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 27
Understanding the Subspace Stabilization of the Hessian and Gradient Covariance Matrix
The phenomenon of the top subspace stabilization of the Hessian matrix is an surprising and critical aspect in study of the second-order information of neural network training.Tight Convergence Bounds for the Classical Kaczmarz Method
The classical method of Kaczmarz, introduced in 1937, is a textbook iterative method for solving linear systems $A x = b.$ Despite its widespread use, particularly in the context of solving inverse…When Should a Human Take Back Control? Optimal Delegation under Turbulent AI Risk
Deploying AI systems requires deciding when to delegate tasks and when humans should intervene to monitor and mitigate risk induced by AI operations.Precise Convergence Speed of Clipped SGD
We present a tightened convergence analysis of clipped gradient descent on $(L_0, L_1)$-smooth functions, with quantitative constants.Shrinking-Tube Concentration for Adaptive Markovian Stochastic Approximation
Adaptive algorithms increasingly make decisions while reshaping the dynamics that generate their future data.Cost-Sensitive Online Window Size Selection for Portfolio Management
This paper investigates cost-sensitive online window size selection for portfolio management under changing market conditions.MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization
Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential.A Contraction Framework for Stochastic Operators with Bootstrapping: Application to TD Learning
Many iterative algorithms rely on bootstrapping.AT-SKM-Net: An Accelerated Trainable Sampling Kaczmarz-Motzkin Framework for Linear Hard-Constraint Feasibility on Dynamic Graphs
Graph-structured optimization with linear constraints is fundamental to critical infrastructure but faces scalability limits due to massive strict hard constraints and high dimensionality.GridSFM: A Foundation Model for Solving AC Optimal Power Flow
We introduce GridSFM, a framework that combines a pretrained foundation model across grid topologies with physics-informed fine-tuning for solving AC Optimal Power Flow (AC-OPF) at scale.Anchored Extra-Proximal Methods: Optimal Higher-Order Methods for Monotone Inclusion Problems
We study the deterministic oracle complexity of finding approximate solutions to composite monotone inclusion problems, formed by the sum of a smooth single-valued monotone operator and a maximally…A Nearly Quadratic Lower Bound for Linear Optimization over Convex Bodies in the Membership Oracle Model
We prove nearly quadratic lower bounds for randomized algorithms for linear optimization and uniform sampling over convex bodies in the membership oracle model.Riemannian Gradient Descent for Gaussian Mixture Models with unknown diagonal covariances
This paper investigates the numerical resolution of the Beurling-LASSO (BLASSO), a convex optimization framework that promotes sparsity in the space of measures.Convergence guarantees for Muon: New parameter regimes and generalizations
In this paper, we establish the first asymptotic convergence guarantees for the Muon algorithm through a more accurate proxy for the Newton-Schultz iteration than the typical matrix sign function.OpenHail: An Event-Driven Gymnasium Environment for Electric Ride-Hailing Fleet Control
Machine-learning policies have attracted increasing interest for ride-hailing fleet control in recent years.Matrix-Vector Complexity of Low-Rank Approximation
We establish matching polynomial query bounds for low-rank approximation from exact matrix--vector products.ZO-COSMO: Index-Free One-Hop Mixing for Decentralized Zeroth-Order Optimization
Sparse communication in decentralized zeroth-order learning requires compatible peer-state coordinates.Distributed Stochastic Approximation Algorithms and Heavy-Tailed Age of Information
Algorithms in multi-agent systems such as federated learning, mobile robotic swarming, and consensus control can be designed and analyzed as distributed stochastic approximation algorithms.Limiting-Kernel Q($λ$): Bridging Short and Long Horizons
In value-based reinforcement learning, improving the accuracy of policy evaluation has been shown to improve downstream policy optimization performance.Inverse knapsack at two capacities: which pairs of value-cardinality hulls are realizable?
One item set evaluated at two capacities $R<D$ produces two concave hulls of optimal value against cardinality.