{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:HC6UHGYIIFZBG62244LBGFWA7C","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":"e872b6ea9a4a9ed24decdc8ec04f223daf61c386fcab72c473a7d3f410dd8804","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-04T14:47:32Z","title_canon_sha256":"62f19dedea21fb27878b949c9ae5d912801853a730932e073e45f37daddad806"},"schema_version":"1.0","source":{"id":"1709.00982","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.00982","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"arxiv_version","alias_value":"1709.00982v1","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.00982","created_at":"2026-05-18T00:36:04Z"},{"alias_kind":"pith_short_12","alias_value":"HC6UHGYIIFZB","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"HC6UHGYIIFZBG622","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"HC6UHGYI","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:13d9f3093cf50d84182ca657cf6ce339b88f029a1793d8049beec21fac338e39","target":"graph","created_at":"2026-05-18T00:36:04Z","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 problem of minimizing a separable convex function under linearly coupled constraints arises from various application domains such as economic systems, distributed control, and network flow. The main challenge for solving this problem is that the size of data is very large, which makes usual gradient-based methods infeasible. Recently, Necoara, Nesterov, and Glineur [Journal of Optimization Theory and Applications, 173 (2017) 227-2254] proposed an efficient randomized coordinate descent method to solve this type of optimization problems and presented an appealing convergence analysis. In th","authors_text":"Min Xu, Qin Fan, Yiming Ying","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-04T14:47:32Z","title":"Faster Convergence of a Randomized Coordinate Descent Method for Linearly Constrained Optimization Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.00982","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":"sha256:8738aa8a070c9d57608091554038f197e076c122c60cb1c531970aae8371ae34","target":"record","created_at":"2026-05-18T00:36:04Z","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":"e872b6ea9a4a9ed24decdc8ec04f223daf61c386fcab72c473a7d3f410dd8804","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-04T14:47:32Z","title_canon_sha256":"62f19dedea21fb27878b949c9ae5d912801853a730932e073e45f37daddad806"},"schema_version":"1.0","source":{"id":"1709.00982","kind":"arxiv","version":1}},"canonical_sha256":"38bd439b084172137b5ae7161316c0f8bd9c48800ceac4b317a0e09aa23f9c2c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38bd439b084172137b5ae7161316c0f8bd9c48800ceac4b317a0e09aa23f9c2c","first_computed_at":"2026-05-18T00:36:04.557582Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:04.557582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IuK00uFF/sf/cR8QbSUuz7FIaZTalRbBtW2mm8DQCT/kfqxzCXcZCQq+CnFvAUFcSXXthZgcRSEipdm9B/UICQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:04.558119Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.00982","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8738aa8a070c9d57608091554038f197e076c122c60cb1c531970aae8371ae34","sha256:13d9f3093cf50d84182ca657cf6ce339b88f029a1793d8049beec21fac338e39"],"state_sha256":"5598e9fce6fd0d3e28ffc82ad9cc5df8584333d538418e57377e131a5e910880"}