{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:FSVGJJLOASB3BICDOEAF3I6WKH","short_pith_number":"pith:FSVGJJLO","schema_version":"1.0","canonical_sha256":"2caa64a56e0483b0a04371005da3d651ce8b3543a660d378663ff7be0edcc8ff","source":{"kind":"arxiv","id":"1506.03371","version":1},"attestation_state":"computed","paper":{"title":"Upper bounds for the reach-avoid probability via robust optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"math.OC","authors_text":"John Lygeros, Maryam Kamgarpour, Nikolaos Kariotoglou, Tyler H. Summers","submitted_at":"2015-06-10T15:59:23Z","abstract_excerpt":"We consider finite horizon reach-avoid problems for discrete time stochastic systems. Our goal is to construct upper bound functions for the reach-avoid probability by means of tractable convex optimization problems. We achieve this by restricting attention to the span of Gaussian radial basis functions and imposing structural assumptions on the transition kernel of the stochastic processes as well as the target and safe sets of the reach-avoid problem. In particular, we require the kernel to be written as a Gaussian mixture density with each mean of the distribution being affine in the curren"},"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":"1506.03371","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2015-06-10T15:59:23Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"dc358eca13b3eda9c1ff726e4d3f46882cf974707be86c0ae79745d1ccab8e1f","abstract_canon_sha256":"5125f32a2abebc23a7143fc04c68136041fc182d7daf4a93b60d55758e869cf4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:55:24.891294Z","signature_b64":"QZYVSiRluar/3QaWhFN5YYg1jdpX5E1k2z/H4xXlh3oKIAt4RaTdnGTksjhSC8que1im9AGN67/2uD69ZrMQAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2caa64a56e0483b0a04371005da3d651ce8b3543a660d378663ff7be0edcc8ff","last_reissued_at":"2026-05-18T01:55:24.890680Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:55:24.890680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Upper bounds for the reach-avoid probability via robust optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"math.OC","authors_text":"John Lygeros, Maryam Kamgarpour, Nikolaos Kariotoglou, Tyler H. Summers","submitted_at":"2015-06-10T15:59:23Z","abstract_excerpt":"We consider finite horizon reach-avoid problems for discrete time stochastic systems. Our goal is to construct upper bound functions for the reach-avoid probability by means of tractable convex optimization problems. We achieve this by restricting attention to the span of Gaussian radial basis functions and imposing structural assumptions on the transition kernel of the stochastic processes as well as the target and safe sets of the reach-avoid problem. In particular, we require the kernel to be written as a Gaussian mixture density with each mean of the distribution being affine in the curren"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.03371","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":"1506.03371","created_at":"2026-05-18T01:55:24.890782+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.03371v1","created_at":"2026-05-18T01:55:24.890782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.03371","created_at":"2026-05-18T01:55:24.890782+00:00"},{"alias_kind":"pith_short_12","alias_value":"FSVGJJLOASB3","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"FSVGJJLOASB3BICD","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"FSVGJJLO","created_at":"2026-05-18T12:29:22.688609+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/FSVGJJLOASB3BICDOEAF3I6WKH","json":"https://pith.science/pith/FSVGJJLOASB3BICDOEAF3I6WKH.json","graph_json":"https://pith.science/api/pith-number/FSVGJJLOASB3BICDOEAF3I6WKH/graph.json","events_json":"https://pith.science/api/pith-number/FSVGJJLOASB3BICDOEAF3I6WKH/events.json","paper":"https://pith.science/paper/FSVGJJLO"},"agent_actions":{"view_html":"https://pith.science/pith/FSVGJJLOASB3BICDOEAF3I6WKH","download_json":"https://pith.science/pith/FSVGJJLOASB3BICDOEAF3I6WKH.json","view_paper":"https://pith.science/paper/FSVGJJLO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.03371&json=true","fetch_graph":"https://pith.science/api/pith-number/FSVGJJLOASB3BICDOEAF3I6WKH/graph.json","fetch_events":"https://pith.science/api/pith-number/FSVGJJLOASB3BICDOEAF3I6WKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FSVGJJLOASB3BICDOEAF3I6WKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FSVGJJLOASB3BICDOEAF3I6WKH/action/storage_attestation","attest_author":"https://pith.science/pith/FSVGJJLOASB3BICDOEAF3I6WKH/action/author_attestation","sign_citation":"https://pith.science/pith/FSVGJJLOASB3BICDOEAF3I6WKH/action/citation_signature","submit_replication":"https://pith.science/pith/FSVGJJLOASB3BICDOEAF3I6WKH/action/replication_record"}},"created_at":"2026-05-18T01:55:24.890782+00:00","updated_at":"2026-05-18T01:55:24.890782+00:00"}