两种新的Toeplitz矩阵填充加速临近梯度算法

Two new accelerated proximal gradient algorithms for Toeplitz matrix completion

本文提出了两种改进的Toeplitz矩阵填充加速临近梯度算法,使迭代矩阵每一步都保持Toeplitz结构,从而降低了奇异值分解时间。在理论上,证明了新算法在一些合理条件下的收敛性。同时,数值实验表明,在Toeplitz矩阵填充问题中,新算法比加速临近梯度(APG)算法在时间上有明显减少。

In this paper,we propose two modified accelerated proximal gradient algorithms for Toeplitz matrix completion in which the iterative matrices keep the Toeplitz structure in each step to decrease SvD times.Furthermore,we prove the convergence of the new algorithms under some reasonal conditions.Finally,numerical experiments show that the new algorithms are much more effective than the accelerated proximal gradient(APG)algorithm for Toeplitz matrix completion in CPU times.