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 20
Largest Rashomon sets of decision trees for robust contextual optimization
Many decision trees fit the same data almost equally well, yet they can route a query point to different leaves and induce different local empirical distributions.Nonlinear Density-Driven Optimal Control (D2OC) for Multi-Agent Spatial Coverage via Sequential Convex Programming
This paper presents a nonlinear extension of Density-Driven Optimal Control (D2OC) for multi-agent spatial coverage with prescribed density distributions.Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking
We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model.Policy as Data: Replay-Based Policy Dual Averaging via Advantage Regression
Actor-critic methods reuse past experience to improve sample efficiency.Asking the Crowd the Right Question: Bias-Cancelling Weights for Federated Learning
A federated objective is a weighted sum of client risks, and the weights are almost always fixed in advance.GPU-Accelerated Bregman Douglas-Rachford Splitting for Discrete Optimal Transport
We present GPU-accelerated Bregman Douglas--Rachford splitting algorithm (BDRS) for discrete optimal transport problem in three input formats: an explicit cost matrix, a point cloud with a ground…Emergent Underactuation in Fully Actuated Multirotors by Minimizing a Homogeneous Allocation Cost
Full actuation is commonly valued for independent position and attitude tracking.A hybrid CNN-adjoint optimization framework for reconstruction of viscoelastic tissue properties in magnetic resonance elastography
Magnetic resonance elastography (MRE) is a noninvasive imaging modality for quantifying the viscoelastic properties of soft tissues from shear wave propagation.Distributed Learning with Selective State Space Models: Architecture-Aware Convergence Analysis
Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with recurrent state-space dynamics.Muon Learns Facts Better: Understanding the Role of Spectral Orthogonalization
The Muon optimizer applies spectral orthogonalization to matrix-valued updates and has shown strong performance in large-scale neural network training, yet the mechanisms of this transformation in…Learn Feasibility Once, Optimize All Objectives: Derivative-Free Diffusion Models for Chance-Constrained Programming
Chance-constrained programs (CCPs) optimize decisions under uncertainty by limiting the probability of constraint violation.Near-Optimal Convex Optimization with Lazy Second-Order Oracles
This paper studies the complexity of convex optimization using lazy second-order oracles (Doikov, Chayti, and Jaggi, ICML 2023), where an algorithm queries gradients every iteration and Hessians…Training-Loss Guarantees for Muon with Finite-Step Newton--Schulz Orthogonalization
Existing convergence analyses of Muon either assume exact orthogonalization or analyze classical Newton--Schulz polynomials, and guarantee only stationarity, so it is unresolved what Muon's five…Information Limits of Low-Rank Approximation Certification
Low-rank approximation can require additional matrix--vector products to verify that its error meets a prescribed tolerance.Grid Theory and Polynomiality in Dynamic Lot-Sizing
Why are some dynamic lot-sizing problems polynomial?On the Convergence of Success Conditioning for Policy Optimization
Success conditioning is a strategy for improving decision-making policies in stochastic environments; it updates a policy by increasing the probability of taking actions that yield successful…Planning to Learn
Policy-gradient methods are central to modern reinforcement learning, including LLM post-training.From Mixing to Tearing: Graph Decomposition in Decentralized Optimization via Message Passing
We study the minimization of sums of smooth strongly convex functions over undirected graphs, with each function held by one agent and communication restricted to neighbors in the graph.Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing
Analog quantum computing with neutral atom computers is rapidly evolving into a promising way to perform combinatorial optimization tasks at scale.Geometry-Dependent Approximation for Non-Monotone $k$-Submodular Maximization
We study nonnegative, non-monotone $k$-submodular maximization with $k\ge2$ labels under support constraints, and show how the certified approximation coefficient improves as the support region…