{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CDOGN3FAU6KYDXI7H44LUUX3WS","short_pith_number":"pith:CDOGN3FA","schema_version":"1.0","canonical_sha256":"10dc66eca0a79581dd1f3f38ba52fbb49e88bd9e38b68e1a2fa54c21d157b926","source":{"kind":"arxiv","id":"2511.19383","version":2},"attestation_state":"computed","paper":{"title":"A Hybrid Learning-to-Optimize Framework for Mixed-Integer Quadratic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A neural network predicts integer variables in parametric mixed-integer quadratic programs while a differentiable QP layer solves for the continuous part using a hybrid loss.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Mu Xie, Rahul Mangharam, Viet-Anh Le","submitted_at":"2025-11-24T18:22:00Z","abstract_excerpt":"In this paper, we propose a learning-to-optimize (L2O) framework to accelerate solving parametric mixed-integer quadratic programming (MIQP) problems, with a particular focus on mixed-integer model predictive control (MI-MPC) applications. The framework learns to predict integer solutions with enhanced optimality and feasibility by integrating supervised learning (for optimality), self-supervised learning (for feasibility), and a differentiable quadratic programming (QP) layer, resulting in a hybrid L2O framework. Specifically, a neural network (NN) is used to learn the mapping from problem pa"},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2511.19383","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2025-11-24T18:22:00Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"ca5c12e5ff3b1d9773cc861a2364a28bcb2b628b7bf860e5b558a6d9499aba05","abstract_canon_sha256":"8c3f644383d1262fce22e7a864ff7d9853003d206eff4ef944e5bc2d6cae0a30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:32.357801Z","signature_b64":"x1Q4KwrmGkk/exgfNZt46A3CgjQK9ZTXUe4DKORdpLOwKDLB286ZUnP2RmnuNomCmA9cgkrxWCuB/uViWa2zDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10dc66eca0a79581dd1f3f38ba52fbb49e88bd9e38b68e1a2fa54c21d157b926","last_reissued_at":"2026-05-18T02:44:32.357173Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:32.357173Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Hybrid Learning-to-Optimize Framework for Mixed-Integer Quadratic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A neural network predicts integer variables in parametric mixed-integer quadratic programs while a differentiable QP layer solves for the continuous part using a hybrid loss.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Mu Xie, Rahul Mangharam, Viet-Anh Le","submitted_at":"2025-11-24T18:22:00Z","abstract_excerpt":"In this paper, we propose a learning-to-optimize (L2O) framework to accelerate solving parametric mixed-integer quadratic programming (MIQP) problems, with a particular focus on mixed-integer model predictive control (MI-MPC) applications. The framework learns to predict integer solutions with enhanced optimality and feasibility by integrating supervised learning (for optimality), self-supervised learning (for feasibility), and a differentiable quadratic programming (QP) layer, resulting in a hybrid L2O framework. Specifically, a neural network (NN) is used to learn the mapping from problem pa"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"a neural network (NN) is used to learn the mapping from problem parameters to optimal integer solutions, while a differentiable QP layer is integrated to compute the corresponding continuous variables given the predicted integers and problem parameters. Moreover, a hybrid loss function is proposed, which combines a supervised loss with respect to the global optimal solution, and a self-supervised loss derived from the problem's objective and constraints.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a neural network trained on a finite set of problem instances will produce integer predictions whose corresponding QP solutions remain near-optimal and feasible for unseen parameter values encountered at runtime.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A hybrid L2O framework predicts optimal integer solutions for MIQP via neural network, recovers continuous variables with a differentiable QP layer, and trains with supervised optimality loss plus self-supervised feasibility loss.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A neural network predicts integer variables in parametric mixed-integer quadratic programs while a differentiable QP layer solves for the continuous part using a hybrid loss.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"af752e1ae3388bd0eb5019d0306dd988e1c009573628d2d1712e831e017b9d50"},"source":{"id":"2511.19383","kind":"arxiv","version":2},"verdict":{"id":"c290aaae-dc86-4637-aec0-d4549c7582ba","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T05:03:37.746958Z","strongest_claim":"a neural network (NN) is used to learn the mapping from problem parameters to optimal integer solutions, while a differentiable QP layer is integrated to compute the corresponding continuous variables given the predicted integers and problem parameters. Moreover, a hybrid loss function is proposed, which combines a supervised loss with respect to the global optimal solution, and a self-supervised loss derived from the problem's objective and constraints.","one_line_summary":"A hybrid L2O framework predicts optimal integer solutions for MIQP via neural network, recovers continuous variables with a differentiable QP layer, and trains with supervised optimality loss plus self-supervised feasibility loss.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a neural network trained on a finite set of problem instances will produce integer predictions whose corresponding QP solutions remain near-optimal and feasible for unseen parameter values encountered at runtime.","pith_extraction_headline":"A neural network predicts integer variables in parametric mixed-integer quadratic programs while a differentiable QP layer solves for the continuous part using a hybrid loss."},"references":{"count":15,"sample":[{"doi":"","year":2019,"title":"Differentiable convex optimization layers","work_id":"ca510b7c-a0b6-40ca-9fa7-cac50c47d76a","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Optnet: Differentiable optimization as a layer in neural networks","work_id":"fce385d6-ab8c-44d5-88d5-f8d8298677d5","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Formal methods for control synthesis: An optimization perspective","work_id":"2e5e1d70-978b-4503-95b9-c8e94a3d8382","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2013,"title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","work_id":"1fe8c7c8-aff7-4b94-9096-e549d7e60789","ref_index":4,"cited_arxiv_id":"1308.3432","is_internal_anchor":true},{"doi":"","year":2014,"title":"Constrained optimization and Lagrange multiplier methods","work_id":"f81857a8-6585-4fe3-bac3-fa9dc42f93d2","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":15,"snapshot_sha256":"069a3f05a0686efd4cb470aea340100084470e0206fbdf1af439df431966f126","internal_anchors":1},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6f5ea609531c75bbd7d7b336d538fcc08504a4699bd96ba4aab66e9d3ab49188"},"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":"2511.19383","created_at":"2026-05-18T02:44:32.357264+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.19383v2","created_at":"2026-05-18T02:44:32.357264+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.19383","created_at":"2026-05-18T02:44:32.357264+00:00"},{"alias_kind":"pith_short_12","alias_value":"CDOGN3FAU6KY","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"CDOGN3FAU6KYDXI7","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"CDOGN3FA","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS","json":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS.json","graph_json":"https://pith.science/api/pith-number/CDOGN3FAU6KYDXI7H44LUUX3WS/graph.json","events_json":"https://pith.science/api/pith-number/CDOGN3FAU6KYDXI7H44LUUX3WS/events.json","paper":"https://pith.science/paper/CDOGN3FA"},"agent_actions":{"view_html":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS","download_json":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS.json","view_paper":"https://pith.science/paper/CDOGN3FA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.19383&json=true","fetch_graph":"https://pith.science/api/pith-number/CDOGN3FAU6KYDXI7H44LUUX3WS/graph.json","fetch_events":"https://pith.science/api/pith-number/CDOGN3FAU6KYDXI7H44LUUX3WS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS/action/storage_attestation","attest_author":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS/action/author_attestation","sign_citation":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS/action/citation_signature","submit_replication":"https://pith.science/pith/CDOGN3FAU6KYDXI7H44LUUX3WS/action/replication_record"}},"created_at":"2026-05-18T02:44:32.357264+00:00","updated_at":"2026-05-18T02:44:32.357264+00:00"}