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 21
Interaction-Aware Embedding Optimization for Neutral-Atom Quantum Processors
Neutral atom-based quantum computers have the ability to dynamically reshape the qubit register geometry between experimental runs.Sharp Convergence and Sample Complexity of Policy Mirror Descent for Average-Reward MDPs
Policy mirror descent (PMD) has a mature finite-time theory in discounted Markov decision processes (MDPs), but less is known in the average-reward setting, a more natural objective for many control…ExpertMuon-Compass: Alignment-Guided Step Sizes for Mixture-of-Experts Training
Mixture-of-experts (MoE) language models send each token to a few experts, so each expert is trained on a different part of the data, and this part changes during training.Ideal Paths for Approximating Logistic Gradient Descent Trajectories at Large Initialization
Modern training on a new task often starts from a previously trained model rather than from scratch, raising the question of how this initialization affects the subsequent training trajectory.Mean field games as a tool for AI safety: a worked example from the July 2026 Hugging Face incident
One way to make AI systems safe is to shape what the system is: its objective and dispositions.OR for AI That Does OR: Routing LLMs up the Escalator inside the OSCAR Framework
Large language models can translate business descriptions into optimization models, but executable code may misrepresent constraints or objectives.Initial condition recovery in nonlinear damped viscous photoacoustic tomography using a convolutional neural network-guided gradient-free optimization framework
Photoacoustic tomography (PAT) is a hybrid imaging modality that combines high optical contrast with high ultrasonic resolution for biomedical imaging applications.OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous
Spacecraft rendezvous and proximity operations (RPO) are currently planned through an expertise-intensive process in which engineers translate high-level operational intent into safe, dynamically…Fully Online Decentralized Learning in Stochastic Games with Unknown Independent Chains
We consider stochastic games with independent controlled chains and unknown transition kernels, where players observe only their local states and realized payoffs.Low-Budget Active Learning through Entropic Optimal Transport
We consider low-budget active learning, which consists of selecting a limited number of points, the coreset, such that a model can be trained to high accuracy on the selection only.Reachability-Informed Reinforcement Learning for Multi-Impulse Interplanetary Transfers
Reinforcement learning offers the prospect of a reusable sequential decision-making mechanism for spacecraft trajectory design, motivating policy interfaces that connect learned decisions to the…Least-time Gradient Flow
Prescribing the speed of gradient flow on the risk itself, by the dynamics $\dot w=-u(E(w))\nabla E(w)/\abs{\nabla E(w)}^{2}$, makes the risk $e(t)=E(w(t))$ obey $\dot e=-u(e)$ exactly, whatever the…Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming
Primal-dual hybrid gradient (PDHG) methods solve large-scale linear programs (LPs) using GPU-friendly matrix-vector products and projections, but their practical performance depends on coordinating…Optimal Momentum Methods for Stochastic Multilevel Compositional Optimization
This paper investigates stochastic multi-level optimization where the objective is a nested composition of several smooth non-convex functions.Convergence Analysis of STORM Under Different Geometries
Stochastic recursive momentum (STORM) achieves fast convergence for nonconvex optimization via the variance reduction effect, but existing analyses rely on the strong average smoothness assumption.Lower Bounds for Stochastic First-Order Algorithms with Variance Reduction in Nonconvex--Concave Minimax Optimization
We establish complexity lower bounds for stochastic first-order algorithms in nonconvex--concave minimax optimization, allowing algorithms to use variance reduction.Removing spurious minima for planar features by skip connections
Understanding loss landscapes is central to explaining neural-network training, yet their structure remains only partially understood even in simple models.Optimal Stochastic Bilevel Optimization with First-Order Oracles
We study nonconvex--strongly-convex bilevel optimization under a stochastic first-order oracle.The Curvature of Regret in Contextual Linear Optimization
Decision-focused learning for linear optimization is complicated by the discontinuity of the optimizer, where small cost errors may leave the decision unchanged or move it to a different vertex.Randomized Matvec Lower Bounds for Simplex-Based Matrix Games
We prove randomized matrix-vector query lower bounds for two normalized matrix-game geometries: a Euclidean unit ball against a probability simplex, with row norms at most one, and two probability…