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针对目标函数为非凸截断核范数的张量补全问题,在加速临近梯度算法的基础上,提出了一种具有随机思想的加速临近梯度算法.主要思想是随机选取张量的一个模进行展开,并更新该模对应的变量,这大大减少了张量补全的CPU时间.图像修复的数值实验表明,新算法提高了计算效率.
Abstract：For the tensor completion problem with a non-convex truncated nuclear norm as the objective function,an accelerated proximal gradient(APG)algorithm with a randomization strategy is proposed,building upon the standard APG method. The main idea is to randomly select one mode of the tensor for unfolding and update the variables corresponding to this mode,which significantly reduces the CPU times required for tensor completion.Numerical experiments on image image restoration show that the new algorithm enhances computational efficiency.
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基本信息:
中图分类号:O183.2;TP391.41
引用信息:
[1]任荣,王川龙.基于截断核范数的张量补全随机算法[J].太原师范学院学报(自然科学版),2025,24(04):1-8.
基金信息:
国家自然科学基金项目（12371381）; 山西省研究生教育创新项目（2025XS489）
2025-12-25
2025-12-25