOBUXHHYRNNR4CKHLLCKI7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EXZ4HOBUXHHYRNNR4CKHLLCKI7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EXZ4HOBUXHHYRNNR4CKHLLCKI7/action/storage_attestation","attest_author":"https://pith.science/pith/EXZ4HOBUXHHYRNNR4CKHLLCKI7/action/author_attestation","sign_citation":"https://pith.science/pith/EXZ4HOBUXHHYRNNR4CKHLLCKI7/action/citation_signature","submit_replication":"https://pith.science/pith/EXZ4HOBUXHHYRNNR4CKHLLCKI7/action/replication_record"}},"created_at":"2026-06-02T02:04:56.830939+00:00","updated_at":"2026-06-02T02:04:56.830939+00:00"}