发表机构
Dipartimento di Ingegneria Industriale, Università degli studi di Firenze（佛罗伦萨大学工业工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出DOFFO_TR无目标函数的去中心化信赖域优化方法，兼容一阶、二阶模型，无需智能体共享函数值与梯度，迭代复杂度有理论保证，性能效率权衡良好且通信开销适中。
AI 中文摘要
本文提出了一种新型无目标函数的信赖域方法，用于解决去中心化网络上的优化问题。与通常依赖步长调整的传统方法不同，我们的框架采用无函数信赖域流程，可自适应选择步长。我们的方法兼容一阶和二阶模型，无需智能体间共享局部函数值与梯度，从而提升了隐私性与计算效率。理论层面，我们建立了可证明的迭代复杂度保证，部分变体的复杂度与经典集中式信赖域方法相当。数值评估表明，我们的方法在性能与效率间实现了良好权衡，与文献中的现有方法相比仅需中等通信开销。
英文摘要
In this paper, we propose a novel objective function-free trust-region method designed to solve optimization problems over decentralized networks. Unlike traditional approaches that often rely on stepsize tuning, our framework employs a function-free trust-region procedure that enables adaptive selection of the step length. Our approach accommodates first- and second-order models and eliminates the need to share local function values and gradients among agents, thereby enhancing privacy and computational efficiency. On the theoretical side, we establish provable iteration complexity guarantees that, for some variants, match those established for classical centralized trust-region methods. Numerical evaluations demonstrate that our approach achieves a favorable trade-off between performance and efficiency, requiring only moderate communication overhead compared to state-of-the-art methods in the literature.
