A Some Tensor Definitions and Properties

Neural Information Processing Systems 

We present in this section fairly standard notation and definitions regarding tensors, e.g., see [ Chapter 3 of [30], that we use throughout the paper. Note that when A is a matrix, this corresponds to the row-major vectorization of A . Lemma 3. Now assume that (6) holds for 1, 2,...,k 1. For k, we let H " b The proof of Theorem 1 follows from Theorem 2.8 in [ Finally, Algorithm 2 itself ensures AS.4 in Hence, by Theorem 2.8 of [44], the result is guaranteed. In Algorithm 3, we present a detailed pseudo-code for our actual implementation of TNT.

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