Applied Math · Mathematics & Statistics
Optimization & Control
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754 papers, from 31 August 2026 to 5 November 2026: 698 arXiv preprints and 56 OpenAlex records.
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Newest papers, page 17
Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity
We consider stochastic nonsmooth nonconvex composite optimization, which includes several important problems such as constrained optimization and the regularized training of neural networks.Fix What's Wrong, Keep What's Right: Hallucination Repair for LLM-Based Optimization Modeling
Large language models (LLMs) are increasingly being explored to automate operations research modeling, reduce modeling time and lower the expertise barrier to optimization based decision support.Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion
This paper studies an open network design problem of constructing graph topologies with maximum algebraic connectivity under fixed numbers of vertices and edges.Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations
Autonomous small-body proximity operations require continuous-thrust guidance under uncertain gravity, solar-radiation pressure (SRP), and stochastic disturbances.Beyond Nominal Equilibria: Risk-Averse Multi-Population Mean-Field Games
Recent advances in mean-field games and its multi-population variants enable large-scale heterogeneous multi-agent systems to be modeled through representative agents and their associated mean-field…The Geometry of Anisotropic Dilation for Optimal Regularization
A central question in data-driven inverse problems is how to construct a regularizer that adapts to the geometry of the data distribution.Global Exponential Convergence of Two-Layer Linear Network Training
We prove global exponential (linear) convergence with an explicit rate in the rich scaling for wide two-layer linear networks trained with smooth Polyak-Lojasiewicz predictor losses.Boundary-aware Reinforcement Learning for Hypercube State Spaces via Deterministic Policy Gradient
We develop a continuous-time deterministic policy gradient framework for reinforcement learning with reflected state dynamics, where the state process is governed by a controlled reflected…Identifiability of a dissipative knowledge-dynamics model: exact recovery under designed excitation, degeneration on observational data
Human learning is a dissipative dynamical process: mastery accumulates through practice, decays through forgetting, and propagates across interdependent concepts.Outperformance Inverse Optimization: Learning Objective Functions that Outperform Agent Decisions
Inverse optimization estimates the weights of an objective function that explain observed decisions as optimal solutions, and is used in a variety of fields.Eigenvalues of the Hessian in Deep Learning: The Origin of Symmetry and Its Breaking
Hessian spectra at trained models in deep learning exhibit a persistent pattern: eigenvalues organize into distinct clusters, including a large bulk near zero and a few isolated outliers.Average-Reward Reinforcement Learning for Multichain MDPs: A Hierarchical Decomposition Approach
We study learning optimal policies in average-reward multichain Markov decision processes (MDPs), where the optimal gain may depend on the initial state and recurrence structures vary across…Safe Meta-Policy Design with Risk Control
Models can be retrained as new data arrive, but deploying every new version risks replacing a good policy with a worse one.Rubix: Global Correspondence-Free Point Set Alignment through Assignment Geometry
Procrustes-Wasserstein alignment jointly estimates a matching and rotation without supplied correspondences, but alternating minimization can stop at suboptimal solutions.Decentralized SGD under Heavy-Tailed Noise: Optimal Convergence Rates and the Role of Gradient Clipping
Heavy-tailed noise has been widely observed in modern machine learning, motivating the use of methods like gradient clipping and normalization.Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks
Understanding why deep neural networks make a given prediction is of great importance for their safe deployment.Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling
The Minimum Birkhoff Decomposition (MBD) seeks a sparse weighted sum of matchings and is a challenging optimization problem with applications in network scheduling and energy trading.Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization
Distributionally robust optimization (DRO) protects decisions against distributional uncertainty by optimizing over an ambiguity set, but poorly aligned set geometry can require large radii and…Noise-Induced Navigation in Non-convex Domains and Compact Manifolds
In this note, we study the problem of designing a feedback law that globally steers a system to a prescribed target configuration.Budgeted Multi-Source Counterfactual Annotation for Off-Policy Evaluation
Off-policy evaluation (OPE) estimates the value of a target policy from logged data, but limited behavior-policy coverage can force high-variance reweighting or reward-model extrapolation.