In mathematics, a bilinear map is a function combining elements of two vector spaces to yield an element of a third vector space, and is linear in each of its arguments. Matrix multiplication is an example.
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Definition[edit]
Vector spaces[edit]
Let V, W and X be three vector spaces over the same base field F. A bilinear map is a function
 B : V × W → X
such that for any w in W the map
 v ↦ B(v, w)
is a linear map from V to X, and for any v in V the map
 w ↦ B(v, w)
is a linear map from W to X.
In other words, when we hold the first entry of the bilinear map fixed while letting the second entry vary, the result is a linear operator, and similarly for when we hold the second entry fixed.
If V = W and we have B(v, w) = B(w, v) for all v, w in V, then we say that B is symmetric.
The case where X is the base field F, and we have a bilinear form, is particularly useful (see for example scalar product, inner product and quadratic form).
Modules[edit]
The definition works without any changes if instead of vector spaces over a field F, we use modules over a commutative ring R. It generalizes to nary functions, where the proper term is multilinear.
For noncommutative rings R and S, a left Rmodule M and a right Smodule N, a bilinear map is a map B : M × N → T with T an (R, S)bimodule, and for which any n in N, m ↦ B(m, n) is an Rmodule homomorphism, and for any m in M, n ↦ B(m, n) is an Smodule homomorphism. This satisfies
 B(r ⋅ m, n) = r ⋅ B(m, n)
 B(m, n ⋅ s) = B(m, n) ⋅ s
for all m in M, n in N, r in R and s in S, as well as B being additive in each argument.
Properties[edit]
A first immediate consequence of the definition is that B(v, w) = 0_{X} whenever v = 0_{V} or w = 0_{W}. This may be seen by writing the zero vector 0_{X} as 0 ⋅ 0_{X} (and similarly for 0_{W}) and moving the scalar 0 "outside", in front of B, by linearity.
The set L(V, W; X) of all bilinear maps is a linear subspace of the space (viz. vector space, module) of all maps from V × W into X.
If V, W, X are finitedimensional, then so is L(V, W; X). For X = F, i.e. bilinear forms, the dimension of this space is dim V × dim W (while the space L(V × W; F) of linear forms is of dimension dim V + dim W). To see this, choose a basis for V and W; then each bilinear map can be uniquely represented by the matrix B(e_{i}, f_{j}), and vice versa. Now, if X is a space of higher dimension, we obviously have dim L(V, W; X) = dim V × dim W × dim X.
Examples[edit]
 Matrix multiplication is a bilinear map M(m, n) × M(n, p) → M(m, p).
 If a vector space V over the real numbers R carries an inner product, then the inner product is a bilinear map V × V → R.
 In general, for a vector space V over a field F, a bilinear form on V is the same as a bilinear map V × V → F.
 If V is a vector space with dual space V^{∗}, then the application operator, b(f, v) = f(v) is a bilinear map from V^{∗} × V to the base field.
 Let V and W be vector spaces over the same base field F. If f is a member of V^{∗} and g a member of W^{∗}, then b(v, w) = f(v)g(w) defines a bilinear map V × W → F.
 The cross product in R^{3} is a bilinear map R^{3} × R^{3} → R^{3}.
 Let B : V × W → X be a bilinear map, and L : U → W be a linear map, then (v, u) ↦ B(v, Lu) is a bilinear map on V × U.
See also[edit]
External links[edit]
 Hazewinkel, Michiel, ed. (2001), "Bilinear mapping", Encyclopedia of Mathematics, Springer, ISBN 9781556080104

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