权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

EAGER- DynamicData: Novel Approaches for Optimization, Control, and Learning in Distributed Networks

EAGER-DynamicData：分布式网络中优化、控制和学习的新方法

基本信息

批准号：
1462397
负责人：
Wotao Yin
金额：
$ 20万
依托单位：
University of California-Los Angeles
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-15 至 2017-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462397&HistoricalAwards=false
关键词：
EAGER DynamicData Novel Approaches Optimization

项目摘要

Recent advances in sensor and robotics technology have led to large networks of coordinated mobile platforms that can perform automatic sensing, mapping, learning, and control tasks. While the remote tasks can be controlled by the ground center via long-range communication links, the links are expensive and suffer long delays. This project develops computational methods that will significant improve the ability for the remote agents to coordinate locally with one another for tasks such as recognize and navigate around obstacles, reconstruct signals, and learn new control policies from a large amount of multi-modal sensor data, all done with little or no communication to a center. The proposed approach is radically different from the current state of the art. It also includes educational components such as courses, seminars, and initiatives for under-represented minority and women.The proposed work is a set of novel algorithms for a variety of computing problems in multi-agent networks, for problems involving extremely large-scale distributed datasets and high complexity objectives, and with greater accuracy at rates that are provably faster than existing methods. Very promising preliminary results have been obtained. The proposed project includes (a) a new approach to integrate multi-agent coordination with problems arising in optimization, game theory, control, and learning, into systems of equations, inclusions, or variational inequalities; (b) novel operator splitting methods that lead to decentralized numerical solutions of these systems, which scale to new levels of size, complexity, and diversity; (c) stochastic approximation techniques to deal with the imminent "distributed data deluge", along with accelerations techniques based on variance reduction, importance sampling, and asynchronous parallelization; and (d) a set of open-source software products for optimization, control, and learning problems with dynamic and large-scale data, along with a comprehensive evaluation plan. The contributions of the project is a unified framework in parts (a) and (b) above, which enable the decentralized numerical solutions at new levels of speed, complexity, diversity, and resilience. In order to achieve the goals, substantial resources will be devoted to both mathematical research and engineering challenges.

传感器和机器人技术的最新进展已经导致了协调的移动的平台的大型网络，其可以执行自动感测、映射、学习和控制任务。虽然远程任务可以通过远程通信链路由地面中心控制，但这些链路价格昂贵，而且延迟时间长。该项目开发的计算方法将显着提高远程代理在本地相互协调的能力，以完成诸如识别和绕过障碍物，重建信号以及从大量多模态传感器数据中学习新的控制策略等任务，所有这些都是在很少或根本没有与中心通信的情况下完成的。所提出的方法是从根本上不同于目前的艺术状态。它还包括教育组件，如课程，研讨会，并为代表性不足的少数民族和妇女的倡议。所提出的工作是一套新颖的算法，在多智能体网络中的各种计算问题，涉及极大规模的分布式数据集和高复杂性的目标，并且以比现有方法更快的速度具有更高的准确性。已经取得了非常有希望的初步结果。拟议的项目包括：（a）一种新的方法，将多智能体协调与优化、博弈论、控制和学习中出现的问题整合到方程、包含或变分不等式系统中;（B）新颖的算子分裂方法，导致这些系统的分散数值解，这些系统的规模、复杂性和多样性达到新的水平;（c）处理即将到来的“分布式数据泛滥”的随机近似技术，沿着基于方差减少、重要性抽样和异步并行化的加速技术;以及（d）一套开放源代码软件产品，用于优化、控制和学习具有动态和大规模数据的问题，以及沿着综合评估计划。该项目的贡献是在上述部分（a）和（B）中建立了一个统一的框架，使分散的数值解决方案能够达到新的速度，复杂性，多样性和弹性水平。为了实现这些目标，大量资源将用于数学研究和工程挑战。