BriefGPT.xyz
May, 2014
用于最大后验估计的可扩展半定松弛方法
Scalable Semidefinite Relaxation for Maximum A Posterior Estimation
HTML
PDF
Qixing Huang, Yuxin Chen, Leonidas Guibas
TL;DR
本文介绍了一种基于新的半定松弛形式(SDR)的最大后验推理方法,用于在大规模马尔科夫随机场上求解 MAP 问题,并采用 SDPAD-LR 交替方向乘法加速算法,取得了显著的可扩展性和计算效率。
Abstract
Maximum a posteriori (MAP) inference over discrete
markov random fields
is a fundamental task spanning a wide spectrum of real-world applications, which is known to be
np-hard
for general graphs. In this paper, w
→