Constructing low-rank Tucker tensor approximations using generalized completion

IF 0.5 4区 数学 Q4 MATHEMATICS, APPLIED
Sergey Petrov
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引用次数: 0

Abstract

The projected gradient method for matrix completion is generalized towards the higher-dimensional case of low-rank Tucker tensors. It is shown that an operation order rearrangement in the common projected gradient approach provides a complexity improvement. An even better algorithm complexity can be obtained by replacing the completion operator by a general operator that satisfies restricted isometry property; however, such a replacement transforms the completion algorithm into an approximation algorithm.
利用广义补全构建低阶塔克张量近似值
针对低阶塔克张量的高维情况,对矩阵补全的投影梯度法进行了推广。结果表明,普通投影梯度法中的运算顺序重排可以提高复杂度。用一个满足受限等距性质的一般算子代替补全算子,可以获得更好的算法复杂度;但是,这种替换会将补全算法转化为近似算法。
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来源期刊
CiteScore
1.40
自引率
16.70%
发文量
31
审稿时长
>12 weeks
期刊介绍: The Russian Journal of Numerical Analysis and Mathematical Modelling, published bimonthly, provides English translations of selected new original Russian papers on the theoretical aspects of numerical analysis and the application of mathematical methods to simulation and modelling. The editorial board, consisting of the most prominent Russian scientists in numerical analysis and mathematical modelling, selects papers on the basis of their high scientific standard, innovative approach and topical interest. Topics: -numerical analysis- numerical linear algebra- finite element methods for PDEs- iterative methods- Monte-Carlo methods- mathematical modelling and numerical simulation in geophysical hydrodynamics, immunology and medicine, fluid mechanics and electrodynamics, geosciences.
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