Inner product computations on manifolds

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In summary, the inner product on a manifold can be computed by first converting the vectors to 1-forms and then using the metric to calculate the inner product. For a 1-form and a vector, the inner product is given by \langle \omega, X \rangle = \omega(X). The corresponding 1-form or covector for a vector X is denoted X^\flat and is given by X^\flat(Y) = g(X,Y), where g is the metric.
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FuzzyFungi
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Hi there! I have a pretty basic question about how to compute an inner product [tex]\left\langle\omega, X\right\rangle[/tex] on a manifold.

I understand that, if both arguments are vectors (or vector fields) and we're in euclidean space, the computation is exactly as if I were doing a dot product. However, if we're in a manifold (Lets say... On the surface of a unit sphere in [tex]\Re^3[/tex]) how would the computation be done?

What if the first argument is a 1-form?

From what I've read, I've found lots of helpful information concerning properties of inner products, their usefulness as metrics, and nice identities with them. But when it comes to finding the value of one, I am lost.

Thanks!
 
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My understanding has always been that the inner product of a 1-form and a vector is given by [tex]\langle \omega, X \rangle = \omega(X)[/tex]

For the inner product of two vectors, we first have to convert the first vector to a 1-form. If [tex]X[/tex] is a vector then the corresponding 1-form or covector is denoted [tex]X^\flat[/tex] and is given by [tex]X^\flat(Y) = g(X,Y)[/tex]. If the components of [tex]X[/tex] in some coordinate system are [tex]X^i[/tex] then the components of [tex]X^\flat[/tex] will be [tex]X_j = g_{ij}X^i[/tex]. I.e. [tex]X^\flat = X_j dx^j = g_{ij}X^i dx^j[/tex].

So [tex]\langle X,Y \rangle := \langle X^\flat,Y \rangle = X^\flat(Y) = g(X,Y)[/tex] and g is really our inner product of vectors.

I hope that makes sense
 

FAQ: Inner product computations on manifolds

What is an inner product in computing?

An inner product is a mathematical operation that takes two vectors and produces a scalar value. In computing, it is often used to measure the similarity or correlation between two vectors or to project one vector onto another.

How is an inner product calculated?

The inner product between two vectors is calculated by multiplying the corresponding elements of the vectors and then summing the results. For example, the inner product of vectors a and b can be calculated as aTb = a1*b1 + a2*b2 + ... + an*bn, where ai and bi represent the elements of a and b respectively.

What is the significance of computing inner products?

Computing inner products is important in various fields of computing, such as machine learning, data analysis, and signal processing. It allows us to measure the similarity between two vectors, which is useful for tasks like clustering, classification, and recommendation systems.

What is the difference between an inner product and a dot product?

The terms "inner product" and "dot product" are often used interchangeably, but technically, they are not the same. While both operations involve multiplying corresponding elements of two vectors and summing the results, an inner product can be defined for vectors in any vector space, while a dot product is specific to Euclidean space (i.e. vectors in 2D or 3D space).

Can an inner product be negative?

Yes, an inner product can be negative. The sign of the inner product depends on the angle between the two vectors. If the angle is acute, the inner product will be positive, and if the angle is obtuse, the inner product will be negative. If the vectors are orthogonal (i.e. perpendicular), the inner product will be zero.

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