{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:YDTMJH64EIA6PZ3MBI2F4BFIWO","short_pith_number":"pith:YDTMJH64","canonical_record":{"source":{"id":"1908.07247","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-20T09:38:43Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"083ea7ee984d83cb18074f45dd949b2b1a26c17da9e3dd8b1963fade052efca1","abstract_canon_sha256":"ea845904fe4e2a752a7e7c92b71ac01585e6b63b22a9f04fedc86f1a451b097b"},"schema_version":"1.0"},"canonical_sha256":"c0e6c49fdc2201e7e76c0a345e04a8b3820c5dddcc3cc147e306389aaddc0ee3","source":{"kind":"arxiv","id":"1908.07247","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.07247","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"arxiv_version","alias_value":"1908.07247v2","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07247","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"pith_short_12","alias_value":"YDTMJH64EIA6","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"pith_short_16","alias_value":"YDTMJH64EIA6PZ3M","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"pith_short_8","alias_value":"YDTMJH64","created_at":"2026-07-05T02:25:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:YDTMJH64EIA6PZ3MBI2F4BFIWO","target":"record","payload":{"canonical_record":{"source":{"id":"1908.07247","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-20T09:38:43Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"083ea7ee984d83cb18074f45dd949b2b1a26c17da9e3dd8b1963fade052efca1","abstract_canon_sha256":"ea845904fe4e2a752a7e7c92b71ac01585e6b63b22a9f04fedc86f1a451b097b"},"schema_version":"1.0"},"canonical_sha256":"c0e6c49fdc2201e7e76c0a345e04a8b3820c5dddcc3cc147e306389aaddc0ee3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:25:58.858216Z","signature_b64":"Ak59KeXOmgfTyQLTrHJiKzT42pVzhI9LC5ir3ghCxFEfQGGq7HSWogADshLZfTxw7IxWX5gSsOeiMhfmUazNBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0e6c49fdc2201e7e76c0a345e04a8b3820c5dddcc3cc147e306389aaddc0ee3","last_reissued_at":"2026-07-05T02:25:58.857843Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:25:58.857843Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.07247","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":"2026-07-05T02:25:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6cOpHgww9eqHnXLaz5ydSciBRai0zreul+CDMZrBBzFtZlHFWO9KFF2+FPVqG7LoDrhnOCV4f7OOVOd4n6ovDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T07:57:57.389049Z"},"content_sha256":"789f91cfd9b675306541e1006110929239987d08f32681900bc31dbb91612028","schema_version":"1.0","event_id":"sha256:789f91cfd9b675306541e1006110929239987d08f32681900bc31dbb91612028"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:YDTMJH64EIA6PZ3MBI2F4BFIWO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An efficient bounded-variable nonlinear least-squares algorithm for embedded MPC","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"math.OC","authors_text":"Alberto Bemporad, Nilay Saraf","submitted_at":"2019-08-20T09:38:43Z","abstract_excerpt":"This paper presents a new approach to solve linear and nonlinear model predictive control (MPC) problems that requires small memory footprint and throughput and is particularly suitable when the model and/or controller parameters change at runtime. Typically MPC requires two phases: 1) construct an optimization problem based on the given MPC parameters (prediction model, tuning weights, prediction horizon, and constraints), which results in a quadratic or nonlinear programming problem, and then 2) call an optimization algorithm to solve the resulting problem. In the proposed approach the probl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07247","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.07247/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T02:25:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OO8n7Ej5Pij1GLyJ/TnEscjkUAnvbn8WQ21yxqrMPCp1uunmmZylTpG/brPclBjUWGLWVlcI+Ykm4LgWCagRDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T07:57:57.389668Z"},"content_sha256":"f595cf6c0a0e81a4ed216913a105ac65aa9fea696357dab2d708b596a8b18245","schema_version":"1.0","event_id":"sha256:f595cf6c0a0e81a4ed216913a105ac65aa9fea696357dab2d708b596a8b18245"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YDTMJH64EIA6PZ3MBI2F4BFIWO/bundle.json","state_url":"https://pith.science/pith/YDTMJH64EIA6PZ3MBI2F4BFIWO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YDTMJH64EIA6PZ3MBI2F4BFIWO/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":"2026-08-17T07:57:57Z","links":{"resolver":"https://pith.science/pith/YDTMJH64EIA6PZ3MBI2F4BFIWO","bundle":"https://pith.science/pith/YDTMJH64EIA6PZ3MBI2F4BFIWO/bundle.json","state":"https://pith.science/pith/YDTMJH64EIA6PZ3MBI2F4BFIWO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YDTMJH64EIA6PZ3MBI2F4BFIWO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:YDTMJH64EIA6PZ3MBI2F4BFIWO","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":"ea845904fe4e2a752a7e7c92b71ac01585e6b63b22a9f04fedc86f1a451b097b","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-20T09:38:43Z","title_canon_sha256":"083ea7ee984d83cb18074f45dd949b2b1a26c17da9e3dd8b1963fade052efca1"},"schema_version":"1.0","source":{"id":"1908.07247","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.07247","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"arxiv_version","alias_value":"1908.07247v2","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07247","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"pith_short_12","alias_value":"YDTMJH64EIA6","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"pith_short_16","alias_value":"YDTMJH64EIA6PZ3M","created_at":"2026-07-05T02:25:58Z"},{"alias_kind":"pith_short_8","alias_value":"YDTMJH64","created_at":"2026-07-05T02:25:58Z"}],"graph_snapshots":[{"event_id":"sha256:f595cf6c0a0e81a4ed216913a105ac65aa9fea696357dab2d708b596a8b18245","target":"graph","created_at":"2026-07-05T02:25:58Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.07247/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a new approach to solve linear and nonlinear model predictive control (MPC) problems that requires small memory footprint and throughput and is particularly suitable when the model and/or controller parameters change at runtime. Typically MPC requires two phases: 1) construct an optimization problem based on the given MPC parameters (prediction model, tuning weights, prediction horizon, and constraints), which results in a quadratic or nonlinear programming problem, and then 2) call an optimization algorithm to solve the resulting problem. In the proposed approach the probl","authors_text":"Alberto Bemporad, Nilay Saraf","cross_cats":["cs.SY","eess.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-20T09:38:43Z","title":"An efficient bounded-variable nonlinear least-squares algorithm for embedded MPC"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07247","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":"sha256:789f91cfd9b675306541e1006110929239987d08f32681900bc31dbb91612028","target":"record","created_at":"2026-07-05T02:25:58Z","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":"ea845904fe4e2a752a7e7c92b71ac01585e6b63b22a9f04fedc86f1a451b097b","cross_cats_sorted":["cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-08-20T09:38:43Z","title_canon_sha256":"083ea7ee984d83cb18074f45dd949b2b1a26c17da9e3dd8b1963fade052efca1"},"schema_version":"1.0","source":{"id":"1908.07247","kind":"arxiv","version":2}},"canonical_sha256":"c0e6c49fdc2201e7e76c0a345e04a8b3820c5dddcc3cc147e306389aaddc0ee3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c0e6c49fdc2201e7e76c0a345e04a8b3820c5dddcc3cc147e306389aaddc0ee3","first_computed_at":"2026-07-05T02:25:58.857843Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:25:58.857843Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ak59KeXOmgfTyQLTrHJiKzT42pVzhI9LC5ir3ghCxFEfQGGq7HSWogADshLZfTxw7IxWX5gSsOeiMhfmUazNBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:25:58.858216Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.07247","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:789f91cfd9b675306541e1006110929239987d08f32681900bc31dbb91612028","sha256:f595cf6c0a0e81a4ed216913a105ac65aa9fea696357dab2d708b596a8b18245"],"state_sha256":"f6854d8b58c5c1159447b1963fe5de6f70b7713013aeaec8400537a2900fc874"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SnNTGSVusIVEcTEwvbO2QKol9r/zE5kVVZ/ykcThljd/R8ppCiExz0a9QCgt99GESd6t5C3POVr0UAPKLvQQCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T07:57:57.394540Z","bundle_sha256":"1e0e38f6c23e624e96e4cf863e6ccfa51b6dd939c83f3bac2d5682ab82f586a9"}}