The set of all linear forms over a vector space forms another vector space called the dual space.
For the typical case of a linear form over , the form can be seen just as a row vector with n elements, the full form being specified by the value of each of the basis vectors.
The dual space of a vector space , sometimes denoted , is the vector space of all linear forms over with the obvious addition and scalar multiplication operations defined.
Since a linear form is completely determined by how it acts on a basis, and since for each basis element it is specified by a scalar, at least in finite dimension, the dimension of the dual space is the same as the , and so they are isomorphic because all vector spaces of the same dimension on a given field are isomorphic, and so the dual is quite a boring concept in the context of finite dimension.
One place where duals are different from the non-duals however is when dealing with tensors, because they transform differently than vectors from the base space .
Dual vectors are the members of a dual space.
In the context of tensors , we use raised indices to refer to members of the dual basis vs the underlying basis:
The dual basis vectors are defined to "pick the corresponding coordinate" out of elements of V. E.g.:
By expanding into the basis, we can put this more succinctly with the Kronecker delta as:
Note that in Einstein notation, the components of a dual vector have lower indices. This works well with the upper case indices of the dual vectors, allowing us to write a dual vector as:
In the context of quantum mechanics, the bra notation is also used for dual vectors.

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In mathematics, particularly in the context of linear algebra and functional analysis, a **linear form** (or linear functional) is a specific type of function that satisfies certain properties. Here are the main characteristics: 1. **Linear Transformation**: A linear form maps a vector from a vector space to a scalar.