{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:ZUECJB5LAVG4RJDBUQ52MJLWA3","short_pith_number":"pith:ZUECJB5L","schema_version":"1.0","canonical_sha256":"cd082487ab054dc8a461a43ba6257606eb4816c7e6037918d062e8522461c686","source":{"kind":"arxiv","id":"1406.5223","version":1},"attestation_state":"computed","paper":{"title":"Distributed, simple and stable network localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Claudia Soares, Joao Gomes, Joao Xavier","submitted_at":"2014-06-19T21:34:07Z","abstract_excerpt":"We propose a simple, stable and distributed algorithm which directly optimizes the nonconvex maximum likelihood criterion for sensor network localization, with no need to tune any free parameter. We reformulate the problem to obtain a gradient Lipschitz cost; by shifting to this cost function we enable a Majorization-Minimization (MM) approach based on quadratic upper bounds that decouple across nodes; the resulting algorithm happens to be distributed, with all nodes working in parallel. Our method inherits the MM stability: each communication cuts down the cost function. Numerical simulations"},"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":"1406.5223","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2014-06-19T21:34:07Z","cross_cats_sorted":[],"title_canon_sha256":"47348134e9fb4832df61c0a6bf5de7e97ed050f58f5aa4af05448c13bc461dcf","abstract_canon_sha256":"2256867f667a62da8b4e723ec72882a0c4320ae4779de1424fd36e94448d364b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:32:19.066618Z","signature_b64":"79D4CSP86M7Xh9OxFiyXUTL7Ybeuqkx/Qv11kQz0R5Yv5SVJGb9/2lbkW16PgowcrFKCCYKHE4TtEWhkJ09cAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd082487ab054dc8a461a43ba6257606eb4816c7e6037918d062e8522461c686","last_reissued_at":"2026-05-18T01:32:19.065991Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:32:19.065991Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed, simple and stable network localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Claudia Soares, Joao Gomes, Joao Xavier","submitted_at":"2014-06-19T21:34:07Z","abstract_excerpt":"We propose a simple, stable and distributed algorithm which directly optimizes the nonconvex maximum likelihood criterion for sensor network localization, with no need to tune any free parameter. We reformulate the problem to obtain a gradient Lipschitz cost; by shifting to this cost function we enable a Majorization-Minimization (MM) approach based on quadratic upper bounds that decouple across nodes; the resulting algorithm happens to be distributed, with all nodes working in parallel. Our method inherits the MM stability: each communication cuts down the cost function. Numerical simulations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.5223","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":"1406.5223","created_at":"2026-05-18T01:32:19.066079+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.5223v1","created_at":"2026-05-18T01:32:19.066079+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.5223","created_at":"2026-05-18T01:32:19.066079+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZUECJB5LAVG4","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZUECJB5LAVG4RJDB","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZUECJB5L","created_at":"2026-05-18T12:28:59.999130+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/ZUECJB5LAVG4RJDBUQ52MJLWA3","json":"https://pith.science/pith/ZUECJB5LAVG4RJDBUQ52MJLWA3.json","graph_json":"https://pith.science/api/pith-number/ZUECJB5LAVG4RJDBUQ52MJLWA3/graph.json","events_json":"https://pith.science/api/pith-number/ZUECJB5LAVG4RJDBUQ52MJLWA3/events.json","paper":"https://pith.science/paper/ZUECJB5L"},"agent_actions":{"view_html":"https://pith.science/pith/ZUECJB5LAVG4RJDBUQ52MJLWA3","download_json":"https://pith.science/pith/ZUECJB5LAVG4RJDBUQ52MJLWA3.json","view_paper":"https://pith.science/paper/ZUECJB5L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.5223&json=true","fetch_graph":"https://pith.science/api/pith-number/ZUECJB5LAVG4RJDBUQ52MJLWA3/graph.json","fetch_events":"https://pith.science/api/pith-number/ZUECJB5LAVG4RJDBUQ52MJLWA3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZUECJB5LAVG4RJDBUQ52MJLWA3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZUECJB5LAVG4RJDBUQ52MJLWA3/action/storage_attestation","attest_author":"https://pith.science/pith/ZUECJB5LAVG4RJDBUQ52MJLWA3/action/author_attestation","sign_citation":"https://pith.science/pith/ZUECJB5LAVG4RJDBUQ52MJLWA3/action/citation_signature","submit_replication":"https://pith.science/pith/ZUECJB5LAVG4RJDBUQ52MJLWA3/action/replication_record"}},"created_at":"2026-05-18T01:32:19.066079+00:00","updated_at":"2026-05-18T01:32:19.066079+00:00"}