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 26
Optimal Reinsurance-Dividend Strategy with Fixed Transaction Costs in a Regime-Switching Brownian Risk Model: A Viscosity Solution to the Impulse Control Problem
We consider a problem of optimal proportional reinsurance-dividend distribution under a Brownian risk model, where both the drift and volatility coefficients are subject to endogenous…Distributionally Robust Average-Reward Reinforcement Learning: Finite-Sample Guarantees under Weak Communication
We study distributionally robust reinforcement learning (DR-RL) in the average-reward setting under weak communication.Revisiting AdaGrad in Stochastic Convex Optimization: Last Iterates, High Probability, and Lower Bounds
AdaGrad and AdaGrad-Norm are widely used adaptive methods, but their precise behavior in stochastic convex optimization remains less understood.Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks
Over-the-air computation has emerged as a scalable and efficient solution for deploying federated learning algorithms in wireless networks by exploiting waveform superposition for simultaneous model…Clipped or Unclipped? Finite-Sample Trade-offs for Averaged SGD under Heavy-Tailed Noise
Gradient clipping is widely used to stabilize training, but it need not improve the statistical accuracy of averaged SGD, even under heavy-tailed noise.Decentralized Optimization with Cross-Coupled Mixed Affine Constraints
We study decentralized optimization with cross-coupled mixed affine constraints, where local and shared variables interact through two affine channels.TR-SSQP: A Trust-Region Method for Constrained Stochastic Optimization under Heavy-Tailed Noise
We consider stochastic nonlinear optimization problems with deterministic equality constraints.Optimal Prophet Inequalities for Gain from Trade
We initiate the study of prophet trading, an online trading model in which a trader interacts with sellers and buyers who arrive in a uniformly random order and whose prices are drawn independently…Learning Provable Neural Network Observer for Uncertain Dynamical Systems
In many safety-critical applications, control of uncertain dynamical systems relies on observers that estimate states and external disturbances.Faster Linear Programming with $\sqrt{\mathrm{rank}}$ Linear System Solves
Lee and Sidford [LS19] showed that the linear program $\min\{c^\top x\ :\ A^\top x=b,\ l\leq x\leq u\}$ over $x\in\mathbb{R}^m$, where $A\in\mathbb{R}^{m\times n}$, can be solved to accuracy $ε$…Tight Stochastic Condition-Number Dependence in Nonconvex-Strongly-Concave Minimax Optimization
We study whether the linear condition-number dependence in the stochastic complexity of SAPD+ is necessary for nonconvex-strongly-concave minimax optimization.Retraction-Based Gradient Projection Algorithms on Manifolds
We introduce a framework for retraction-based convex optimization on Riemannian manifolds, which includes a notion of retraction-specific convex sets and retraction-based gradient projection…Cross-Backend QIEO: Universal Runtime Portability across OpenMP5, CUDA, HIP, and Multi-Language Interfaces
Quantum-inspired algorithms emulate quantum mechanical principles, such as, superposition, interference, and probabilistic amplitude evolution, on classical hardware by representing candidate…Landscape Limits of Quantum-Inspired Evolutionary Optimization across 256 continuous functions
Quantum-inspired evolutionary optimization (QIEO) represents design variables as a set of qubits and searches a continuous, multi-dimensional landscape through rotation of the qubit's amplitude pair.Towards Understanding Momentum Acceleration in River-Valley Loss Landscape
The empirical success of pretraining large language models has inspired a deeper investigation into the underlying loss landscapes and the optimization dynamics.Moment Ambiguity and the Limits of Robust Stochastic Optimization
We study fundamental information-theoretic limits of robust stochastic optimization when the distribution is known only through its exact moment sequence.Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning
Distributionally robust optimization (DRO) provides a principled framework for learning under distribution shift, but its practical use is hindered by the difficulty of evaluating worst-case risks…Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transport
Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ significantly from historical training data.Averaged Mirror Descent and Dual Gradient Methods: Convergent Algorithms for Entropic Gromov-Wasserstein Problems
The Gromov-Wasserstein (GW) distance measures the discrepancy between metric measure (mm) spaces and identifies optimal alignments between them based solely on their intrinsic structure.Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization
Multi-objective Bayesian optimization (MOBO) is commonly approached through specialized acquisition functions or scalarization schemes designed to explicitly account for trade-offs among…