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 34
Continuous Learning of Gravity Field Irregularities Around Small Bodies via Neural Hamiltonian ODEs
We propose to learn the unknown dynamics in the proximity of a small body directly from tracking data, representing them as a feed-forward neural network embedded in the system Hamiltonian.Deriving the Pure Price of Anarchy for Networked Resource Allocation Games
This work considers multi-agent coordination with arbitrary information networks among the agents using a game-theoretic approach.Score-based Outlier Generation via Controlling the Radon-Nikodym Derivative
Outliers are important for stress-testing algorithms and understanding system behaviour under rare conditions.Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise
Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, particularly in applications requiring robustness or…Rank-Efficient LoRA via Joint Tangent-Space Optimization under Isotropic Curvature
Low-Rank Adaptation (LoRA) is an effective approach for adapting large pretrained models by learning low-rank weight updates.Online Inverse Integer Linear Optimization via Small-Gradient Skipping: Constant Regret and Finite Mistakes
In online inverse linear optimization, the learner predicts a weight at each round, observes the optimal action of the agent, and updates its prediction.Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
Meta-learning has emerged as an effective paradigm for transferring knowledge across sequential bandit tasks.Beyond Verified Answers: Solver-Informed Self-Distillation for Bootstrapping Operations Research Language Models
Modern large language models (LLMs) can translate natural-language descriptions into operations research (OR) formulations.Minimum-makespan completion and vertex selection leave the Wang-Sitters constant at 11/6
The 11/6 worst-case constant of the Wang-Sitters rounding scheme, which a companion note establishes, can naturally be attributed to the freedom in Step 3, where an arbitrary valid slot matching is…Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations
Reinforcement learning (RL) agents deployed in real-world environments are often vulnerable to adversarial perturbations in state observations, creating risks in safety-critical applications.Governed Human-AI Prioritization Under Uncertainty: Adaptive Estimation and Dependency-Constrained Portfolio Selection
AI-native software engineering increasingly combines human judgment, historical analogy, parametric estimation, and AI-generated forecasts in the same prioritization decision.Hyperspectral Anomaly Detection via Group Sparse Low-Rank Tensor Factorization With Automatic Anomaly Grouping
Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection.Sparse Data Augmentation for Optimization with Provable Guarantees
In nonconvex optimization problems arising in geometric machine learning, data augmentation is commonly used to promote invariance by averaging empirical losses over transformations of the data.GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra
This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA).Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
We study zeroth-order optimization of non-convex functions with the aid of directional hints, which are cheap but potentially inaccurate approximations of the true gradient direction, given by…How to Make the Gradient Mapping Small for Constrained Stochastic Min-Max Problems and Beyond
We study the stochastic first-order oracle complexity for constrained or regularized convex-concave min-max optimization and stochastic monotone variational inequalities.The Exact Time-Uniform Rate Frontier for Stochastic Gradient Descent on Smooth Convex Objectives
We study the time-uniform convergence of the raw iterate of standard stochastic gradient descent (SGD) for unconstrained smooth convex objectives.A Lower Bound for the Heavy-Ball Method on Smooth Convex Functions
Can the classical Heavy-Ball method, with arbitrary horizon-dependent parameters chosen in advance, achieve Nesterov's $O(T^{-2})$ last-iterate rate on every smooth convex objective?PAC-Bayesian Bounds for Learning Partially Observed Stochastic Linear Time-Invariant State-Space Systems with Inputs and Sub-Gaussian Noise
In this paper we derive a Probably Approximately Correct (PAC)-Bayesian error bound for partially observed linear time-invariant (LTI) stochastic dynamical systems in state-space form with inputs…Near-Optimal Quantum Lower Bounds for Convex Optimization via Fourier Rank
We establish a near-linear quantum query lower bound for high-accuracy convex optimization over an explicit family of $n$-dimensional ellipsoids.