Editing Tensor (section)

== Properties ==
Assuming a [[basis of a vector space|basis]] of a real vector space, e.g., a coordinate frame in the ambient space, a tensor can be represented as an organized [[Array data structure#Multidimensional arrays|multidimensional array]] of numerical values with respect to this specific basis. Changing the basis transforms the values in the array in a characteristic way that allows to ''define'' tensors as objects adhering to this transformational behavior. For example, there are invariants of tensors that must be preserved under any change of the basis, thereby making only certain multidimensional arrays of numbers a [[Tensor#Holors|tensor.]] Compare this to the array representing <math> \varepsilon_{ijk}</math> not being a tensor, for the sign change under transformations changing the orientation.

Because the components of vectors and their duals transform differently under the change of their dual bases, there is a [[covariant transformation|covariant and/or contravariant transformation law]] that relates the arrays, which represent the tensor with respect to one basis and that with respect to the other one. The numbers of, respectively, {{nowrap|vectors: {{mvar|n}}}} ([[covariance and contravariance of vectors|contravariant]] indices) and dual {{nowrap|vectors: {{mvar|m}}}} ([[covariance and contravariance of vectors|covariant]] indices) in the input and output of a tensor determine the ''type'' (or ''valence'') of the tensor, a pair of natural numbers {{nowrap|{{math|(''n'', ''m'')}}}}, which determine the precise form of the transformation law. The ''{{vanchor|order}}'' of a tensor is the sum of these two numbers.

The order (also ''degree'' or ''{{vanchor|rank}}'') of a tensor is thus the sum of the orders of its arguments plus the order of the resulting tensor. This is also the dimensionality of the array of numbers needed to represent the tensor with respect to a specific basis, or equivalently, the number of indices needed to label each component in that array.  For example, in a fixed basis, a standard linear map that maps a vector to a vector, is represented by a matrix (a 2-dimensional array), and therefore is a 2nd-order tensor.  A simple vector can be represented as a 1-dimensional array, and is therefore a 1st-order tensor.  Scalars are simple numbers and are thus 0th-order tensors. This way the tensor representing the scalar product, taking two vectors and resulting in a scalar has order {{math|2 + 0 {{=}} 2}}, the same as the stress tensor, taking one vector and returning another {{math|1 + 1 {{=}} 2}}. The {{nowrap|<math> \varepsilon_{ijk}</math>-symbol,}} mapping two vectors to one vector, would have order {{math|2 + 1 {{=}} 3.}}

The collection of tensors on a vector space and its dual forms a [[tensor algebra]], which allows products of arbitrary tensors. Simple applications of tensors of order {{math|2}}, which can be represented as a square matrix, can be solved by clever arrangement of transposed vectors and by applying the rules of matrix multiplication, but the tensor product should not be confused with this.