mlsanityData to 14 Oct 2026

Learning to optimize: A tutorial for continuous and mixed-integer optimization

arXiv 2405.15251 ↗First posted 24 May 2024math.OC cs.LG stat.MLcited by 8

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Abstract (arXiv)

Learning to Optimize (L2O) stands at the intersection of traditional optimization and machine learning, utilizing the capabilities of machine learning to enhance conventional optimization techniques. As real-world optimization problems frequently share common structures, L2O provides a tool to exploit these structures for better or faster solutions. This tutorial dives deep into L2O techniques, introducing how to accelerate optimization algorithms, promptly estimate the solutions, or even reshape the optimization problem itself, making it more adaptive to real-world applications. By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand, this tutorial provides a comprehensive guide for practitioners and researchers alike.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

11 of 34 references.

References · 11

  1. 2017Smart "Predict, then Optimize"cited by 339
  2. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 182
  3. 2014From Predictive to Prescriptive Analyticscited by 143
  4. 2014ParamILS: An Automatic Algorithm Configuration Frameworkcited by 89
  5. 2021Learning to Select Cuts for Efficient Mixed-Integer Programmingcited by 48
  6. 2015A Three-Operator Splitting Scheme and its Optimization Applicationscited by 34
  7. 2022Efficient Neural Neighborhood Search for Pickup and Delivery Problemscited by 29
  8. 2022Adaptive Cut Selection in Mixed-Integer Linear Programmingcited by 26
  9. 2023Machine Learning for Cutting Planes in Integer Programming: A Surveycited by 20
  10. 2019Benders Cut Classification via Support Vector Machines for Solving Two-stage Stochastic Programscited by 9
  11. 2023Automatic MILP Solver Configuration By Learning Problem Similaritiescited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 4.

8 papers.

  1. 2025Lightweight Deep Unfolding Networks with Enhanced Robustness for Infrared Small Target Detectioncited by 4
  2. 2024An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrollingcited by 3
  3. 2025Accelerating Optimization via Differentiable Stopping Timecited by 1
  4. 2026Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networkscited by 1
  5. 2025Tuning-Free Structured Sparse PCA via Deep Unfolding Networkscited by 0
  6. 2026Neural Ising Machines via Unrolling and Zeroth-Order Trainingcited by 0
  7. 2026Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontierscited by 0
  8. 2026DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programmingcited by 0