A constrained optimizer cannot react to every prediction error. PEAR keeps the directions that can move the decision, making it most natural when system dynamics and constraints stay fixed while objective coefficients change across instances.제약 최적화기는 모든 예측 오차에 반응하지 않는다. PEAR는 의사결정을 움직일 수 있는 오차만 남기며, 시스템의 동역학과 제약은 같고 목적함수 계수만 달라지는 문제에 특히 잘 맞는다.
Decision-Focused Learning via Tangent-Space Projection of Prediction ErrorDecision-Focused Learning via Tangent-Space Projection of Prediction Error
A critical note on learning interpretable objective weights from expert production plans while preserving a known industrial MILP.알려진 산업 MILP를 유지하면서 전문가 생산계획으로부터 해석 가능한 목적함수 가중치를 학습하는 inverse optimization에 대한 비판적 정리.
Uncovering expert objectives in production planning via inverse optimization: An industrial case studyUncovering expert objectives in production planning via inverse optimization: An industrial case study
A note on ORACLE, which turns near-optimal energy-system exploration from point generation into an inner/outer approximation problem with a certified distance metric.near-optimal energy-system exploration을 단순한 point generation이 아니라 inner/outer approximation과 certified distance metric의 문제로 바꾸는 ORACLE에 대한 정리.
ORACLE: A rigorous metric and method to explore all near-optimal designs for energy systemsORACLE: A rigorous metric and method to explore all near-optimal designs for energy systems
A critical note on MadNCL, which combines Algorithm NCL, MadNLP, and GPU-friendly KKT reformulations to improve robustness on large-scale degenerate nonlinear programs.Algorithm NCL, MadNLP, GPU 친화적 KKT 재구성을 결합해 대규모 퇴화 비선형계획에서 강건성을 높이려는 MadNCL 논문에 대한 비판적 정리.
MADNCL: a GPU implementation of algorithm NCL for large-scale, degenerate nonlinear programsMADNCL: a GPU implementation of algorithm NCL for large-scale, degenerate nonlinear programs
A critical note on NLPOpt-Net, which learns a parametric NLP solution map with a neural warm start and an objective-aware differentiable projection layer.NLPOpt-Net을 신경망 warm start와 목적함수 인식 differentiable projection layer가 결합된 파라메트릭 NLP solution-map 학습법으로 정리한 비판적 노트.
NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility GuaranteesNLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees
A critical note on learned Lyapunov terminal costs for NMPC, focusing on Cholesky-structured positive-definite surrogates, horizon compression, and the unresolved role of approximation error.NMPC의 학습 기반 Lyapunov terminal cost를 비판적으로 읽는 글로, Cholesky 구조의 양의 정부호 surrogate, horizon compression, 그리고 approximation error의 미해결 역할을 중심으로 다룬다.
Learning Lyapunov terminal costs from data for complexity reduction in nonlinear model predictive controlLearning Lyapunov terminal costs from data for complexity reduction in nonlinear model predictive control