Matrix completion under interval uncertainty
将区间不确定性下的矩阵补全转化为带元素级盒约束的问题，提出一种高效的交替方向并行坐标下降法，在图像修复基准上信噪比优于已知方法，并在5分钟内处理含1亿条推荐的协同过滤实例。
Matrix completion under interval uncertainty can be cast as matrix completion with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes.