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Function volume() in the Stokes package

Robin K. S. Hankin

volume
function (n) 
{
    as.kform(seq_len(n))
}

To cite the stokes package in publications, please use Hankin (2022). Spivak (1965), in a memorable passage, states:

The volume element

The fact that dimΛn(Rn)=1 is probably not new to you, since det is often defined as the unique element ωΛn(Rn) such that ω(e1,,en)=1. For a general vector space V there is no extra criterion of this sort to distinguish a particular ωΛn(Rn). Suppose, however, that an inner product T for V is given. If v1,,vn and w1,,wn are two bases which are orthonormal with respect to T, and the matrix A=(aij) is defined by wi=nj=1aijvj, then

δij=T(wi,wj)=nk,l=1aikajlT(vk,vl)=nk=1aikajk.

In other words, if AT denotes the transpose of the matrix A, then we have AAT=I, so detA=±1. It follows from Theorem 4-6 [see vignette det.Rmd] that if ωΛn(V) satisfies ω(v1,,vn)=±1, then ω(w1,,wn)=±1. If an orientation μ for V has also been given, it follows that there is a unique ωΛn(V) such that ω(v1,,vn)=1 whenever v1,,vn is an orthornormal basis such that [v1,,vn]=μ. This unique ω is called the volume element of V, determined by the inner product T and orientation μ. Note that det is the volume element of Rn determined by the usual inner product and usual orientation, and that |det(v1,,vn)| is the volume of the parallelepiped spanned by the line segments from 0 to each of v1,,vn.

- Michael Spivak, 1969 (Calculus on Manifolds, Perseus books). Page 83

In the stokes package, function volume(n) returns the volume element on the usual basis, that is, ω(e1,,en). We will take n=7 as an example:

(V <- volume(7))
## An alternating linear map from V^7 to R with V=R^7:
##                    val
##  1 2 3 4 5 6 7  =    1

We can verify Spivak’s reasoning as follows:

f <- as.function(V)
f(diag(7))
## [1] 1

Above, we see that ω(e1,,en)=1. To verify that V(v1,,vn)=det(A), where Aij=(vi)j:

A <- matrix(rnorm(49),7,7)
LHS <- f(A)
RHS <- det(A)
c(LHS=LHS,RHS=RHS,diff=LHS-RHS)
##      LHS      RHS     diff 
## 1.770074 1.770074 0.000000

Now we create w1,,wn, another orthonormal set. We may verify by generating a random orthogonal matrix and permuting its rows:

M1 <- qr.Q(qr(matrix(rnorm(49),7,7)))  # M1: a random orthogonal matrix
M2 <- M1[c(2,1,3,4,5,6,7),]            # M2: (odd) permutation of rows of M1
c(f(M1),f(M2))
## [1]  1 -1

Above we see that the volume element of M1 and M2 are ±1 to within numerical precision.

References

Hankin, R. K. S. 2022. “Stokes’s Theorem in R.” arXiv. https://doi.org/10.48550/ARXIV.2210.17008.
Spivak, M. 1965. Calculus on Manifolds. Addison-Wesley.