Data to 7 Oct 2026

A Rewriting System for Convex Optimization Problems

arXiv 1709.04494 ↗First posted 13 Sep 2017math.OC cs.MScited by 178

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Abstract (arXiv)

We describe a modular rewriting system for translating optimization problems written in a domain-specific language to forms compatible with low-level solver interfaces. Translation is facilitated by reductions, which accept a category of problems and transform instances of that category to equivalent instances of another category. Our system proceeds in two key phases: analysis, in which we attempt to find a suitable solver for a supplied problem, and canonicalization, in which we rewrite the problem in the selected solver's standard form. We implement the described system in version 1.0 of CVXPY, a domain-specific language for mathematical and especially convex optimization. By treating reductions as first-class objects, our method makes it easy to match problems to solvers well-suited for them and to support solvers with a wide variety of standard forms.

Key facts. Cited by 178 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2017.

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Citing papers per year: 2020: 1; 2021: 33; 2022: 38; 2023: 30; 2024: 29; 2025: 30; 2026: 17.

178 papers.

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2023Quantifying & Modeling Multimodal Interactions: An Information Decomposition Frameworkcited by 81
2022Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experimentscited by 50
2023How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Controlcited by 50
2022Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositionscited by 31
2022A General Framework for Sample-Efficient Function Approximation in Reinforcement Learningcited by 31
2023Exploring and Interacting with the Set of Good Sparse Generalized Additive Modelscited by 30
2021Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stabilitycited by 29
2021On Robust Optimal Transport: Computational Complexity and Barycenter Computationcited by 27
2022Gaussian Universality of Perceptrons with Random Labelscited by 26
2021Global Optimality Beyond Two Layers: Training Deep ReLU Networks via Convex Programscited by 25
2025AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?cited by 24
2022OPOM: Customized Invisible Cloak towards Face Privacy Protectioncited by 22

38 more

2021Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularizationcited by 21
2021Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solverscited by 21
2021Defense Against Reward Poisoning Attacks in Reinforcement Learningcited by 20
2021On the Convergence and Optimality of Policy Gradient for Markov Coherent Riskcited by 19
2022End-to-End Stochastic Optimization with Energy-Based Modelcited by 19
2021Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systemscited by 18
2022Near-Optimal Collaborative Learning in Banditscited by 18
2022On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Errorcited by 16
2024Linear Recursive Feature Machines provably recover low-rank matricescited by 16
2022Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Trainingcited by 15
2021Neural Spectrahedra and Semidefinite Lifts: Global Convex Optimization of Polynomial Activation Neural Networks in Fully Polynomial-Timecited by 14
2021Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networkscited by 14
2023Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attackscited by 14
2021Boosted CVaR Classificationcited by 12
2021Constrained Instance and Class Reweighting for Robust Learning under Label Noisecited by 12
2023Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixturescited by 12
2023A Symmetry-Aware Exploration of Bayesian Neural Network Posteriorscited by 12
2025Distributionally Robust Reinforcement Learning with Human Feedbackcited by 12
2022Fair Generalized Linear Models with a Convex Penaltycited by 11
2023Loss Balancing for Fair Supervised Learningcited by 11
2025The Nuclear Route: Sharp Asymptotics of ERM in Overparameterized Quadratic Networkscited by 11
2022Personalized Federated Learning via Convex Clusteringcited by 10
2022Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solverscited by 10
2024Incentivizing Quality Text Generation via Statistical Contractscited by 10
2021FL-Market: Trading Private Models in Federated Learningcited by 9
2021PAGnol: An Extra-Large French Generative Modelcited by 9
2024Testing Calibration in Nearly-Linear Timecited by 9
2021Risk-Averse Stochastic Shortest Path Planningcited by 8
2021On Multimarginal Partial Optimal Transport: Equivalent Forms and Computational Complexitycited by 8
2022Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributionscited by 8
2021City-scale Scene Change Detection using Point Cloudscited by 7
2021OnSlicing: Online End-to-End Network Slicing with Reinforcement Learningcited by 7
2023Adversarial Ink: Componentwise Backward Error Attacks on Deep Learningcited by 7
2023fairret: a Framework for Differentiable Fairness Regularization Termscited by 7
2023The Convex Landscape of Neural Networks: Characterizing Global Optima and Stationary Points via Lasso Modelscited by 7
2021Training Quantized Neural Networks to Global Optimality via Semidefinite Programmingcited by 6
2022Metrizing Fairnesscited by 6
2022Gradient-descent quantum process tomography by learning Kraus operatorscited by 6

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