{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:JCERLNCIRMQPGMI7BKJ757XUOI","short_pith_number":"pith:JCERLNCI","canonical_record":{"source":{"id":"1502.07974","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2015-02-27T17:08:18Z","cross_cats_sorted":[],"title_canon_sha256":"eaa7bde81ed9472024682cbee7646e15119f7ddab2d45f5fcc3bbc9dd577156d","abstract_canon_sha256":"3c52a7b2f7dddfcbdce0c8e89a79853d6b1a5f1dcc16c20d50a2bbffa41beffb"},"schema_version":"1.0"},"canonical_sha256":"488915b4488b20f3311f0a93fefef4722cd09740c9264a028fca814ee861744e","source":{"kind":"arxiv","id":"1502.07974","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.07974","created_at":"2026-05-18T02:26:01Z"},{"alias_kind":"arxiv_version","alias_value":"1502.07974v1","created_at":"2026-05-18T02:26:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.07974","created_at":"2026-05-18T02:26:01Z"},{"alias_kind":"pith_short_12","alias_value":"JCERLNCIRMQP","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"JCERLNCIRMQPGMI7","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"JCERLNCI","created_at":"2026-05-18T12:29:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:JCERLNCIRMQPGMI7BKJ757XUOI","target":"record","payload":{"canonical_record":{"source":{"id":"1502.07974","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2015-02-27T17:08:18Z","cross_cats_sorted":[],"title_canon_sha256":"eaa7bde81ed9472024682cbee7646e15119f7ddab2d45f5fcc3bbc9dd577156d","abstract_canon_sha256":"3c52a7b2f7dddfcbdce0c8e89a79853d6b1a5f1dcc16c20d50a2bbffa41beffb"},"schema_version":"1.0"},"canonical_sha256":"488915b4488b20f3311f0a93fefef4722cd09740c9264a028fca814ee861744e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:26:01.718843Z","signature_b64":"U1R7GzQh6SOg5LYdw0w41dFuQ4MG2legMnADSdm84k/6ZBZLNJ2YdckqIy/JitpzQGJiC9He7XdT7ETexNlDCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"488915b4488b20f3311f0a93fefef4722cd09740c9264a028fca814ee861744e","last_reissued_at":"2026-05-18T02:26:01.718361Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:26:01.718361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.07974","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":"2026-05-18T02:26:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L/VDJsgnXIdFL7VL1vTc+CIkG2uGL2j8wNSNWPEZw0u3+ZUV/keYP1ZxxaM1unR2OYqTGKxgLi+eqoLs2xMzBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T10:35:33.076638Z"},"content_sha256":"507a10d1e7e1dd064152a41f4477fc3c0b61fb03e560dba43c57f23f3fab228a","schema_version":"1.0","event_id":"sha256:507a10d1e7e1dd064152a41f4477fc3c0b61fb03e560dba43c57f23f3fab228a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:JCERLNCIRMQPGMI7BKJ757XUOI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Convex Feasibility Approach to Anytime Model Predictive Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Alberto Bemporad, Daniele Bernardini, Panagiotis Patrinos","submitted_at":"2015-02-27T17:08:18Z","abstract_excerpt":"This paper proposes to decouple performance optimization and enforcement of asymptotic convergence in Model Predictive Control (MPC) so that convergence to a given terminal set is achieved independently of how much performance is optimized at each sampling step. By embedding an explicit decreasing condition in the MPC constraints and thanks to a novel and very easy-to-implement convex feasibility solver proposed in the paper, it is possible to run an outer performance optimization algorithm on top of the feasibility solver and optimize for an amount of time that depends on the available CPU re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.07974","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":"2026-05-18T02:26:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NdOJzthDrOlXnf2gSdWAZEJhyO7f6C2mxF9ciN5aMVWshiqmPxWnXHWhRWW2p3xPgqQuHX67o9Q9nkPNyQRnDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T10:35:33.076981Z"},"content_sha256":"ebeeb2919984a45ec410996e802e901ff250cbe4d8b7318e8110090216ad2522","schema_version":"1.0","event_id":"sha256:ebeeb2919984a45ec410996e802e901ff250cbe4d8b7318e8110090216ad2522"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JCERLNCIRMQPGMI7BKJ757XUOI/bundle.json","state_url":"https://pith.science/pith/JCERLNCIRMQPGMI7BKJ757XUOI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JCERLNCIRMQPGMI7BKJ757XUOI/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":"2026-06-11T10:35:33Z","links":{"resolver":"https://pith.science/pith/JCERLNCIRMQPGMI7BKJ757XUOI","bundle":"https://pith.science/pith/JCERLNCIRMQPGMI7BKJ757XUOI/bundle.json","state":"https://pith.science/pith/JCERLNCIRMQPGMI7BKJ757XUOI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JCERLNCIRMQPGMI7BKJ757XUOI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:JCERLNCIRMQPGMI7BKJ757XUOI","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":"3c52a7b2f7dddfcbdce0c8e89a79853d6b1a5f1dcc16c20d50a2bbffa41beffb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2015-02-27T17:08:18Z","title_canon_sha256":"eaa7bde81ed9472024682cbee7646e15119f7ddab2d45f5fcc3bbc9dd577156d"},"schema_version":"1.0","source":{"id":"1502.07974","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.07974","created_at":"2026-05-18T02:26:01Z"},{"alias_kind":"arxiv_version","alias_value":"1502.07974v1","created_at":"2026-05-18T02:26:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.07974","created_at":"2026-05-18T02:26:01Z"},{"alias_kind":"pith_short_12","alias_value":"JCERLNCIRMQP","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"JCERLNCIRMQPGMI7","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"JCERLNCI","created_at":"2026-05-18T12:29:27Z"}],"graph_snapshots":[{"event_id":"sha256:ebeeb2919984a45ec410996e802e901ff250cbe4d8b7318e8110090216ad2522","target":"graph","created_at":"2026-05-18T02:26:01Z","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":"This paper proposes to decouple performance optimization and enforcement of asymptotic convergence in Model Predictive Control (MPC) so that convergence to a given terminal set is achieved independently of how much performance is optimized at each sampling step. By embedding an explicit decreasing condition in the MPC constraints and thanks to a novel and very easy-to-implement convex feasibility solver proposed in the paper, it is possible to run an outer performance optimization algorithm on top of the feasibility solver and optimize for an amount of time that depends on the available CPU re","authors_text":"Alberto Bemporad, Daniele Bernardini, Panagiotis Patrinos","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2015-02-27T17:08:18Z","title":"A Convex Feasibility Approach to Anytime Model Predictive Control"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.07974","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":"sha256:507a10d1e7e1dd064152a41f4477fc3c0b61fb03e560dba43c57f23f3fab228a","target":"record","created_at":"2026-05-18T02:26:01Z","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":"3c52a7b2f7dddfcbdce0c8e89a79853d6b1a5f1dcc16c20d50a2bbffa41beffb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2015-02-27T17:08:18Z","title_canon_sha256":"eaa7bde81ed9472024682cbee7646e15119f7ddab2d45f5fcc3bbc9dd577156d"},"schema_version":"1.0","source":{"id":"1502.07974","kind":"arxiv","version":1}},"canonical_sha256":"488915b4488b20f3311f0a93fefef4722cd09740c9264a028fca814ee861744e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"488915b4488b20f3311f0a93fefef4722cd09740c9264a028fca814ee861744e","first_computed_at":"2026-05-18T02:26:01.718361Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:26:01.718361Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U1R7GzQh6SOg5LYdw0w41dFuQ4MG2legMnADSdm84k/6ZBZLNJ2YdckqIy/JitpzQGJiC9He7XdT7ETexNlDCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:26:01.718843Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.07974","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:507a10d1e7e1dd064152a41f4477fc3c0b61fb03e560dba43c57f23f3fab228a","sha256:ebeeb2919984a45ec410996e802e901ff250cbe4d8b7318e8110090216ad2522"],"state_sha256":"87411e2c1af4a7bfe3ffa4b13a8b8b26b7041b8071d41b4a0bb0456d157c786d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CQ5l2DhmJjVUhXJI/tGWZvTMpwqZ5aO4xowUsfy4RL8r1CrWkwJ7/8htBcZJoOjPiCBpmhsxSxhJSTL5aTBiBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T10:35:33.079325Z","bundle_sha256":"a94673bae5f4be1f25f5fedac28fb6c90bc2d7dfb7fb27534daa967175fa9931"}}