惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

J
Java Code Geeks
量子位
MongoDB | Blog
MongoDB | Blog
N
Netflix TechBlog - Medium
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
B
Blog
A
About on SuperTechFans
腾讯CDC
The GitHub Blog
The GitHub Blog
云风的 BLOG
云风的 BLOG
雷峰网
雷峰网
Last Week in AI
Last Week in AI
H
Help Net Security
WordPress大学
WordPress大学
博客园 - 司徒正美
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
H
Hackread – Cybersecurity News, Data Breaches, AI and More
T
Tailwind CSS Blog
博客园 - 【当耐特】
S
SegmentFault 最新的问题
美团技术团队
M
MIT News - Artificial intelligence
L
LangChain Blog
博客园 - 聂微东

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Stochastic Control Problems Motivated by Sailboat Traject...
[Submitted on 4 Apr 2024 (v1), last revised 30 Aug 2026 (this ve · 2024-04-05 · via math.PR updates on arXiv.org

View PDF HTML (experimental)

Abstract:We develop a mathematical model for sailboat navigation that captures the essential features of the problem and can provide insights that might not be available otherwise. In our model, the motion of the sailboat, which would travel at speed $v>0$ in a constant wind, is the solution of a system of two stochastic differential equations driven by a Brownian motion on a circle with speed $\sigma > 0$. We formulate two stochastic control/optimal switching problems, in which the objective is to reach a circular upwind target of radius $\eta \geq 0$ as quickly as possible. In the first problem, there is a tacking cost $c > 0$, so this is an impulse control problems, while in the second problem, we assume that $c=0$ and singular controls are needed. We establish the viability of both models (assuming that $\eta > 0$ in the second model), that is, their value functions are finite, and we obtain bounds on these value functions related to the parameters of the problem. In the second problem, since the state equation for the optimally controlled motion has discontinuous coefficients and is driven by a degenerate diffusion, standard results on existence and uniqueness of strong solutions do not apply: we provide a proof via the Yamada-Watanabe argument.

Submission history

From: Carlo Ciccarella [view email]
[v1] Thu, 4 Apr 2024 19:34:48 UTC (1,723 KB)
[v2] Wed, 9 Apr 2025 13:20:16 UTC (601 KB)
[v3] Wed, 24 Dec 2025 11:07:25 UTC (542 KB)
[v4] Sun, 30 Aug 2026 12:53:43 UTC (711 KB)