{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:R2HHBMZ7Y3KM2CRPTURU6R4FUR","short_pith_number":"pith:R2HHBMZ7","canonical_record":{"source":{"id":"1708.07620","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-08-25T06:20:51Z","cross_cats_sorted":[],"title_canon_sha256":"4a9ac2b6c28b586fd11e4cd21bf05e6aeeda1108fb2dd1c8535053401b04f760","abstract_canon_sha256":"69ce3b990ad9168822d43f7af92b5783bd2e378c2dbf058eee4136fd6e5196f5"},"schema_version":"1.0"},"canonical_sha256":"8e8e70b33fc6d4cd0a2f9d234f4785a477871405a844617a06a89b03e5900439","source":{"kind":"arxiv","id":"1708.07620","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.07620","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"arxiv_version","alias_value":"1708.07620v2","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.07620","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"pith_short_12","alias_value":"R2HHBMZ7Y3KM","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"R2HHBMZ7Y3KM2CRP","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"R2HHBMZ7","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:R2HHBMZ7Y3KM2CRPTURU6R4FUR","target":"record","payload":{"canonical_record":{"source":{"id":"1708.07620","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-08-25T06:20:51Z","cross_cats_sorted":[],"title_canon_sha256":"4a9ac2b6c28b586fd11e4cd21bf05e6aeeda1108fb2dd1c8535053401b04f760","abstract_canon_sha256":"69ce3b990ad9168822d43f7af92b5783bd2e378c2dbf058eee4136fd6e5196f5"},"schema_version":"1.0"},"canonical_sha256":"8e8e70b33fc6d4cd0a2f9d234f4785a477871405a844617a06a89b03e5900439","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:13.834322Z","signature_b64":"jgW3pbieoH54ZVNOAgLhIXysRsVxJIykumU9FraxJFLFyZO4mbjWt0FvGQ/AJNfZ28gTA9NohB8xEUTiqMI5Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e8e70b33fc6d4cd0a2f9d234f4785a477871405a844617a06a89b03e5900439","last_reissued_at":"2026-05-18T00:19:13.833742Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:13.833742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.07620","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":"2026-05-18T00:19:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eGo9FCrWct7l+dX9drbpCgRu0tU5qsfx+ZXf+wR++uXr4GjevO4SSGafjqelCPhs2+GlO21bROd8sO4ZiMgGAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T14:52:46.371666Z"},"content_sha256":"a62893127571a1a13ef859f0abe673320df2b2524bb68fb1f6a4a6db0baa132c","schema_version":"1.0","event_id":"sha256:a62893127571a1a13ef859f0abe673320df2b2524bb68fb1f6a4a6db0baa132c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:R2HHBMZ7Y3KM2CRPTURU6R4FUR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Jie Lu, Xuyang Wu","submitted_at":"2017-08-25T06:20:51Z","abstract_excerpt":"In the large collection of existing distributed algorithms for convex multi-agent optimization, only a handful of them provide convergence rate guarantees on agent networks with time-varying topologies, which, however, restrict the problem to be unconstrained. Motivated by this, we develop a family of distributed Fenchel dual gradient methods for solving constrained, strongly convex but not necessarily smooth multi-agent optimization problems over time-varying undirected networks. The proposed algorithms are constructed based on the application of weighted gradient methods to the Fenchel dual "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.07620","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":""},"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":"2026-05-18T00:19:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BXdQgxCj4qkcdscS4AAMV+55xBK2jjRkvppC1m6t/8WlTltGQdai8mAGc5RoQIYDL1auW6bo75EZG7JyxuWHBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T14:52:46.372014Z"},"content_sha256":"9165283cedd1ec158d43178fe7b056733f439fbd8e7a0bfd85ef9ede449196b2","schema_version":"1.0","event_id":"sha256:9165283cedd1ec158d43178fe7b056733f439fbd8e7a0bfd85ef9ede449196b2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R2HHBMZ7Y3KM2CRPTURU6R4FUR/bundle.json","state_url":"https://pith.science/pith/R2HHBMZ7Y3KM2CRPTURU6R4FUR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R2HHBMZ7Y3KM2CRPTURU6R4FUR/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":"2026-06-22T14:52:46Z","links":{"resolver":"https://pith.science/pith/R2HHBMZ7Y3KM2CRPTURU6R4FUR","bundle":"https://pith.science/pith/R2HHBMZ7Y3KM2CRPTURU6R4FUR/bundle.json","state":"https://pith.science/pith/R2HHBMZ7Y3KM2CRPTURU6R4FUR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R2HHBMZ7Y3KM2CRPTURU6R4FUR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:R2HHBMZ7Y3KM2CRPTURU6R4FUR","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":"69ce3b990ad9168822d43f7af92b5783bd2e378c2dbf058eee4136fd6e5196f5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-08-25T06:20:51Z","title_canon_sha256":"4a9ac2b6c28b586fd11e4cd21bf05e6aeeda1108fb2dd1c8535053401b04f760"},"schema_version":"1.0","source":{"id":"1708.07620","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.07620","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"arxiv_version","alias_value":"1708.07620v2","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.07620","created_at":"2026-05-18T00:19:13Z"},{"alias_kind":"pith_short_12","alias_value":"R2HHBMZ7Y3KM","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"R2HHBMZ7Y3KM2CRP","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"R2HHBMZ7","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:9165283cedd1ec158d43178fe7b056733f439fbd8e7a0bfd85ef9ede449196b2","target":"graph","created_at":"2026-05-18T00:19:13Z","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":"In the large collection of existing distributed algorithms for convex multi-agent optimization, only a handful of them provide convergence rate guarantees on agent networks with time-varying topologies, which, however, restrict the problem to be unconstrained. Motivated by this, we develop a family of distributed Fenchel dual gradient methods for solving constrained, strongly convex but not necessarily smooth multi-agent optimization problems over time-varying undirected networks. The proposed algorithms are constructed based on the application of weighted gradient methods to the Fenchel dual ","authors_text":"Jie Lu, Xuyang Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-08-25T06:20:51Z","title":"Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.07620","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:a62893127571a1a13ef859f0abe673320df2b2524bb68fb1f6a4a6db0baa132c","target":"record","created_at":"2026-05-18T00:19:13Z","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":"69ce3b990ad9168822d43f7af92b5783bd2e378c2dbf058eee4136fd6e5196f5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-08-25T06:20:51Z","title_canon_sha256":"4a9ac2b6c28b586fd11e4cd21bf05e6aeeda1108fb2dd1c8535053401b04f760"},"schema_version":"1.0","source":{"id":"1708.07620","kind":"arxiv","version":2}},"canonical_sha256":"8e8e70b33fc6d4cd0a2f9d234f4785a477871405a844617a06a89b03e5900439","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8e8e70b33fc6d4cd0a2f9d234f4785a477871405a844617a06a89b03e5900439","first_computed_at":"2026-05-18T00:19:13.833742Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:13.833742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jgW3pbieoH54ZVNOAgLhIXysRsVxJIykumU9FraxJFLFyZO4mbjWt0FvGQ/AJNfZ28gTA9NohB8xEUTiqMI5Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:13.834322Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.07620","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a62893127571a1a13ef859f0abe673320df2b2524bb68fb1f6a4a6db0baa132c","sha256:9165283cedd1ec158d43178fe7b056733f439fbd8e7a0bfd85ef9ede449196b2"],"state_sha256":"cfd5d582c0bb848755f8d3d7f1c36596929494e76c93512b048a2706df008ed3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NtdGkPfUFReoWuxC+R7QuC/p3CjmTx9mCNd+HEHy8Tc2bcLiEKtVU1iDbmcVVLHkSTBzojGkUGexBjV0fm9wDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T14:52:46.373922Z","bundle_sha256":"b897a019286ee841ff7f77e7e829322b8e4a99f7ed67296d475502c93bf43e3e"}}