gives the rank of tensor.
Details and Options
- TensorRank accepts any type of tensor, either symbolic or explicit, including any type of array.
- On explicit rectangular arrays of scalars, TensorRank coincides with ArrayDepth. On symbolic arrays, TensorRank stays unevaluated unless the array has been assigned a rank through any form of assumption.
Examplesopen allclose all
Basic Examples (1)
Properties & Relations (2)
Possible Issues (3)
TensorRank can obtain some information contextually. Expressions without tensor properties inside numeric functions, arrays, or derivatives are considered scalars:
TensorRank does not check for dimensions homogeneity, only rank homogeneity: