# Optimization Learning

arXiv: https://arxiv.org/abs/2501.03443 · HTML: https://mlsanity.com/paper/2501.03443/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2025.

## Built on by (most cited first, 15 of 15)

- [FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees](https://mlsanity.com/paper/2506.00362/) (2025)
- [Pinet: Optimizing hard-constrained neural networks with orthogonal projection layers](https://mlsanity.com/paper/2508.10480/) (2025)
- [Improving Feasibility via Fast Autoencoder-Based Projections](https://mlsanity.com/paper/2604.03489/) (2026)
- [Sobolev Training of End-to-End Optimization Proxies](https://mlsanity.com/paper/2505.11342/) (2025)
- [SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints](https://mlsanity.com/paper/2602.09317/) (2026)
- [EDGE: A Theoretical Framework for Misconception-Aware Adaptive Learning](https://mlsanity.com/paper/2508.07224/) (2025)
- [IntelliCode: A Multi-Agent LLM Tutoring System with Centralized Learner Modeling](https://mlsanity.com/paper/2512.18669/) (2025)
- [Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels](https://mlsanity.com/paper/2603.05495/) (2026)
- [Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning](https://mlsanity.com/paper/2503.01859/) (2025)
- [Task Scheduling & Forgetting in Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2503.01941/) (2025)
- [Fast MLE and MAPE-Based Device Activity Detection for Grant-Free Access via PSCA and PSCA-Net](https://mlsanity.com/paper/2503.15259/) (2025)
- [Irec: A Metacognitive Scaffolding for Self-Regulated Learning through Just-in-Time Insight Recall: A Conceptual Framework and System Prototype](https://mlsanity.com/paper/2506.20156/) (2025)
- [Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow](https://mlsanity.com/paper/2511.11677/) (2025)
- [Decision-focused learning for optimal PV-Battery scheduling](https://mlsanity.com/paper/2605.28340/) (2026)
- [Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application](https://mlsanity.com/paper/2606.25362/) (2026)

## Abstract (arXiv)

This article introduces the concept of optimization learning, a methodology to design optimization proxies that learn the input/output mapping of parametric optimization problems. These optimization proxies are trustworthy by design: they compute feasible solutions to the underlying optimization problems, provide quality guarantees on the returned solutions, and scale to large instances. Optimization proxies are differentiable programs that combine traditional deep learning technology with repair or completion layers to produce feasible solutions. The article shows that optimization proxies can be trained end-to-end in a self-supervised way. It presents methodologies to provide performance guarantees and to scale optimization proxies to large-scale optimization problems. The potential of optimization proxies is highlighted through applications in power systems and, in particular, real-time risk assessment and security-constrained optimal power flow.