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 24
Achieving an $O(1/N)$ Optimality Gap in Average-Reward Weakly-Coupled MDPs
We study average-reward weakly-coupled Markov decision processes (WCMDPs), where a WCMDP consists of $N$ smaller MDPs, called arms, that share multiple per-step budget constraints.Multi-Agent Flow Matching with Decoupled Generative Guidance
Generative modeling is widely used for producing diverse objects from complex, multimodal distributions.Lower Bounds for Linear-Oracle Online Learning
Can a constant number of linear minimizations per round improve on the $T^{3/4}$ regret rate of online Frank-Wolfe on general convex sets?Unified Optimality Conditions for Stochastic Optimal Control in the Rough Path and Itô Frameworks
Stochastic differential equations (SDEs) can be studied via Itô calculus and rough path theory.TACIT: Optimization Models that Learn from Their Mistakes
Real-world optimization problems are difficult to model accurately because many objectives and constraints reside in domain experts' tacit knowledge, making them hard to formalize.A Parameter-Free Zeroth-Order Method with Covariance Matrix Adaptation and Effective Dimension
Zeroth-order optimization methods are essential for solving black-box problems where gradient information is unavailable or expensive to compute.Online Stochastic Allocation with Increasing Returns
Online resource allocation is a fundamental problem in revenue management, sponsored search, and platform operations.Efficient Reasoning via Constrained Optimization in Latent Space
Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they still suffer from overthinking, generating redundant reasoning steps which incur substantial token consumption.Riemannian Difference-of-Convex Optimization for K-Means Clustering
K-means is a widely adopted clustering approach in signal processing and machine learning.On the Relation Between Interval Regret and Dynamic Regret
Non-stationary online learning has attracted much attention in recent years, as static regret is insufficient to guide algorithm design in changing environments.On Parameter Symmetries and Conservation Laws in Gradient Flow
Parameter space symmetries and conservation laws play an important role in understanding the loss landscapes and implicit biases of neural networks.Finite-Time Concentration and Convergence Rates for Projected Two-Time-Scale Stochastic Approximation with Markov Noise
We study finite-time concentration and convergence rates for projected two-time-scale stochastic approximation driven by a controlled Markov chain.ProofLoom: Proof-Obligation-Driven Theory Construction for Autoformalizing Research-Level Stochastic Optimization
Formalizing research-level stochastic optimization in Lean requires both an algorithm model and domain theory connecting foundational libraries to convergence proofs.Universality and Generalization of Causal Transformers Across Context Lengths
Long contexts are central to modern transformer systems, but most expressivity results choose a different network for each fixed sequence length.Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control and Multi-Agent Reinforcement Learning Approach
Successive weaving segments (SWSs) on urban expressways are bottlenecks prone to recurrent congestion and collisions, requiring fine-grained active traffic management (ATM).Convex Optimization Is Free When Accuracy Is Expensive
This paper studies convex optimization when the gradient cannot be evaluated exactly, but only approximated by a hierarchy of algorithms whose compute grows like $δ^{-γ}$ in the accuracy $δ$.An analysis of Mirror-Descent Soft Actor-Critic
Soft Actor-Critic (SAC) is widely used for entropy-regularised reinforcement learning with continuous action spaces, and practical implementations perform only a few actor steps towards an evolving…Learned Preconditioning for a Primal-Dual Interior-Point Method
Interior-point methods (IPMs) are among the most widely used algorithms for constrained optimization, yet their Newton-based search directions require costly second-order information and large…Preferent Compression Bounds Are Tight
The lack of rigorous safety and performance certificates remains a key bottleneck to the deployment of modern learning-based methods.Dual-Anchor Acceleration Is Near-Optimal for Stochastic Monotone Root-Finding
Among distinct optimal acceleration mechanisms for deterministic monotone root-finding problems and fixed-point problems, dual-anchoring has recently been shown to admit a more robust direct…