Learning to Optimize

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A Solver-Free Training Method for Predict-then-Optimize
Jun 17, 2026Beichen Wan, Mo LiuPredict-Then-Optimize Learning to Optimize
Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA.
Learning Splitting Heuristics for Parallel String Solvers
Jun 9, 2026Chenhao Gao, Peisen YaoAutomated Algorithm Discovery Automated Heuristic Design
The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou, China
LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization
Jun 8, 2026Binh Nguyen, Trinh Tran, Truong X. NghiemLearning to Optimize Alternating Direction Method of Multipliers
Department of Electrical and Computer Engineering, College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
Jun 7, 2026Stéphane Eilles-Chan Way, Hugo Percot, Quentin Cappart +2Predict-Then-Optimize Learning to Optimize
Polytechnique Montréal, Montreal, Canada · Ecole Polytechnique, Palaiseau, France · UCLouvain, Louvain-la-Neuve, Belgium +2
Amortized Neural Optimization for Pre-Layout Signal Integrity Design Space Exploration using Differentiable Surrogates
Jun 5, 2026Julian Withöft, Werner John, Emre Ecik +2Surrogate-Assisted Optimization Electronic Design Automation
Multi-ResNets for Subspace Preconditioning in Constrained Optimization
Jun 4, 2026Merve Karakas, Christopher J. Williams, Emmanuel O. Balogun +3Residual Learning Learning to Optimize
UCLA · University of Oxford · Sunrun† +1
Amortized Nonlinear Model Predictive Control
Jun 4, 2026Francesco Pillitteri, Alberto BemporadRobotic Control Nonlinear MPC
IMT School for Advanced Studies, Lucca, Italy
Learning Admissible Heuristics via Cost Partitioning
Jun 3, 2026Hugo Barral, Quentin Cappart, Marie-José Huguet +1Automated Heuristic Design Learning to Optimize
LAAS-CNRS, Universit´e de Toulouse, CNRS, INSA, Toulouse, France · Polytechnique Montr´eal, Montr´eal, Canada · UCLouvain, Louvain-la-Neuve, Belgium +1
Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA
May 30, 2026Xiao Jin, Yongxiong Wang, Haobo Liu +2Surrogate Modeling Surrogate-Assisted Optimization
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, 200093, Shanghai, China · Xinxiang University, 453003, Xinxiang, China · College of System Engineering, National University of Defense Technology, 410073, Changsha, China
GLENS: Global Search via Learning from Solver Iterates with Diffusion Models
May 29, 2026Anjian Li, Bartolomeo Stellato, Ryne BeesonLearning to Optimize Diffusion Sampling
Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA. · Department of Operations Research and Financial Engineering, Princeton University, Princeton, 08544, NJ, USA. · Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, 08544, NJ, USA.
KLineage: Recovering the Missing When of Kernel Optimization by Deoptimizing Experts
May 27, 2026Shuoming Zhang, Qiuchu Yu, Ruiyuan Xu +11GPU Kernel Optimization GPU Acceleration
SKLP, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · University of New South Wales
PILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training
May 23, 2026Sattam Altuuaim, Lama Ayash, Muhammad Mubashar +1Deep Learning Optimization Learning to Optimize
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia · University of Strathclyde, Glasgow, Scotland
An Open-Source Training Dataset for Foundation Models for Black-box Optimization
May 22, 2026Aaron Klein, Herilalaina Rakotoarison, Luca Thale-Bombien +1Black-Box Optimization Learning to Optimize
University of Helsinki · ELLIS Institute Tübingen, Prior Labs
SO-Mamba: State-Ownership Mamba for Unrolled MRI Reconstruction
May 21, 2026Pengcheng Fang, Hongli Chen, Fangfang Tang +3Mamba Learning to Optimize
University of Southampton · University of Queensland · Soochow University
Learning fMRI activations dictionaries across individual geometries via optimal transport
May 20, 2026Sonia Mazelet, Rémi Flamary, Bertrand ThirionLearning to Optimize Geometric Representation Learning
CMAP, Ecole Polytechnique · Palaiseau, France · Mind, Inria-Saclay
AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splatting
May 20, 2026Zhaojie Zeng, Yuesong Wang, Yawei Luo +1Learning to Optimize Image Reconstruction
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China · School of Software Technology, Zhejiang University, Hangzhou, Zhejiang
Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
May 18, 2026Tung Quoc Le, Anh Tuan Nguyen, Viet Anh NguyenMixed-Integer Linear Programming Learning to Optimize
Université Grenoble Alpes, LJK, CNRS, Grenoble INP, 38000 Grenoble, France · Carnegie Mellon University, Machine Learning Department · Chinese University of Hong Kong, Department of Systems Engineering and Engineering Management.
Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics
May 18, 2026Mahdi Naser-MoghadasiLearning to Optimize
Research Division, BrightMind AI, Seattle, WA · Texas Tech University, Lubbock, TX
Misspecified Estimate-then-Optimize Leads to Supra-Competitive Prices
May 15, 2026Jackie Baek, Vivek F. Farias, Farrell WuPredict-Then-Optimize Learning to Optimize
Stern School of Business, New York University · Massachusetts Institute of Technology
Learn2Splat: Extending the Horizon of Learned 3DGS Optimization
May 15, 2026Naama Pearl, Stefano Esposito, Haofei Xu +63D Gaussian Splatting Meta-Learning
University of Tübingen, Tübingen AI Center · ETH Zurich · Meta Reality Labs
DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems
May 14, 2026Romario Gualdrón-Hurtado, Roman Jacome, Leon Suarez +1Learning to Optimize Image Reconstruction
Department of Computer Science, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia · Department of Electrical Engineering, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia
Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization
May 13, 2026Isabella Poles, Simon Arberet, Riqiang Gao +5Learning to Optimize Oncology
Politecnico di Milano, Milan, Italy · Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA · Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
Local Inverse Geometry Can Be Amortized
May 13, 2026Aaditya L. KachhadiyaNeural Surrogate Modeling Inference-Time Optimization
Independent Researcher Surat, India
NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering
May 13, 2026Yingming Mao, Ximeng Liu, Jingyi Cheng +8Stochastic Optimization Neural Network Optimization
Xi’an Jiaotong University · Shanghai Innovation Institute · Shanghai Jiao Tong University +2
Low-Cost Labels, Reliable Choices: Rollout-Calibrated Hyper-Heuristics for Job Shop Scheduling
May 11, 2026Junhao Wei, Yanxiao Li, Yifu Zhao +8Selective Prediction Metaheuristic Optimization
Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China · College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China · 4Macao Polytechnic University, Macao, 999078, China
LLM4Branch: Large Language Model for Discovering Efficient Branching Policies of Integer Programs
May 11, 2026Zhinan Hou, Xingchen Li, Yankai Zhang +2Branch and Bound Mixed-Integer Linear Programming
Department of Automation, BNRist, Tsinghua University, Beijing, China.
Tight Generalization Bounds for Noiseless Inverse Optimization
May 9, 2026Pouria Fatemi, Hoomaan Maskan, Suvrit Sra +1Parameter Estimation Learning to Optimize
Technical University of Munich, Germany · Uppsala University, Sweden · University of Toronto, Canada
When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
May 7, 2026Yi Wang, Chandrajit BajajDynamical Systems Learning to Optimize
1Oden Institute · Department of Computer Science
Distributionally-Robust Learning to Optimize
May 7, 2026Vinit Ranjan, Jisun Park, Bartolomeo StellatoLearning to Optimize Distributionally 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.
NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees
Apr 30, 2026Bimol Nath Roy, Rahul Golder, MM Faruque HasanNeural Network Optimization Learning to Optimize
Texas A&M University

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A Solver-Free Training Method for Predict-then-Optimize

Beichen Wan, Mo Liu

Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA.

Predict-Then-Optimize Learning to Optimize Decision-Focused Learning

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We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent linear optimization task. Directly minimizing the empirical decision regret is intractable for linear programming and combinatorial optimization since the decision mapping is piecewise constant, and the gradients are zero almost everywhere. While existing methods address this by smoothing the differentiation process, they suffer from scalability issues, since a computationally expensive solver call is required for every gradient evaluation. To address this, we propose a decision-focused learning pipeline based on a measure transformation principle, which yields a new surrogate loss that is completely optimization-solver-free during training. We establish theoretical guarantees, including Fisher consistency and excess risk bounds. Empirically, our method achieves decision quality competitive with state-of-the-art methods while reducing training time by orders of magnitude.

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