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 36
Recovering linear images of sparse signals from indirect observations
In this paper, we develop and analyze techniques for recovering a linear image $Bx$ of an unknown signal $x$ from indirect noisy observation $ω=Ax+ξ$.Fast PAC Global Optimization via Restarted Langevin: Exploration, Exploitation, and Degenerate Cooling
We study the computational effort required for global optimization of a smooth, possibly nonconvex objective $Γ:\mathbb{R}^d\to\mathbb{R}$.Algorithmic Optimality Guarantees for Nonsmooth $H_\infty$ Output-Feedback Policy Search
We study continuous-time full-order dynamic output-feedback $H_\infty$ policy search, a nonconvex and nonsmooth problem.Robust conditional dimension reduction for dissimilarity data
Conditional dimension reduction (cDR) learns low-dimensional latent coordinates while accounting for observed covariates that represent known sources of variation in the data.Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization
Large language models (LLMs) are increasingly used to formulate optimization models from natural-language problem descriptions, yet realistic operations research (OR) requests are often incomplete…Quantum lower bounds for convex optimization and real matrix-vector query problems
We (the author and the AI systems that did the heavy lifting) show that the quantum query complexity of minimizing a convex function over a convex subset of $\mathbb{R}^n$ with evaluation and…Belief Updating without Complete Trust
We represent a non-Bayesian agent as one who does not completely trust the information they receive.Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing
Artificial intelligence (AI) now supports investment workflows from data and prediction through research, portfolios, execution, and tool use.Why Better Models Can Create Riskier Systems: Evidence from LLM Agents in Financial Markets
Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring.La Agente Óptima: Towards Agentic Self-Driving Laboratories
Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery.Recommender System as Slow and Fast Thinkers
Sequential recommendation models are foundational to modern personalized services, yet their effectiveness varies substantially across heterogeneous user environments.Data-Adaptive Rerandomization for 2K Factorial Designs
Factorial designs allow simultaneous estimation of multiple main effects and interactions, but covariate imbalance can substantially reduce estimation precision.Learning Parametric Monotone Games
We study the problem of learning from data a parametric Nash equilibrium (NE) problem that is monotone (or strongly monotone) for all parameter values.Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization
Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates.Competitive Market Behavior of LLMs
Large language models (LLMs) are increasingly deployed as economic agents, yet there is little evidence whether LLM agents are suited for participating in market mechanisms designed for humans, and…Prediction-Assisted Pricing and Admission for LLM APIs with Stochastic Token Consumption
An LLM application often sells or internally allocates several service products: a small or premium model, a short or long token cap, and possibly multiple posted prices.How outside options are incorporated into payoff distributions
This paper examines how outside options are incorporated into payoff distributions in games with coalition structures.Extending Subsampling to Sequential Stopping
Fixed-width sequential stopping rules terminate a stochastic simulation once an estimated confidence interval reaches a prescribed width.AI and the Economy: An Economic Examination of Production, Distribution, Firms, Labor, and Welfare
Artificial Intelligence (AI) is rapidly transforming economic systems by altering production processes, labor markets, and the structure of firms and industries.Drift-Aware LLM Routing with Sparse Contexts and Shared Budgets
A multi-model language service must route each request while preserving workload-level budgets for compute, latency, memory, or monetary cost.