{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UT4LT2PDPSNEZXLPLOJDALCROE","short_pith_number":"pith:UT4LT2PD","schema_version":"1.0","canonical_sha256":"a4f8b9e9e37c9a4cdd6f5b92302c517121ca069da98c9ccef57ab0ccfe339ee8","source":{"kind":"arxiv","id":"1702.06219","version":1},"attestation_state":"computed","paper":{"title":"An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA","stat.ML"],"primary_cat":"math.OC","authors_text":"Ali Jadbabaie, Shahin Shahrampour","submitted_at":"2017-02-21T00:18:14Z","abstract_excerpt":"This paper addresses tracking of a moving target in a multi-agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The location of the target at each time induces a global time-varying loss function, and the global loss is a sum of local losses, each of which is associated to one agent. Agents noisy observations could be nonlinear. We formulate this problem as a distributed online optimization where agents communicate with each other to track the minimizer of the global loss. We then propose a dec"},"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":"1702.06219","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-02-21T00:18:14Z","cross_cats_sorted":["cs.MA","stat.ML"],"title_canon_sha256":"a9574c3007da109de0403ec020ee16f417ef2e91e3fabcee544a6156a0a72a18","abstract_canon_sha256":"fbdc29acbce65d2862d2e4d96832c0390b81ec41b74a0f5229442a24238e7a4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:14.701976Z","signature_b64":"VLEaKTRudYDNcu5GUD6IvqlG8eem64UrRz3UwHOTgAXodh7cObaCkEM0jfbsdPRSR5tgIebx7/7QPf3lNSYLAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4f8b9e9e37c9a4cdd6f5b92302c517121ca069da98c9ccef57ab0ccfe339ee8","last_reissued_at":"2026-05-18T00:50:14.701403Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:14.701403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA","stat.ML"],"primary_cat":"math.OC","authors_text":"Ali Jadbabaie, Shahin Shahrampour","submitted_at":"2017-02-21T00:18:14Z","abstract_excerpt":"This paper addresses tracking of a moving target in a multi-agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The location of the target at each time induces a global time-varying loss function, and the global loss is a sum of local losses, each of which is associated to one agent. Agents noisy observations could be nonlinear. We formulate this problem as a distributed online optimization where agents communicate with each other to track the minimizer of the global loss. We then propose a dec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.06219","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1702.06219","created_at":"2026-05-18T00:50:14.701485+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.06219v1","created_at":"2026-05-18T00:50:14.701485+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.06219","created_at":"2026-05-18T00:50:14.701485+00:00"},{"alias_kind":"pith_short_12","alias_value":"UT4LT2PDPSNE","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_16","alias_value":"UT4LT2PDPSNEZXLP","created_at":"2026-05-18T12:31:49.984773+00:00"},{"alias_kind":"pith_short_8","alias_value":"UT4LT2PD","created_at":"2026-05-18T12:31:49.984773+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/UT4LT2PDPSNEZXLPLOJDALCROE","json":"https://pith.science/pith/UT4LT2PDPSNEZXLPLOJDALCROE.json","graph_json":"https://pith.science/api/pith-number/UT4LT2PDPSNEZXLPLOJDALCROE/graph.json","events_json":"https://pith.science/api/pith-number/UT4LT2PDPSNEZXLPLOJDALCROE/events.json","paper":"https://pith.science/paper/UT4LT2PD"},"agent_actions":{"view_html":"https://pith.science/pith/UT4LT2PDPSNEZXLPLOJDALCROE","download_json":"https://pith.science/pith/UT4LT2PDPSNEZXLPLOJDALCROE.json","view_paper":"https://pith.science/paper/UT4LT2PD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.06219&json=true","fetch_graph":"https://pith.science/api/pith-number/UT4LT2PDPSNEZXLPLOJDALCROE/graph.json","fetch_events":"https://pith.science/api/pith-number/UT4LT2PDPSNEZXLPLOJDALCROE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UT4LT2PDPSNEZXLPLOJDALCROE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UT4LT2PDPSNEZXLPLOJDALCROE/action/storage_attestation","attest_author":"https://pith.science/pith/UT4LT2PDPSNEZXLPLOJDALCROE/action/author_attestation","sign_citation":"https://pith.science/pith/UT4LT2PDPSNEZXLPLOJDALCROE/action/citation_signature","submit_replication":"https://pith.science/pith/UT4LT2PDPSNEZXLPLOJDALCROE/action/replication_record"}},"created_at":"2026-05-18T00:50:14.701485+00:00","updated_at":"2026-05-18T00:50:14.701485+00:00"}