论文标题
为3D反卷积的滑动弗兰克 - 沃尔夫求解器增强
Boosting the Sliding Frank-Wolfe solver for 3D deconvolution
论文作者
论文摘要
在无网稀疏优化的背景下，最近引入的滑动弗兰克·沃尔夫算法显示出有趣的分析和实用性。然而，在大型数据中的应用是在计算上很重的大数据，例如3D反卷积。在本文中，我们调查了一种利用这种负担的策略，以使该方法更容易进行3D反卷积。我们表明，增强的SFW可以在大幅减少的时间内实现相同的结果。
In the context of gridless sparse optimization, the Sliding Frank Wolfe algorithm recently introduced has shown interesting analytical and practical properties. Nevertheless, is application to large data, such as in the case of 3D deconvolution, is computationally heavy. In this paper, we investigate a strategy for leveraging this burden, in order to make this method more tractable for 3D deconvolution. We show that a boosted SFW can achieve the same results in a significantly reduced amount of time.
