The stokes package
To cite the stokes package in publications please use
(Hankin 2022c). Ordinary differential
calculus may be formalized and generalized to arbitrary-dimensional
oriented manifolds using the exterior calculus. Here I show how the
stokes package furnishes functionality for working with the
exterior calculus, and provide numerical verification of a number of
theorems. Notation follows that of Spivak
(1965), and Hubbard and Hubbard
(2015).
Recall that a \(k\)-tensor is a
multilinear map \(S\colon
V^k\longrightarrow\mathbb{R}\), where \(V=\mathbb{R}^n\) is considered as a vector
space; Spivak denotes the space of multilinear maps as \(\mathcal{J}^k(V)\). Formally,
multilinearity means
\[
S{\left(v_1,\ldots,av_i,\ldots,v_k\right)} = a\cdot
S{\left(v_1,\ldots,v_i,\ldots,v_k\right)}
\]
and
\[
S{\left(v_1,\ldots,v_i+{v_i}',\ldots,v_k\right)}=S{\left(v_1,\ldots,v_i,\ldots,x_v\right)}+
S{\left(v_1,\ldots,{v_i}',\ldots,v_k\right)}.
\]
where \(v_i\in V\). If \(S\in\mathcal{J}^k(V)\) and \(T\in\mathcal{J}^l(V)\), then we may define
\(S\otimes T\in\mathcal{J}^{k+l}(V)\)
as
\[
S\otimes T{\left(v_1,\ldots,v_k,v_{k+1},\ldots,v_{k+l}\right)}=
S{\left(v_1,\ldots,v_k\right)}\cdot
T{\left(v_1,\ldots,v_l\right)}.
\]
Spivak observes that \(\mathcal{J}^k(V)\) is spanned by the \(n^k\) products of the form
\[
\phi_{i_1}\otimes\phi_{i_2}\otimes\cdots\otimes\phi_{i_k}\qquad
1\leq i_i,i_2,\ldots,i_k\leq n
\]
where \(v_1,\ldots,v_k\) is a basis
for \(V\) and \(\phi_i{\left(v_j\right)}=\delta_{ij}\); we
can therefore write
\[
S=\sum_{1\leq i_1,\ldots,i_k\leq n} a_{i_1\ldots i_k}
\phi_{i_1}\otimes\cdots\otimes\phi_{i_k}.
\]
The space spanned by such products has a natural representation in R
as an array of dimensions \(n\times\cdots\times n=n^k\). If
A is such an array, then the element
A[i_1,i_2,...,i_k] is the coefficient of \(\phi_{i_1}\otimes\ldots\otimes\phi_{i_k}\).
However, it is more efficient and conceptually cleaner to consider a
sparse array, as implemented by the spray package.
We will consider the case \(n=5,k=4\),
so we have multilinear maps from \(\left(\mathbb{R}^5\right)^4\) to \(\mathbb{R}\). Below, we will test algebraic
identities in R using the idiom furnished by the stokes package. For our
example we will define \(S=1.5\phi_5\otimes\phi_1\otimes\phi_1\otimes\phi_1+2.5\phi_1\otimes\phi_1\otimes\phi_2\otimes\phi_3+3.5\phi_1\otimes\phi_3\otimes\phi_4\otimes\phi_2\)
using a matrix with three rows, one per term, and whose rows correspond
to each term’s tensor products of the \(\phi\)’s. We first have to load the
stokes package:
Then the idiom is straightforward:
k <- 4
n <- 5
M <- matrix(c(5,1,1,1, 1,1,2,3, 1,3,4,2),3,4,byrow=TRUE)
M
## [,1] [,2] [,3] [,4]
## [1,] 5 1 1 1
## [2,] 1 1 2 3
## [3,] 1 3 4 2
S <- as.ktensor(M,coeffs= 0.5 + 1:3)
S
## A linear map from V^4 to R with V=R^5:
## val
## 1 3 4 2 = 3.5
## 1 1 2 3 = 2.5
## 5 1 1 1 = 1.5
Observe that, if stored as an array of size \(n^k\), \(S\) would have \(5^4=625\) elements, all but three of which
are zero. So \(S\) is a 4-tensor,
mapping \(V^4\) to \(\mathbb{R}\), where \(V=\mathbb{R}^5\). Here we have \(S=1.5\phi_5\otimes\phi_1\otimes\phi_1\otimes\phi_1+2.5\phi_1\otimes\phi_1\otimes\phi_2\otimes\phi_3+3.5\phi_1\otimes\phi_3\otimes\phi_4\otimes\phi_2\).
Note that in some implementations the row order of object S
will differ from that of M; this phenomenon is due to the
underlying C implementation using the STL map
class; see the disordR package (Hankin 2022a) and is discussed in more detail
in the mvp package (Hankin
2022b).
Package idiom for evaluation of a tensor
First, we will define \(E\) to be a
random point in \(V^k\) in terms of a
matrix:
set.seed(0)
(E <- matrix(rnorm(n*k),n,k)) # A random point in V^k
## [,1] [,2] [,3] [,4]
## [1,] 1.2629543 -1.539950042 0.7635935 -0.4115108
## [2,] -0.3262334 -0.928567035 -0.7990092 0.2522234
## [3,] 1.3297993 -0.294720447 -1.1476570 -0.8919211
## [4,] 1.2724293 -0.005767173 -0.2894616 0.4356833
## [5,] 0.4146414 2.404653389 -0.2992151 -1.2375384
Recall that \(n=5\), \(k=4\), so \(E\in\left(\mathbb{R}^5\right)^4\). We can
evaluate \(S\) at \(E\) as follows:
## [1] -3.068997
Vector space structure of tensors
Tensors have a natural vector space structure; they may be added and
subtracted, and multiplied by a scalar, the same as any other vector
space. Below, we define a new tensor \(S_1\) and work with \(2S-3S_1\):
S1 <- as.ktensor(1+diag(4),1:4)
2*S-3*S1
## A linear map from V^4 to R with V=R^5:
## val
## 1 3 4 2 = 7
## 1 1 2 3 = 5
## 5 1 1 1 = 3
## 1 1 1 2 = -12
## 1 1 2 1 = -9
## 1 2 1 1 = -6
## 2 1 1 1 = -3
We may verify that tensors are linear using package idiom:
LHS <- as.function(2*S-3*S1)(E)
RHS <- 2*as.function(S)(E) -3*as.function(S1)(E)
c(lhs=LHS,rhs=RHS,diff=LHS-RHS)
## lhs rhs diff
## 2.374816e+00 2.374816e+00 -4.440892e-16
(that is, identical up to numerical precision).
Numerical verification of multilinearity in the package
Testing multilinearity is straightforward in the package. To do this,
we need to define three matrices E1,E2,E3 corresponding to
points in \(\left(\mathbb{R}^5\right)^4\) which are
identical except for one column. In E3, this column is a
linear combination of the corresponding column in E2 and
E3:
E1 <- E
E2 <- E
E3 <- E
x1 <- rnorm(n)
x2 <- rnorm(n)
r1 <- rnorm(1)
r2 <- rnorm(1)
E1[,2] <- x1
E2[,2] <- x2
E3[,2] <- r1*x1 + r2*x2
Then we can verify the multilinearity of \(S\) by coercing to a function which is
applied to E1, E2, E3:
f <- as.function(S)
LHS <- r1*f(E1) + r2*f(E2)
RHS <- f(E3)
c(lhs=LHS,rhs=RHS,diff=LHS-RHS)
## lhs rhs diff
## -0.5640577 -0.5640577 0.0000000
(that is, identical up to numerical precision). Note that this is
not equivalent to linearity over \(V^{nk}\):
E1 <- matrix(rnorm(n*k),n,k)
E2 <- matrix(rnorm(n*k),n,k)
LHS <- f(r1*E1+r2*E2)
RHS <- r1*f(E1)+r2*f(E2)
c(lhs=LHS,rhs=RHS,diff=LHS-RHS)
## lhs rhs diff
## 0.1731245 0.3074186 -0.1342941
Tensor product of general tensors
Given two k-tensor objects \(S,T\)
we can form the tensor product \(S\otimes
T\), defined as
\[
S\otimes T{\left(v_1,\ldots,v_k,v_{k+1},\ldots, v_{k+l}\right)}=
S{\left(v_1,\ldots v_k\right)} \cdot T{\left(v_{k+1},\ldots
v_{k+l}\right)}
\]
We will calculate the tensor product of two tensors
S1,S2 defined as follows:
(S1 <- ktensor(spray(cbind(1:3,2:4),1:3)))
## A linear map from V^2 to R with V=R^4:
## val
## 3 4 = 3
## 2 3 = 2
## 1 2 = 1
(S2 <- as.ktensor(matrix(1:6,2,3)))
## A linear map from V^3 to R with V=R^6:
## val
## 2 4 6 = 1
## 1 3 5 = 1
The R idiom for \(S1\otimes S2\)
would be tensorprod(), or %X%:
## A linear map from V^5 to R with V=R^6:
## val
## 1 2 1 3 5 = 1
## 3 4 1 3 5 = 3
## 1 2 2 4 6 = 1
## 2 3 2 4 6 = 2
## 2 3 1 3 5 = 2
## 3 4 2 4 6 = 3
Then, for example:
E <- matrix(rnorm(30),6,5)
LHS <- as.function(tensorprod(S1,S2))(E)
RHS <- as.function(S1)(E[,1:2]) * as.function(S2)(E[,3:5])
c(lhs=LHS,rhs=RHS,diff=LHS-RHS)
## lhs rhs diff
## -1.048329 -1.048329 0.000000
(that is, identical up to numerical precision).
Alternating forms
An alternating form is a multilinear map \(T\) satisfying
\[
T{\left(v_1,\ldots,v_i,\ldots,v_j,\ldots,v_k\right)}=
-T{\left(v_1,\ldots,v_j,\ldots,v_i,\ldots,v_k\right)}
\]
(or, equivalently, \(T{\left(v_1,\ldots,v_i,\ldots,v_i,\ldots,v_k\right)}=
0\)). We write \(\Lambda^k(V)\)
for the space of all alternating multilinear maps from \(V^k\) to \(\mathbb{R}\). Spivak gives \(\operatorname{Alt}\colon\mathcal{J}^k(V)\longrightarrow\Lambda^k(V)\)
defined by
\[\operatorname{Alt}(T)\left(v_1,\ldots,v_k\right)=
\frac{1}{k!}\sum_{\sigma\in S_k}\operatorname{sgn}(\sigma)\cdot
T{\left(v_{\sigma(1)},\ldots,v_{\sigma(k)}\right)}
\]
where the sum ranges over all permutations of \(\left[n\right]=\left\{1,2,\ldots,n\right\}\)
and \(\operatorname{sgn}(\sigma)\in\pm
1\) is the sign of the permutation. If \(T\in\mathcal{J}^k(V)\) and \(\omega\in\Lambda^k(V)\), it is
straightforward to prove that \(\operatorname{Alt}(T)\in\Lambda^k(V)\),
\(\operatorname{Alt}\left(\operatorname{Alt}\left(T\right)\right)=\operatorname{Alt}\left(T\right)\),
and \(\operatorname{Alt}\left(\omega\right)=\omega\).
In the stokes package, this is effected by the Alt()
function:
## A linear map from V^2 to R with V=R^4:
## val
## 3 4 = 3
## 2 3 = 2
## 1 2 = 1
## A linear map from V^2 to R with V=R^4:
## val
## 4 3 = -1.5
## 3 2 = -1.0
## 2 3 = 1.0
## 3 4 = 1.5
## 2 1 = -0.5
## 1 2 = 0.5
Verifying that S1 is in fact alternating is
straightforward:
E <- matrix(rnorm(8),4,2)
Erev <- E[,2:1]
as.function(Alt(S1))(E) + as.function(Alt(S1))(Erev) # should be zero
## [1] 0
However, we can see that this form for alternating tensors (here
called \(k\)-forms) is inefficient and
highly redundant: in this example there is a 1 2 term and a
2 1 term (the coefficients are equal and opposite). In this
example we have \(k=2\) but in general
there would be potentially \(k!\)
essentially repeated terms which collectively require only a single
coefficient. The package provides kform objects which are
inherently alternating using a more efficient representation; they are
described using wedge products which are discussed next.
Wedge products and the exterior calculus
This section follows the exposition of Hubbard and Hubbard, who
introduce the exterior calculus starting with a discussion of elementary
forms, which are alternating forms with a particularly simple structure.
An example of an elementary form would be \(\mathrm{d}x_1\wedge\mathrm{d}x_3\) [treated
as an indivisible entity], which is an alternating multilinear map from
\(\mathbb{R}^n\times\mathbb{R}^n\) to
\(\mathbb{R}\) with
\[
\left(
\mathrm{d}x_1\wedge\mathrm{d}x_3
\right)\left(
\begin{pmatrix}a_1\\a_2\\a_3\\ \vdots\\ a_n\end{pmatrix},
\begin{pmatrix}b_1\\b_3\\b_3\\ \vdots\\ b_n\end{pmatrix}
\right)=\mathrm{det}
\begin{pmatrix} a_1 & b_1 \\ a_3 & b_3\end{pmatrix}
=a_1b_3-a_3b_1
\]
That this is alternating follows from the properties of the
determinant. In general of course, \(\mathrm{d}x_i\wedge\mathrm{d}x_j\left(
\begin{pmatrix}a_1\\ \vdots\\ a_n\end{pmatrix}, \begin{pmatrix}b_1\\
\vdots\\ b_n\end{pmatrix} \right)=\mathrm{det} \begin{pmatrix} a_i &
b_i \\ a_j & b_j\end{pmatrix}\). Because such objects are
linear, it is possible to consider sums of elementary forms, such as
\(d\mathrm{x}_1\wedge\mathrm{d}x_2 + 3
\mathrm{d}x_2\wedge\mathrm{d}x_3\) with
\[
\left(
\mathrm{d}x_1\wedge\mathrm{d}x_2 + 3\mathrm{d}x_2\wedge\mathrm{d}x_3
\right)\left(
\begin{pmatrix}a_1\\a_2\\ \vdots\\ a_n\end{pmatrix},
\begin{pmatrix}b_1\\b_2\\ \vdots\\ b_n\end{pmatrix}
\right)=\mathrm{det}
\begin{pmatrix} a_1 & b_1\\ a_2 & b_2\end{pmatrix}
+3\mathrm{det}
\begin{pmatrix} a_2 & b_2\\ a_3 & b_3\end{pmatrix}
\]
or even \(K=\mathrm{d}x_1\wedge\mathrm{d}x_2\wedge\mathrm{d}x_3
+5\mathrm{d}x_1\wedge\mathrm{d}x_2\wedge\mathrm{d}x_4\) which
would be a linear map from \(\left(\mathbb{R}^n\right)^3\) to \(\mathbb{R}\) with
\[
\left(
\mathrm{d}x_4\wedge\mathrm{d}x_2\wedge\mathrm{d}x_3
+5\mathrm{d}x_1\wedge\mathrm{d}x_2\wedge\mathrm{d}x_4
\right)\left(
\begin{pmatrix}a_1\\a_2\\ \vdots\\ a_n\end{pmatrix},
\begin{pmatrix}b_1\\b_2\\ \vdots\\ b_n\end{pmatrix},
\begin{pmatrix}c_1\\c_2\\ \vdots\\ c_n\end{pmatrix}
\right)=\mathrm{det}
\begin{pmatrix}
a_4 & b_4 & c_4\\
a_2 & b_2 & c_2\\
a_3 & b_3 & c_3
\end{pmatrix}
+5\mathrm{det}
\begin{pmatrix}
a_1 & b_1 & c_1\\
a_2 & b_2 & c_2\\
a_4 & b_4 & c_4
\end{pmatrix}.
\]
Defining \(K\) has ready R idiom in
which we define a matrix whose rows correspond to the differentials in
each term:
M <- matrix(c(4,2,3,1,4,2),2,3,byrow=TRUE)
M
## [,1] [,2] [,3]
## [1,] 4 2 3
## [2,] 1 4 2
K <- as.kform(M,c(1,5))
K
## An alternating linear map from V^3 to R with V=R^4:
## val
## 1 2 4 = -5
## 2 3 4 = 1
Function as.kform() takes each row of M and
places the elements in increasing order; the coefficient will change
sign if the permutation is odd. Note that the order of the rows in
K is immaterial and indeed in some implementations will
appear in a different order: the stokes package uses the
spray package, which in turn utilises the STL map class of
C++.
Formal definition of wedge product
The wedge product maps two alternating forms to another alternating
form; given \(\omega\in\Lambda^k(V)\)
and \(\eta\in\Lambda^l(V)\), Spivak
defines the wedge product \(\omega\wedge\eta\in\Lambda^{k+l}(V)\)
as
\[
\omega\wedge\eta={k+l\choose k\quad
l}\operatorname{Alt}(\omega\otimes\eta)
\]
and this is implemented in the package by function
wedge(), or, more idiomatically, ^:
M1 <- matrix(c(3,5,4, 4,6,1),2,3,byrow=TRUE)
K1 <- as.kform(M1,c(2,7))
K1
## An alternating linear map from V^3 to R with V=R^6:
## val
## 1 4 6 = 7
## 3 4 5 = -2
M2 <- cbind(1:5,3:7)
K2 <- as.kform(M2,1:5)
K2
## An alternating linear map from V^2 to R with V=R^7:
## val
## 5 7 = 5
## 4 6 = 4
## 3 5 = 3
## 2 4 = 2
## 1 3 = 1
In symbolic notation, K1 is equal to \(7\mathrm{d}x_1\wedge\mathrm{d}x_4\wedge\mathrm{d}x_6
-2\mathrm{d}x_3\wedge\mathrm{d}x_4\wedge\mathrm{d}x_5\). and
K2 is \(\mathrm{d}x_1\wedge\mathrm{d}x_3+
2\mathrm{d}x_2\wedge\mathrm{d}x_4+ 3\mathrm{d}x_3\wedge\mathrm{d}x_5+
4\mathrm{d}x_4\wedge\mathrm{d}x_6+ 5\mathrm{d}x_5\wedge
\mathrm{d}x_7\). Package idiom for wedge products is
straightforward:
## An alternating linear map from V^5 to R with V=R^7:
## val
## 1 3 4 5 6 = -21
## 1 4 5 6 7 = -35
(we might write the product as \(-35\mathrm{d}x_1\wedge\mathrm{d}x_4\wedge\mathrm{d}x_5\wedge
\mathrm{d}x_6\wedge\mathrm{d}x_7
-21\mathrm{d}x_1\wedge\mathrm{d}x_3\wedge\mathrm{d}x_4\wedge\mathrm{d}x_5\wedge\mathrm{d}x_6\)).
See how the wedge product eliminates rows with repeated entries, gathers
permuted rows together (respecting the sign of the permutation), and
expresses the result in terms of elementary forms. The product is a
linear combination of two elementary forms; note that only two
coefficients out of a possible \({7\choose
5}=21\) are nonzero. Note again that the order of the rows in the
product is arbitrary.
The wedge product has formal properties such as distributivity but by
far the most interesting one is associativity, which I will demonstrate
below:
F1 <- as.kform(matrix(c(3,4,5, 4,6,1,3,2,1),3,3,byrow=TRUE))
F2 <- as.kform(cbind(1:6,3:8),1:6)
F3 <- kform_general(1:8,2)
(F1 ^ F2) ^ F3
## An alternating linear map from V^7 to R with V=R^8:
## val
## 1 2 4 5 6 7 8 = -5
## 2 3 4 5 6 7 8 = 6
## 1 2 3 5 6 7 8 = 11
## 1 2 3 4 5 6 8 = 1
## 1 2 3 4 5 7 8 = -5
## 1 2 3 4 6 7 8 = 2
## 1 2 3 4 5 6 7 = 1
## 1 3 4 5 6 7 8 = -2
## An alternating linear map from V^7 to R with V=R^8:
## val
## 2 3 4 5 6 7 8 = 6
## 1 2 4 5 6 7 8 = -5
## 1 2 3 4 5 7 8 = -5
## 1 2 3 4 5 6 8 = 1
## 1 2 3 4 6 7 8 = 2
## 1 2 3 4 5 6 7 = 1
## 1 3 4 5 6 7 8 = -2
## 1 2 3 5 6 7 8 = 11
Note carefully in the above that the terms in
(F1 ^ F2) ^ F3 and F1 ^ (F2 ^ F3) appear in a
different order. They are nevertheless algebraically identical, as we
may demonstrate by calculating their difference:
(F1 ^ F2) ^ F3 - F1 ^ (F2 ^ F3)
## The zero alternating linear map from V^7 to R with V=R^n:
## empty sparse array with 7 columns
Spivak observes that \(\Lambda^k(V)\) is spanned by the \(n\choose k\) wedge products of the form
\[
\mathrm{d}x_{i_1}\wedge\mathrm{d}x_{i_2}\wedge\ldots\wedge\mathrm{d}x_{i_k}\qquad
1\leq i_i<i_2<\cdots <i_k\leq n
\]
where these products are the elementary forms (compare \(\mathcal{J}^k(V)\), which is spanned by
\(n^k\) elementary forms). Formally,
multilinearity means every element of the space \(\Lambda^k(V)\) is a linear combination of
elementary forms, as illustrated in the package by function
kform_general(). Consider the following idiom:
Krel <- kform_general(4,2,1:6)
Krel
## An alternating linear map from V^2 to R with V=R^4:
## val
## 3 4 = 6
## 2 4 = 5
## 1 4 = 4
## 2 3 = 3
## 1 3 = 2
## 1 2 = 1
Object Krel is a two-form, specifically a map from \(\left(\mathbb{R}^4\right)^2\) to \(\mathbb{R}\). Observe that
Krel has \({4\choose
2}=6\) components, which do not appear in any particular order.
Addition of such \(k\)-forms is
straightforward in R idiom but algebraically nontrivial:
K1 <- as.kform(matrix(1:4,2,2),c(1,109))
K2 <- as.kform(matrix(c(1,3,7,8,2,4),ncol=2,byrow=TRUE),c(-1,5,4))
K1
## An alternating linear map from V^2 to R with V=R^4:
## val
## 2 4 = 109
## 1 3 = 1
## An alternating linear map from V^2 to R with V=R^8:
## val
## 2 4 = 4
## 7 8 = 5
## 1 3 = -1
## An alternating linear map from V^2 to R with V=R^8:
## val
## 2 4 = 113
## 7 8 = 5
In the above, note how the \(\mathrm{d}x_2\wedge\mathrm{d}x_4\) terms
combine [to give 2 4 = 113] and the \(\mathrm{d}x_1\wedge\mathrm{d}x_3\) term
vanishes by cancellation.
Print methods
Although the spray form used above is probably the most direct and
natural representation of differential forms in numerical work,
sometimes we need a more algebraic print method.
U <- ktensor(spray(cbind(1:4,2:5),1:4))
U
## A linear map from V^2 to R with V=R^5:
## val
## 4 5 = 4
## 3 4 = 3
## 2 3 = 2
## 1 2 = 1
we can represent this more algebraically using the
as.symbolic() function:
## [1] +4 d*e +3 c*d +2 b*c + a*b
In the above, U is a multilinear map from \(\left(\mathbb{R}^5\right)^2\) to \(\mathbb{R}\). Symbolically, a
represents the map that takes \((a,b,c,d,e)\) to \(a\), b the map that takes
\((a,b,c,d,e)\) to b, and
so on. The asterisk * represents the tensor product \(\otimes\). Alternating forms work similarly
but \(k\)-forms have different
defaults:
K <- kform_general(3,2,1:3)
K
## An alternating linear map from V^2 to R with V=R^3:
## val
## 2 3 = 3
## 1 3 = 2
## 1 2 = 1
as.symbolic(K,d="d",symbols=letters[23:26])
## [1] +3 dx^dy +2 dw^dy + dw^dx
Note that the wedge product \(\wedge\), although implemented in package
idiom as ^ or %^%, appears in the symbolic
representation as an ascii caret, ^.
We can alter the default print method with the
kform_symbolic_print option, which uses
as.symbolic():
options(kform_symbolic_print = "d")
K
## An alternating linear map from V^2 to R with V=R^3:
## +3 dx2^dx3 +2 dx1^dx3 + dx1^dx2
This print option works nicely with the d() function for
elementary forms:
(d(1) + d(5)) ^ (d(3)-5*d(2)) ^ d(7)
## An alternating linear map from V^3 to R with V=R^7:
## - dx3^dx5^dx7 + dx1^dx3^dx7 +5 dx2^dx5^dx7 -5 dx1^dx2^dx7
options(kform_symbolic_print = NULL) # restore default
Contractions
Given a \(k\)-form \(\phi\colon V^k\longrightarrow\mathbb{R}\)
and a vector \(\mathbf{v}\in V\), the
contraction \(\phi_\mathbf{v}\) of \(\phi\) and \(\mathbf{v}\) is a \(k-1\)-form with
\[
\phi_\mathbf{v}{\left(\mathbf{v}^1,\ldots,\mathbf{v}^{k-1}\right)} =
\phi{\left(\mathbf{v},\mathbf{v}^1,\ldots,\mathbf{v}^{k-1}\right)}
\]
if \(k>1\); we specify \(\phi_\mathbf{v}=\phi(\mathbf{v})\) if \(k=1\). Verification is straightforward:
(o <- rform()) # a random 3-form
## An alternating linear map from V^3 to R with V=R^7:
## val
## 2 4 6 = -6
## 4 6 7 = 5
## 5 6 7 = 4
## 1 3 7 = 7
## 1 5 7 = -12
## 1 4 6 = 8
## 2 3 7 = -2
## 1 2 4 = 1
V <- matrix(runif(21),ncol=3)
LHS <- as.function(o)(V)
RHS <- as.function(contract(o,V[,1]))(V[,-1])
c(LHS=LHS,RHS=RHS,diff=LHS-RHS)
## LHS RHS diff
## 4.512547e-01 4.512547e-01 -5.551115e-17
It is possible to iterate the contraction process; if we pass a
matrix \(V\) to contract()
then this is interpreted as repeated contraction with the columns of
\(V\):
as.function(contract(o,V[,1:2]))(V[,-(1:2),drop=FALSE])
## [1] 0.4512547
If we pass three columns to contract() the result is a
\(0\)-form:
## [1] 0.4512547
In the above, the result is coerced to a scalar; in order to work
with a formal \(0\)-form (which is
represented in the package as a spray with a zero-column
index matrix) we can use the lose=FALSE argument:
## An alternating linear map from V^0 to R with V=R^0:
## val
## = 0.4512547
Transformations and pullback
Suppose we are given a two-form \(\omega=\sum_{i<j}a_{ij}dx_i\wedge dx_j\)
and relationships \(dx_i=\sum_rM_{ir}dy_r\), then we would
have
\[
\omega =
\sum_{i<j}
a_{ij}\left(\sum_rM_{ir}dy_r\right)\wedge\left(\sum_rM_{jr}dy_r\right).
\]
The general situation would be a \(k\)-form where we would have
\[
\omega=\sum_{i_1<\cdots<i_k}a_{i_1\ldots
i_k}dx_{i_1}\wedge\cdots\wedge dx_{i_k}
\]
giving
\[\omega =
\sum_{i_1<\cdots <i_k}\left[
a_{i_1<\cdots <
i_k}\left(\sum_rM_{i_1r}dy_r\right)\wedge\cdots\wedge\left(\sum_rM_{i_kr}dy_r\right)\right].
\]
So \(\omega\) was given in terms of
\(dx_1,\ldots,dx_k\) and we have
expressed it in terms of \(dy_1,\ldots,dy_k\). So for example if
\[
\omega=
dx_1\wedge dx_2 + 5dx_1\wedge dx_3\]
and
\[
\left(
\begin{array}{l}
dx_1\\
dx_2\\
dx_3
\end{array}
\right)=
\left(
\begin{array}{ccc}
1 & 4 & 7\\
2 & 5 & 8\\
3 & 6 & 9\\
\end{array}
\right) \left(
\begin{array}{l}
dy_1\\ dy_2\\ dy_3
\end{array}
\right)
\]
then
\[
\begin{array}{ccl}
\omega &=&
\left(1dy_1+4dy_2+7dy_3\right)\wedge
\left(2dy_1+5dy_2+8dy_3\right)+
5\left(1dy_1+4dy_2+7dy_3\right)\wedge
\left(3dy_1+6dy_2+9dy_3\right)
\\
&=&2dy_1\wedge dy_1+5dy_1\wedge dy_2+\cdots+
5\cdot 7\cdot 6dx_3\wedge dx_2+
5\cdot 7\cdot 9dx_3\wedge dx_3+\\
&=& -33dy_1\wedge dy_2-66dy_1\wedge dy_3-33dy_2\wedge dy_3
\end{array}
\]
Function pullback() function does all this:
options(kform_symbolic_print = "dx") # uses dx etc in print method
pullback(dx^dy+5*dx^dz, matrix(1:9,3,3))
## An alternating linear map from V^2 to R with V=R^3:
## -33 dx^dy -66 dx^dz -33 dy^dz
options(kform_symbolic_print = NULL) # revert to default
However, it is slow and I am not 100% sure that there isn’t a much
more efficient way to do such a transformation. There are a few tests in
tests/testthat. Here I show that transformations may be
inverted using matrix inverses:
(o <- 2 * as.kform(2) ^ as.kform(4) ^ as.kform(5))
## An alternating linear map from V^3 to R with V=R^5:
## val
## 2 4 5 = 2
M <- matrix(rnorm(25),5,5)
Then we will transform according to matrix M and then
transform according to the matrix inverse; the functionality works
nicely with magrittr pipes:
o |> pullback(M) |> pullback(solve(M))
## An alternating linear map from V^3 to R with V=R^5:
## val
## 3 4 5 = 0
## 1 2 3 = 0
## 2 3 5 = 0
## 2 3 4 = 0
## 2 4 5 = 2
## 1 2 5 = 0
## 1 3 4 = 0
## 1 2 4 = 0
## 1 3 5 = 0
## 1 4 5 = 0
Above we see many rows with values small enough for the print method
to print an exact zero, but not sufficiently small to be eliminated by
the spray internals. We can remove the small entries with
zap():
o |> pullback(M) |> pullback(solve(M)) |> zap()
## val
## 2 4 5 = 2
See how the result is equal to the original \(k\)-form \(2dy_2\wedge dy_4\wedge dy_5\).
Exterior derivatives
Given a \(k\)-form \(\omega\), Spivak defines the differential
of \(\omega\) to be a \((k+1)\)-form \(\mathrm{d}\omega\) as follows. If
\[
\omega =
\sum_{
i_1 < i_2 <\cdots<i_k}
\omega_{i_1i_2\ldots i_k}
\mathrm{d}x^{i_1}\wedge
\mathrm{d}x^{i_2}\wedge\cdots\wedge\mathrm{d}x^{i_k}
\]
then
\[
\mathrm{d}\omega =
\sum_{
i_1 < i_2 <\cdots<i_k}
\sum_{\alpha=1}^n D_\alpha\left(\omega_{i_1i_2\ldots i_k}\right)
\cdot
\mathrm{d}x^{i_1}\wedge
\mathrm{d}x^{i_2}\wedge\cdots\wedge\mathrm{d}x^{i_k}
\]
where \(D_if(a)=\lim_{h\longrightarrow
0}\frac{f(a^1,\ldots,a^i+h,\ldots,a^n)-f(a^1,\ldots,a^i,\ldots,a^n)}{h}\)
is the ordinary \(i^\mathrm{th}\)
partial derivative (Spivak, p25). Hubbard and Hubbard take a
conceptually distinct approach and define the exterior derivative \(d\phi\) (they use a bold font, \(\mathbf{d}\phi\)) of the \(k\)-form \(\phi\) as the \((k+1)\)-form given by
\[ {d}\phi
\left({v}_i,\ldots,{v}_{k+1}\right)
=
\lim_{h\longrightarrow 0}\frac{1}{h^{k+1}}\int_{\partial
P_{x}\left(h{v}_1,\ldots,h{v}_{k+1}\right)}\phi
\]
which, by their own account, is a rather opaque mathematical idiom.
However, the definition makes sense and it is consistent with Spivak’s
definition above. The definition allows one to express the fundamental
theorem of calculus in an arbitrary number of dimensions without
modification.
It can be shown that
\[
\mathrm{d}{\left(f\,dx_{i_1}\wedge\cdots\wedge\mathrm{d}x_{i_k}\right)}=
\mathrm{d}f\wedge\mathrm{d}x_{i_1}\wedge\cdots\wedge\mathrm{d}x_{i_k}
\]
where \(f\colon\mathbb{R}^n\longrightarrow\mathbb{R}\)
is a scalar function of position. The package provides
grad() which, when given a vector \(x_1,\ldots,x_n\) returns the one-form
\[
\sum_{i=1}^n x_idx_i
\]
This is useful because \(\mathrm{d}f=\sum_{j=1}^n\left(D_j
f\right)\,\mathrm{d}x_j\). Thus
grad(c(0.4,0.1,-3.2,1.5))
## An alternating linear map from V^1 to R with V=R^4:
## val
## 4 = 1.5
## 3 = -3.2
## 2 = 0.1
## 1 = 0.4
We will use the grad() function to verify that, in \(\mathbb{R}^n\), a certain \((k-1)\)-form has zero work function.
Motivated by the fact that
\[
F_3=\frac{1}{\left(x^2+y^2+z^2\right)^{3/2}}
\begin{pmatrix}x\\y\\z\end{pmatrix}
\]
is a divergenceless velocity field in \(\mathbb{R}^3\), H&H go on to define
[page 548, equation 6.7.16]
\[
\omega_{n}=\mathrm{d}\frac{1}{\left(x_1^2+\ldots
+x_n^2\right)^{n/2}}\sum_{i=1}^{n}(-1)^{i-1}
x_i\mathrm{d}x_1\wedge\cdots\wedge\widehat{\mathrm{d}x_i}\wedge\cdots\wedge\mathrm{d}x_n
\]
(where a hat indicates the absence of a term), and show analytically
that \(\mathrm{d}\omega=0\). Here I
show this using R idiom. The first thing is to define a function that
implements the hat:
f <- function(x){
n <- length(x)
as.kform(t(apply(diag(n)<1,2,which)))
}
So, for example:
## An alternating linear map from V^4 to R with V=R^5:
## val
## 1 2 3 4 = 1
## 1 2 3 5 = 1
## 1 2 4 5 = 1
## 1 3 4 5 = 1
## 2 3 4 5 = 1
Then we can use the grad() function to calculate \(\mathrm{d}\omega\), using the quotient law
to express the derivatives analytically:
df <- function(x){
n <- length(x)
S <- sum(x^2)
grad(rep(c(1,-1),length=n)*(S^(n/2) - n*x^2*S^(n/2-1))/S^n
)
}
Thus
## An alternating linear map from V^1 to R with V=R^5:
## val
## 5 = -5.67e-05
## 4 = 2.03e-05
## 3 = 8.10e-06
## 2 = -2.84e-05
## 1 = 4.05e-05
Now we can use the wedge product of the two parts to show that the
exterior derivative is zero:
x <- rnorm(9)
print(df(x) ^ f(x)) # should be zero
## An alternating linear map from V^9 to R with V=R^9:
## val
## 1 2 3 4 5 6 7 8 9 = 0
Differential of the differential, \(d^2=0\)
We can use the package to verify the celebrated fact that, for any
\(k\)-form \(\phi\), \(\mathrm{d}\left(\mathrm{d}\phi\right)=0\).
The first step is to define scalar functions
f1(), f2(), f3(), all \(0\)-forms:
f1 <- function(w,x,y,z){x + y^3 + x*y*w*z}
f2 <- function(w,x,y,z){w^2*x*y*z + sin(w) + w+z}
f3 <- function(w,x,y,z){w*x*y*z + sin(x) + cos(w)}
Now we need to define elementary \(1\)-forms:
dw <- as.kform(1)
dx <- as.kform(2)
dy <- as.kform(3)
dz <- as.kform(4)
I will demonstrate the theorem by defining a \(2\)-form which is the sum of three
elementary two-forms, evaluated at a particular point in \(\mathbb{R}^4\):
phi <-
(
+f1(1,2,3,4) ^ dw ^ dx
+f2(1,2,3,4) ^ dw ^ dy
+f3(1,2,3,4) ^ dy ^ dz
)
We could use slightly slicker R idiom by defining elementary forms
e1,e2,e3 and then defining phi to be a linear
sum, weighted with \(0\)-forms given by
the (scalar) functions f1,f2,f3:
e1 <- dw ^ dx
e2 <- dw ^ dy
e3 <- dy ^ dz
phi <-
(
+f1(1,2,3,4) ^ e1
+f2(1,2,3,4) ^ e2
+f3(1,2,3,4) ^ e3
)
phi
## An alternating linear map from V^2 to R with V=R^4:
## val
## 1 3 = 29.84147
## 1 2 = 53.00000
## 3 4 = 25.44960
Now to evaluate first derivatives of f1() etc at point
\((1,2,3,4)\), using
Deriv() from the Deriv package:
library("Deriv")
Df1 <- Deriv(f1)(1,2,3,4)
Df2 <- Deriv(f2)(1,2,3,4)
Df3 <- Deriv(f3)(1,2,3,4)
So Df1 etc are numeric vectors of length 4, for
example:
## w x y z
## 24 13 35 6
To calculate dphi, or \(\mathrm{d}\phi\), we can use function
grad():
dphi <-
(
+grad(Df1) ^ e1
+grad(Df2) ^ e2
+grad(Df3) ^ e3
)
dphi
## An alternating linear map from V^3 to R with V=R^4:
## val
## 1 3 4 = 30.15853
## 1 2 3 = 23.00000
## 1 2 4 = 6.00000
## 2 3 4 = 11.58385
Now work on the differential of the differential. First evaluate the
Hessians (4x4 numeric matrices) at the same point:
Hf1 <- matrix(Deriv(f1,nderiv=2)(1,2,3,4),4,4)
Hf2 <- matrix(Deriv(f2,nderiv=2)(1,2,3,4),4,4)
Hf3 <- matrix(Deriv(f3,nderiv=2)(1,2,3,4),4,4)
For example
## w x y z
## w 0 12 8 6
## x 12 0 4 3
## y 8 4 18 2
## z 6 3 2 0
(note the matrix is symmetric; also note carefully the nonzero
diagonal term). But \(dd\phi\) is
clearly zero as the Hessians are symmetrical:
ij <- expand.grid(seq_len(nrow(Hf1)),seq_len(ncol(Hf1)))
ddphi <- # should be zero
(
+as.kform(ij,c(Hf1))
+as.kform(ij,c(Hf2))
+as.kform(ij,c(Hf3))
)
ddphi
## The zero alternating linear map from V^2 to R with V=R^n:
## empty sparse array with 2 columns
as expected.
Stokes’s theorem
In its most general form, Stokes’s theorem states
\[
\int_{\partial X}\phi=\int_X\mathrm{d}\phi
\]
where \(X\subset\mathbb{R}^n\) is a
compact oriented \((k+1)\)-dimensional
manifold with boundary \(\partial X\)
and \(\phi\) is a \(k\)-form defined on a neighborhood of \(X\).
We will verify Stokes, following 6.9.5 of Hubbard in which
\[
\phi=
\left(x_1-x_2^2+x_3^3-\cdots\pm x_n^n\right)
\left(
\sum_{i=1}^n
\mathrm{d}x_1\wedge\cdots\wedge\widehat{\mathrm{d}x_i}\wedge\cdots\wedge\mathrm{d}x_n
\right)
\]
(a hat indicates that a term is absent), and we wish to evaluate
\(\int_{\partial C_a}\phi\) where \(C_a\) is the cube \(0\leq x_j\leq a, 1\leq j\leq n\). Stokes
tells us that this is equal to \(\int_{C_a}\mathrm{d}\phi\), which is given
by
\[
d\phi = \left(
1+2x_2+\cdots + nx_n^{n-1}\right)
\mathrm{d}x_1\wedge\cdots\wedge\mathrm{d}x_n
\]
and so the volume integral is just
\[
\sum_{j=1}^n
\int_{x_1=0}^a
\int_{x_2=0}^a
\cdots
\int_{x_i=0}^a
jx_j^{j-1}
dx_1 dx_2\ldots dx_n=
a^{n-1}\left(a+a^2+\cdots+a^n\right).
\]
Stokes’s theorem, being trivial, is not amenable to direct numerical
verification but the package does allow slick creation of \(\phi\):
phi <- function(x){
n <- length(x)
sum(x^seq_len(n)*rep_len(c(1,-1),n)) * as.kform(t(apply(diag(n)<1,2,which)))
}
phi(1:9)
## An alternating linear map from V^8 to R with V=R^9:
## val
## 2 3 4 5 6 7 8 9 = 371423053
## 1 2 3 4 5 7 8 9 = 371423053
## 1 3 4 5 6 7 8 9 = 371423053
## 1 2 3 4 6 7 8 9 = 371423053
## 1 2 3 4 5 6 7 9 = 371423053
## 1 2 4 5 6 7 8 9 = 371423053
## 1 2 3 5 6 7 8 9 = 371423053
## 1 2 3 4 5 6 8 9 = 371423053
## 1 2 3 4 5 6 7 8 = 371423053
(recall that phi is a function that maps \(\mathbb{R}^9\) to 8-forms. Here we choose
\(\left(1,2,\ldots,9\right)\in\mathbb{R}^9\)
and phi(1:9) as shown above is the resulting 8-form. Thus,
if we write \(\phi_{1:9}\) for
phi(1:9) we would have \(\phi_{1:9}\colon\left(\mathbb{R}^9\right)^8\longrightarrow\mathbb{R}\),
with package idiom as follows:
f <- as.function(phi(1:9))
E <- matrix(runif(72),9,8) # (R^9)^8
f(E)
## [1] -26620528
Further, \(\mathrm{d}\phi\) is given
by
dphi <- function(x){
nn <- seq_along(x)
sum(nn*x^(nn-1)) * as.kform(seq_along(x))
}
dphi(1:9)
## An alternating linear map from V^9 to R with V=R^9:
## val
## 1 2 3 4 5 6 7 8 9 = 405071317
(observe that dphi(1:9) is a 9-form, with \(\mathrm{d}\phi_{1:9}\colon\left(\mathbb{R}^9\right)^9\longrightarrow\mathbb{R}\)).
Now consider Spivak’s theorem 4.6 (page 82), which in this context
states that a 9-form is proportional to the determinant of the \(9\times 9\) matrix formed from its
arguments, with constant of proportionality equal to the form evaluated
on the identity matrix \(I_9\)
[formally and more generally, if \(v_1,\ldots,v_n\) is a basis for \(V\), \(\omega\in\Lambda^n(V)\) and \(w_i=\sum a_{ij}v_j\) then \(\omega\left(w_1,\ldots,w_n\right) =
\det\left(a_{ij}\right)\cdot\omega\left(v_1,\ldots v_n\right)\)].
Numerically:
f <- as.function(dphi(1:9))
E <- matrix(runif(81),9,9)
f(E)
## [1] -9850953
det(E)*f(diag(9)) # should match f(E) by Spivak's 4.6
## [1] -9850953
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