RLC Circuits-Based Distributed Mirror Descent Method
نویسندگان
چکیده
منابع مشابه
Distributed Mirror Descent over Directed Graphs
In this paper, we propose Distributed Mirror Descent (DMD) algorithm for constrained convex optimization problems on a (strongly-)connected multi-agent network. We assume that each agent has a private objective function and a constraint set. The proposed DMD algorithm employs a locally designed Bregman distance function at each agent, and thus can be viewed as a generalization of the well-known...
متن کاملMirror Descent Based Database Privacy
In this paper, we focus on the problem of private database release in the interactive setting: a trusted database curator receives queries in an online manner for which it needs to respond with accurate but privacy preserving answers. To this end, we generalize the IDC (Iterative Database Construction) framework of [15,13] that maintains a differentially private artificial dataset and answers i...
متن کاملData-Distributed Weighted Majority and Online Mirror Descent
In this paper, we focus on the question of the extent to which online learning can benefit from distributed computing. We focus on the setting in whichN agents online-learn cooperatively, where each agent only has access to its own data. We propose a generic datadistributed online learning meta-algorithm. We then introduce the Distributed Weighted Majority and Distributed Online Mirror Descent ...
متن کاملComposite Objective Mirror Descent
We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known firstorder algorithms, such as the projected gradient method, mirror descent, and forwardbackward splitting, our method yields new analysis and algorithms. We also derive specific instantiations of our method for commonly use...
متن کاملValidation analysis of mirror descent stochastic approximation method
The main goal of this paper is to develop accuracy estimates for stochastic programming problems by employing stochastic approximation (SA) type algorithms. To this end we show that while running a Mirror Descent Stochastic Approximation procedure one can compute, with a small additional effort, lower and upper statistical bounds for the optimal objective value. We demonstrate that for a certai...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Control Systems Letters
سال: 2020
ISSN: 2475-1456
DOI: 10.1109/lcsys.2020.2972908