We characterise the sensitivity of several additive tensor decompositions with respect to perturbations of the original tensor. These decompositions include canonical polyadic decompositions, block term decompositions, and sums of tree tensor networks. Our main result shows that the condition number of all these decompositions is invariant under Tucker compression. This result can dramatically speed up the computation of the condition number in practical applications. We give the example of an $265\times 371\times 7$ tensor of rank $3$ from a food science application whose condition number was computed in $6.9$ milliseconds by exploiting our new theorem, representing a speedup of four orders of magnitude over the previous state of the art.
翻译:我们把几种添加性抗拉分解物的敏感度与原抗拉的扰动有关,这些分解物包括罐状聚变分解物、轮廓分解物和树木抗拉网络的总和。我们的主要结果显示,所有这些分解物的状态在塔克压缩下是无变的。这个结果可以大大加快实际应用中条件数的计算。我们举了一个例子,从食品科学应用中提取了265美元乘数371乘数,7美元乘数为3美元,其条件数以6.9美元毫秒计算,这是利用我们的新定理器计算出来的,比以往的艺术状态加速了4个数量级。