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 12
Low-Bit Distributed Constrained Optimization with Application to Optimal Scheduling and Allocation
This paper proposes a distributed resource allocation algorithm in which each agent communicates only the sign of its local cost gradient, a single bit per directed link per iteration.Dynamic Programming for Value-at-Risk Markov Decision Processes in Continuous Spaces
Value-at-Risk (VaR) is a widely adopted quantile-based risk measure in practice, yet incorporating VaR objectives into Markov decision processes (VaR MDPs) remains a longstanding challenge.Accelerating Finite-Sum Bilevel Optimization with Fully First-Order Oracles
We study nonconvex--strongly-convex bilevel optimization with finite-sum objectives and fully first-order oracles.Comment on "The polarization hierarchy for polynomial optimization over convex bodies, with applications to nonnegative matrix rank"
We revisit the feasibility and convergence claims for the polarization hierarchy of Plávala, Ligthart and Gross [Linear Algebra Appl.\ 723 (2025) 15--32].Optimal Trading Filters and the Value of Predictive Signals
The value of a forecast depends on its accuracy, but also on its actionability.Gaussian Process Bandits with Paired Auxiliary Observations
Bayesian optimization (BO) is widely used for expensive black-box optimization, yet observation noise makes each evaluation less informative and slows posterior learning.ParetoChat: An LLM-Based Interface for Interactive Multi-Objective Optimization
Multi-objective optimization models are often deployed in settings where decision makers (DMs) must balance some conflicting criteria.Distributed Performance Boosting through Neural Control for Plug-and-Play Networks
We address the problem of improving the performance of pre-stabilized networked systems subject to plug-and-play (PnP) operations, namely additions or removals of subsystems.Untrained CNN and coefficient-space regularization for joint reconstruction in photoacoustic tomography
The joint reconstruction of the initial pressure and spatially varying sound speed in photoacoustic tomography from boundary measurements is considered.A general recipe for data-adaptive anytime-valid envelopes for iterative algorithms
Most convergence guarantees for iterative stochastic algorithms are stated at a fixed iteration count and do not justify stopping at a data-dependent time.On the possibilities and limitations of LLM-based multi-agent systems for multi-criteria decision-making in healthcare operations management
Multi-criteria models for healthcare operations need weights that express how stakeholders trade off patient waiting time, provider workload, and operating cost, but these weights are difficult to…From Alpha to Execution: Trading Horizons, Regimes, and Schedules under Concave Impact and Hurst Scaling
Classical execution models assume linear impact, diffusive prices, and an exogenous horizon.Polytope Sampling at the Speed of Isoperimetry
We give faster algorithms for sampling from the uniform distribution over a polytope.Parametric Resonance in Primal-Dual Learning for Slowly Non-Stationary Constrained MDPs
Primal-dual methods for constrained Markov decision processes are analyzed under a two-timescale assumption, with non-stationarity entering through two channels.Certifiably Correct Fréchet Means in Orbit Spaces
We study Fréchet means in orbit spaces of finite orthogonal group actions.When is a better LLM worth paying for? Stochastic Optimization with Switching Mixed-Fidelity Oracles
Large language models (LLMs) are increasingly used upstream of decision-making to clean data, detect label errors, and reconstruct missing values.Honesty is the Best Policy ... or is it? Demand Management for Probabilistic Service Options
To enable well-informed customer choices and redistribute demand across service options, providers can display probabilities regarding predicted service levels at the time of booking, effectively…Quantum-compatible Riemannian conjugate gradient for ground-state preparation
Riemannian optimization provides a natural geometric framework for designing quantum algorithms for ground-state preparation, most notably through quantum imaginary-time evolution.Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents
Risk-sensitive reinforcement learning (RL) addresses decision-making under uncertainty and adverse outcomes.The Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret
Many modern learning systems are used not to select a single best action, but to sample diverse high-quality outputs.