论文标题
随机梯度下降非常适合最小化压力
Stochastic Gradient Descent Works Really Well for Stress Minimization
论文作者
论文摘要
应力最小化是最佳研究的力量图形布局方法之一，因为它可靠地产生高质量的布局。因此，令人惊讶的是，基于随机梯度下降（Zheng，Pawar and Goodman，TVCG 2019）的新方法可以改善基于大型化的最新方法。我们提供了实验证据，表明新方法实际上并没有产生更好的布局，但是它仍然是首选的，因为它在初始化较差的情况下更简单和强大。
Stress minimization is among the best studied force-directed graph layout methods because it reliably yields high-quality layouts. It thus comes as a surprise that a novel approach based on stochastic gradient descent (Zheng, Pawar and Goodman, TVCG 2019) is claimed to improve on state-of-the-art approaches based on majorization. We present experimental evidence that the new approach does not actually yield better layouts, but that it is still to be preferred because it is simpler and robust against poor initialization.
