报告题目: A Loosely Coupled Distributed Singular Value Decomposition Algorithm for Large Dense Matrices

报告人:方晟(牛津大学)

时间：2014年10月23日（星期四）16：00-17：00

地点：理科楼数学系A304

摘要: The emergence of novel computational architectures, such as multi-core processors and graphic cards, makes it increasingly important to develop new highly parallel methods for big data computations that meet the loosely coupled computing requirements(low communication costs and low synchronicity) and harness the available computational power. Singular Value Decomposition(SVD) of large dense matrix plays an essential role in a variety of models and algorithms in data mining and machine learning. The calculations of SVD always form the computational bottleneck that dominates the computational costs in these models and algorithms. We will introduce a new methodology for the calculations of SVD of large and dense matrices that satisfies the loosely coupled computing requirements. We will start with a geometric motivation of our approach and a discussion of how it differs from other approaches. The convergence analysis is split into the derivation of bounds on the local error occurring at individual nodes, and bounds on the global error accumulation. Several variants of the algorithm will be compared, and numerical experiments and applications in matrix optimisation problems will also be discussed.

报告人简介：方晟博士, 2010年毕业于清华大学数学科学系,获学士学位. 2010年9月去牛津大学数学学院读博, 师从Raphael Hauser教授, 研究兴趣为大规模矩阵计算、数值优化和机器学习，在大规模矩阵计算和机器学习方面有很好的工作。

联系人：张立平