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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ORSMT: Unleashing the Potential of Satisfiability Modulo Theories for Semantic Verification and Repair in LLM-Based Optimization
Large language models increasingly automate optimization modeling from natural language problems, producing written mathematics, solver code and a returned report.Curvature-Scaled Sharpness-Aware Minimization: Convergence Beyond Smoothness
Sharpness-Aware Minimization (SAM) has established itself as a widely used method for training deep neural networks.Group Distributionally Robust Optimization over Decentralized Networks
We study group distributionally robust optimization (GDRO) over decentralized networks, where workers collaborate to learn robust models by minimizing the maximum local risk.Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming
Cutting planes (cuts) remove fractional solutions from mixed-integer linear programs (MILPs) without excluding any feasible integer solutions, making them a key ingredient in modern MILP solvers.Preventing Model Extraction in Contextual Linear Optimization
Consider a contextual linear optimization problem, where a predict-then-optimize (PTO) system returns optimal decisions without revealing the cost vectors used in its downstream linear program.Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization
Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined.A Quasipolynomial Algorithm for $\ell_\infty$ Contractions
We give an algorithm for computing an $\eps$-approximate fixed point of any $d$-dimensional nonexpansion with respect to the $\ell_\infty$ norm in time $\log(1/\eps)^{O(\log^2 d)}$.A Transformer-based Approach for Real-time Multi-agent Path Planning with Varying Initial/Final Conditions
We address the problem of real-time multi-agent path planning under prescribed initial and final configurations, formulated as a high-dimensional optimal control problem subject to inter-agent…LLM2Cert: LLM Synthesis of Certificates with SMT/Lean Verification
Lyapunov functions and barrier certificates give state-space guarantees of stability and safety, but synthesizing and verifying them---especially for nonlinear, high-dimensional systems---remains an…Minimum-Time Escape from the Conic Field of View of a Reeds-Shepp Pursuer: Classification and Game Value for a Slower or Equally Fast Evader
We study minimum-time escape of an omnidirectional evader from the forward conic field of view of a Reeds-Shepp pursuer with minimum turning radius $r$.Anytime-Valid Runtime Monitoring of Learned Barrier Certificates
Learned barrier certificates provide a principled mechanism for establishing safety of stochastic systems, but their validity may deteriorate during deployment when the underlying dynamics differ…Constant Step Sizes for Muon-Type Spectral Optimizers on Strongly Convex Quadratics
Muon and related optimizers update a weight matrix with a spectrally transformed gradient.When Momentum Helps Polyak--Ruppert Averaging
Polyak--Ruppert averaging gives SGD and momentum methods the same asymptotic covariance.Weak-$L^α$ Noise, Strong Guarantees: Gradient Clipping under Affine Heavy Tails
Gradient clipping is a key tool for obtaining high-probability convergence guarantees for stochastic first-order methods under heavy-tailed noise.Local Work, Global Progress: A Convergence Theory of AI Agent Collaboration
Teams of large language model (LLM) agents are increasingly applied to complex tasks, but their collaboration protocols are mostly designed empirically, without convergence guarantees.Variance Reduction with Delayed Feedback for Non-Convex Optimization
We study variance reduction for non-convex optimization when gradient evaluations return with a delay.Efficient Ground-State Manifold Amplification for Rydberg Atom Arrays via Houdayer Cluster Updates
We demonstrate a hybrid quantum-classical approach for exploring ground-state manifolds of maximum independent set problems, in which quantum hardware supplies the initial solutions and an entirely…Convergence of stochastic gradient descent for learning linear neural networks
Stochastic gradient descent (SGD) is the cornerstone of modern deep learning, yet its theoretical convergence properties remain poorly understood in non-convex settings.OptScientist: Automated Discovery of Optimization Laws
Many scientific phenomena are best understood as outcomes of underlying optimization problems, yet automated scientific discovery has largely focused on recovering equations that describe observed…Continuous-Time Policy Gradient from Discrete Data: Convergence Analysis and Model-Free Algorithms
Continuous-time reinforcement learning seeks an optimal policy under unknown dynamics driven by stochastic differential equations using observations collected at discrete times.