{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:2XLPT2ZAGAUNDI7HDRGHABKCEA","short_pith_number":"pith:2XLPT2ZA","canonical_record":{"source":{"id":"1608.01713","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-04T22:47:50Z","cross_cats_sorted":[],"title_canon_sha256":"7b93455ba35176eb82587e29b2d725055e054a52e81c7c1848a5eac8ba5dfa3a","abstract_canon_sha256":"593d72e2d792913f97d6303fc4f7eefebaf6427f654f08d4b07cb059a8e3940c"},"schema_version":"1.0"},"canonical_sha256":"d5d6f9eb203028d1a3e71c4c700542203656c09b017c9982de0a921ff7327a5f","source":{"kind":"arxiv","id":"1608.01713","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.01713","created_at":"2026-05-18T01:09:44Z"},{"alias_kind":"arxiv_version","alias_value":"1608.01713v1","created_at":"2026-05-18T01:09:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.01713","created_at":"2026-05-18T01:09:44Z"},{"alias_kind":"pith_short_12","alias_value":"2XLPT2ZAGAUN","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"2XLPT2ZAGAUNDI7H","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"2XLPT2ZA","created_at":"2026-05-18T12:29:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:2XLPT2ZAGAUNDI7HDRGHABKCEA","target":"record","payload":{"canonical_record":{"source":{"id":"1608.01713","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-04T22:47:50Z","cross_cats_sorted":[],"title_canon_sha256":"7b93455ba35176eb82587e29b2d725055e054a52e81c7c1848a5eac8ba5dfa3a","abstract_canon_sha256":"593d72e2d792913f97d6303fc4f7eefebaf6427f654f08d4b07cb059a8e3940c"},"schema_version":"1.0"},"canonical_sha256":"d5d6f9eb203028d1a3e71c4c700542203656c09b017c9982de0a921ff7327a5f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:09:44.797158Z","signature_b64":"lyiSURDjgKnLzO/kXAn0MHGRQHWo03LgzQktoyH+WtxKu5onmm9zw7EeVZDPc7C1jBjcJrR7bJ5j9Lmx08mRCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5d6f9eb203028d1a3e71c4c700542203656c09b017c9982de0a921ff7327a5f","last_reissued_at":"2026-05-18T01:09:44.796676Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:09:44.796676Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.01713","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":"2026-05-18T01:09:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g3GcTBIjQJcuJpWTA+BzxYPXhOtu+sU8EpuOFUyM82dLlKtzxHuonU5j99opU1TC8NJKgniB9PhP9mF2qIcCCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T03:41:13.861158Z"},"content_sha256":"c90797ea444ee3b2a584c30b824952d2ff8ec847e572b2f7e8b1428a95a34e92","schema_version":"1.0","event_id":"sha256:c90797ea444ee3b2a584c30b824952d2ff8ec847e572b2f7e8b1428a95a34e92"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:2XLPT2ZAGAUNDI7HDRGHABKCEA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Global Convergence Rate of Proximal Incremental Aggregated Gradient Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Asu Ozdaglar, Mert Gurbuzbalaban, Nuri Denizcan Vanli","submitted_at":"2016-08-04T22:47:50Z","abstract_excerpt":"We focus on the problem of minimizing the sum of smooth component functions (where the sum is strongly convex) and a non-smooth convex function, which arises in regularized empirical risk minimization in machine learning and distributed constrained optimization in wireless sensor networks and smart grids. We consider solving this problem using the proximal incremental aggregated gradient (PIAG) method, which at each iteration moves along an aggregated gradient (formed by incrementally updating gradients of component functions according to a deterministic order) and taking a proximal step with "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.01713","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":"2026-05-18T01:09:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PgUZTZysaVBNgPcMcoI4p1bW8clwco3BNwmMosr4TSYOqWe9sKyFhdvngfenVMNl+TuhGwfaEsWV10K3V7pvBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T03:41:13.861758Z"},"content_sha256":"44762bc0f6d3d2bfa8d458fa51cc8b94ffe26d34be2a6dbce09157a4b40ccfcb","schema_version":"1.0","event_id":"sha256:44762bc0f6d3d2bfa8d458fa51cc8b94ffe26d34be2a6dbce09157a4b40ccfcb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2XLPT2ZAGAUNDI7HDRGHABKCEA/bundle.json","state_url":"https://pith.science/pith/2XLPT2ZAGAUNDI7HDRGHABKCEA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2XLPT2ZAGAUNDI7HDRGHABKCEA/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":"2026-05-22T03:41:13Z","links":{"resolver":"https://pith.science/pith/2XLPT2ZAGAUNDI7HDRGHABKCEA","bundle":"https://pith.science/pith/2XLPT2ZAGAUNDI7HDRGHABKCEA/bundle.json","state":"https://pith.science/pith/2XLPT2ZAGAUNDI7HDRGHABKCEA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2XLPT2ZAGAUNDI7HDRGHABKCEA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:2XLPT2ZAGAUNDI7HDRGHABKCEA","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":"593d72e2d792913f97d6303fc4f7eefebaf6427f654f08d4b07cb059a8e3940c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-04T22:47:50Z","title_canon_sha256":"7b93455ba35176eb82587e29b2d725055e054a52e81c7c1848a5eac8ba5dfa3a"},"schema_version":"1.0","source":{"id":"1608.01713","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.01713","created_at":"2026-05-18T01:09:44Z"},{"alias_kind":"arxiv_version","alias_value":"1608.01713v1","created_at":"2026-05-18T01:09:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.01713","created_at":"2026-05-18T01:09:44Z"},{"alias_kind":"pith_short_12","alias_value":"2XLPT2ZAGAUN","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"2XLPT2ZAGAUNDI7H","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"2XLPT2ZA","created_at":"2026-05-18T12:29:55Z"}],"graph_snapshots":[{"event_id":"sha256:44762bc0f6d3d2bfa8d458fa51cc8b94ffe26d34be2a6dbce09157a4b40ccfcb","target":"graph","created_at":"2026-05-18T01:09:44Z","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 focus on the problem of minimizing the sum of smooth component functions (where the sum is strongly convex) and a non-smooth convex function, which arises in regularized empirical risk minimization in machine learning and distributed constrained optimization in wireless sensor networks and smart grids. We consider solving this problem using the proximal incremental aggregated gradient (PIAG) method, which at each iteration moves along an aggregated gradient (formed by incrementally updating gradients of component functions according to a deterministic order) and taking a proximal step with ","authors_text":"Asu Ozdaglar, Mert Gurbuzbalaban, Nuri Denizcan Vanli","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-04T22:47:50Z","title":"Global Convergence Rate of Proximal Incremental Aggregated Gradient Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.01713","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:c90797ea444ee3b2a584c30b824952d2ff8ec847e572b2f7e8b1428a95a34e92","target":"record","created_at":"2026-05-18T01:09:44Z","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":"593d72e2d792913f97d6303fc4f7eefebaf6427f654f08d4b07cb059a8e3940c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2016-08-04T22:47:50Z","title_canon_sha256":"7b93455ba35176eb82587e29b2d725055e054a52e81c7c1848a5eac8ba5dfa3a"},"schema_version":"1.0","source":{"id":"1608.01713","kind":"arxiv","version":1}},"canonical_sha256":"d5d6f9eb203028d1a3e71c4c700542203656c09b017c9982de0a921ff7327a5f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d5d6f9eb203028d1a3e71c4c700542203656c09b017c9982de0a921ff7327a5f","first_computed_at":"2026-05-18T01:09:44.796676Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:09:44.796676Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lyiSURDjgKnLzO/kXAn0MHGRQHWo03LgzQktoyH+WtxKu5onmm9zw7EeVZDPc7C1jBjcJrR7bJ5j9Lmx08mRCw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:09:44.797158Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.01713","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c90797ea444ee3b2a584c30b824952d2ff8ec847e572b2f7e8b1428a95a34e92","sha256:44762bc0f6d3d2bfa8d458fa51cc8b94ffe26d34be2a6dbce09157a4b40ccfcb"],"state_sha256":"ed4f9fe227c8dfc16183d363df900b185a614f552d4f0e563b4be54c89b65541"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JEr3IIyq0lrMSZQ7yIq6dtKk1jazhQVJDjBtUJ+15P6IWeQ+vsW/Lx/ECuSQQeozIBKpb5OG1PfJpEjELVdfCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T03:41:13.864703Z","bundle_sha256":"2688faee2db4dd47c4dbf03ce02c0194014c7429a768f86b77335b3305bd272e"}}