用极分解实现奇异值软阈值化，加速GPU计算。
Singular value soft-thresholding via the polar decomposition
- 通过极分解重构奇异值软阈值化算法
- 在GPU上比传统SVD方法提速显著
- 适合对精度要求不高的实时场景
奇异值软阈值化可通过转化为矩阵极分解来计算，从而利用适合GPU的极分解算法。实验表明，在GPU上相比传统的SVD方法有显著加速。我们暂未研究其鲁棒性，但注意到符号函数的不连续性意味着该方法可能仅适用于低精度应用场景。
原文摘要 · Abstract (English)
Singular value soft-thresholding can be computed via a reduction to the matrix polar decomposition, which allows one to exploit GPU-friendly algorithms for computing the polar decomposition. Empirically, there is a significant speed-up on GPUs compared to the standard approach using the SVD. We leave the investigation of robustness to future work, but note that due to the discontinuous nature of the sign function, the reduction to the polar decomposition is likely only suitable for low-accuracy applications.
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