Learning to Optimize

Ask about this topic

Overview

Learning to Optimize studies how data can improve the algorithms and decisions used to solve optimization problems, especially when similar problems recur or standard solvers are costly. Approaches include meta-learning and reinforcement learning for update rules and optimizer selection, unrolled or differentiable methods such as learned ADMM, and learned heuristics or warm starts for mixed-integer and other solvers. These methods are used in settings ranging from neural-network training and black-box search to scheduling, power systems, and model predictive control, with work increasingly combining fast learned predictions with exact optimization or explicit feasibility checks. A central tension is whether methods trained on particular instances can generalize reliably to new problems while preserving solution quality, constraint satisfaction, and the computational guarantees of classical solvers.

Latest papers

CardsList

139 papers27 papers in the last four weeks, up 125% on the four weeks before. 0.2% of all new papers.

  1. When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize

    May 7, 2026Yi Wang, Chandrajit BajajDynamical SystemsLearning to Optimize

    1Oden Institute · Department of Computer Science

  2. Distributionally-Robust Learning to Optimize

    May 7, 2026Vinit Ranjan, Jisun Park, Bartolomeo StellatoLearning to OptimizeDistributionally Robust Optimization

    Department of Operations Research and Financial Engineering, Princeton University. · Department of Operations Research and Financial Engineering, Princeton University, and Research Institute of Mathematics, Seoul National University. · Research Institute of Mathematics, Seoul National University.

  3. NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees

    Apr 30, 2026Bimol Nath Roy, Rahul Golder, MM Faruque HasanNeural Network OptimizationLearning to Optimize

    Texas A&M University

  4. Learning Over-Relaxation Policies for ADMM with Convergence Guarantees

    Apr 29, 2026Junan Lin, Paul J. Goulart, Luca FurieriLearning to OptimizeAlternating Direction Method of Multipliers

    Department of Engineering Science, University of Oxford, OX1 3PJ Oxford, UK

  5. Accelerating Regularized Attention Kernel Regression for Spectrum Cartography

    Apr 28, 2026Liping Tao, Chee Wei TanLearning to Optimize

    College of Computing and Data Science, Nanyang Technological University, Singapore 639798

  6. Transferable SCF-Acceleration through Solver-Aligned Initialization Learning

    Apr 23, 2026Eike S. Eberhard, Viktor Kotsev, Timm Güthle +1Quantum ChemistryDensity Functional Theory

    1Technical University of Munich (TUM) · 2Munich Data Science Institute (MDSI) · 3Munich Center for Machine Learning (MCML)

  7. Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization

    Apr 22, 2026Shan He, Runze Wang, Zhuoyun Du +4Multi-Agent System OptimizationLearning to Optimize

    Future Living Lab of Alibaba

  8. Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraints

    Apr 21, 2026Konstantinos Ziliaskopoulos, Alexander VinelPredict-Then-OptimizeLearning to Optimize

    Auburn University

  9. RELOAD: A Robust and Efficient Learned Query Optimizer for Database Systems

    Apr 16, 2026Seokwon Lee, Jaeyoung Sim, Sihyun Kim +3Learning to OptimizeDatabase Systems

    Yonsei University BDAI Lab, Seoul 03722, South Korea · Microsoft Gray Systems Lab, Redmond, USA

  10. Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach

    Apr 14, 2026Mohamed-Bachir BelaidConstrained OptimizationLearning to Optimize

    NILU, Climate and environmental research institute, Kjeller, Norway

  11. HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization

    Apr 14, 2026Trinh Tran, Binh Nguyen, Truong X. NghiemLearning to OptimizeAlternating Direction Method of Multipliers

    Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA

  12. Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

    Apr 13, 2026Saumya Goyal, Rohith Rongali, Ritabrata Ray +1Meta-LearningGradient Descent

    Machine Learning Department, Carnegie Mellon University · Electrical and Computer Engineering Department, Carnegie Mellon University.

  13. Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers

    Apr 13, 2026I. Esra BuyuktahtakinReinforcement LearningPredict-Then-Optimize

    Grado Department of Industrial and Systems Engineering, Virginia Tech Blacksburg, VA 24061, USA

  14. A Self-Attentive Meta-Optimizer with Group-Adaptive Learning Rates and Weight Decay

    Apr 10, 2026JiangBo Zhao, ZhaoXin LiuDeep Learning OptimizationLearning to Optimize

  15. One-Shot Localisation of the Global Minimum of a Noisy One-Dimensional Function: A Controlled Comparison of an Iterative Neural Minimizer, Set Transformers and Classical Estimators

    Apr 4, 2026Qusay Muzaffar, David Levin, Michael WermanBlack-Box OptimizationZeroth-Order Optimization

    Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel · Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel

  16. Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training

    Apr 4, 2026Adhiraj ChattopadhyayPortfolio OptimizationLearning to Optimize

    Department of Management Studies, Indian Institute of Technology Roorkee, India

  17. Sparse Bayesian Learning Algorithms Revisited: From Learning Majorizers to Structured Algorithmic Learning using Neural Networks

    Apr 2, 2026Rushabha Balaji, Kuan-Lin Chen, Danijela Cabric +1Sparse RecoveryLearning to Optimize

    Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA · Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA

  18. When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling

    Apr 2, 2026Andrea Mencaroni, Robbert Reijnen, Yingqian Zhang +1RL ControlLearning to Optimize

    Department of Industrial Systems Engineering and Product Design, Ghent University, Ghent, Belgium · Industrial Systems Engineering (ISyE), Flanders Make, Kortrijk, Belgium · Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, The Netherlands +1

  19. Learning to Model Optimization Problems from Solver Outcomes

    Apr 1, 2026Runda Guan, Xiangqing Shen, Jiajun Zhang +3Natural Language to Optimization ModelingLearning to Optimize

    School of Computer Science and Engineering, Nanjing University of Science and Technology · School of Intelligence Science and Technology, Nanjing University · Institute of Automation, Chinese Academy of Sciences

  20. Speeding Up Mixed-Integer Programming Solvers with Sparse Learning for Branching

    Mar 31, 2026Selin Bayramoğlu, George L Nemhauser, Nikolaos V SahinidisLearning to OptimizeBranch and Bound

    H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology · School of Chemical & Biomolecular Engineering, Georgia Institute of Technology

  21. End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers

    Mar 31, 2026Zhenyu Pu, Yu Yang, Liang Yu +1Data Center Energy ManagementLearning to Optimize

    School of Automation Science and Engineering, Xi’an Jiaotong University, Shaanxi, China · College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China · Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University, Beijing 100084, China +1

  22. Machine Learning-Assisted High-Dimensional Matrix Estimation

    Mar 30, 2026Wan Tian, Hui Yang, Zhouhui Lian +2Learning to OptimizeAlternating Direction Method of Multipliers

    Advanced Institute of Information Technology, Peking University · Wangxuan Institute of Computer Technology, Peking University, China, 100871 · Guanghua School of Management, Peking University, Beijing, China, 100871 +3

  23. A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization

    Mar 30, 2026Wenjie Qiu, Zixin Wang, Hongyu Fang +2Global OptimizationEvolutionary Optimization

    South China University of Technology, Guangzhou, Guangdong, China

  24. Inverse Neural Operator for ODE Parameter Optimization

    Mar 12, 2026Zhi-Song Liu, Wenqing Peng, Helmi Toropainen +5Parameter EstimationOrdinary Differential Equations

    Department of Computational Engineering, Lappeenranta-Lahti University of Technology, Lahti, Finland · Atmospheric Modelling Center Lahti (AMC-Lahti), Finland · University of Helsinki, Helsinki, Finland +3

  25. Learning to Score: Tuning Cluster Schedulers through Reinforcement Learning

    Mar 11, 2026Martin Asenov, Qiwen Deng, Gingfung Yeung +1Learning to OptimizeReinforcement Learning

    Edinburgh Research Centre, Central Software Institute, Huawei · School of Computer Science, University of St Andrews

  26. Learning to Solve Orienteering Problem with Time Windows and Variable Profits

    Mar 6, 2026Songqun Gao, Zanxi Ruan, Patrick Floor +3Learning to OptimizeCombinatorial Optimization

    Department of Industrial Engineering, Università di Trento, Trento, Italy · Interdepartmental Robotics Labs (IDRA), Trento, Italy · Department of Engineering for Innovation Medicine, Università di Verona, Verona, Italy +2

  27. Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels

    Mar 5, 2026Khai Nguyen, Petros Ellinas, Anvita Bhagavathula +1Learning to OptimizeNeural Surrogate Modeling

    Massachusetts Institute of Technology, Cambridge, MA, USA · Technical University of Denmark, Kongens Lyngby, Denmark

  28. Optimal trajectory-guided stochastic co-optimization for e-fuel system design and real-time operation

    Mar 3, 2026Jeongdong Kim, Minsu Kim, Jonggeol Na +1Learning to OptimizeStochastic Optimization

    Department of Chemical Engineering , Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, Massachusetts, United States. · Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seoul, 03722, Republic of Korea. · Department of Chemical Engineering and Materials Science, Ewha Womans University, 52 Ewhayeodae-gil, Seoul, 03760, Republic of Korea. +2

  29. Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data

    Feb 23, 2026Zhenyao Ma, Yue Liang, Dongxu LiInterpretable MLLearning to Optimize

    Xiamen University · University of Tübingen · Xi’an Jiaotong University

  30. Celo2: Towards Learned Optimization Free Lunch

    Feb 22, 2026Abhinav Moudgil, Boris Knyazev, Eugene BelilovskyLearning to OptimizeDeep Learning Optimization

    Mila · Concordia University · Samsung AI Lab, Montreal +1