knitr::include_graphics(system.file("help/figures/stokes.png", package = "stokes"))
set.seed(0) knitr::opts_chunk$set(echo = TRUE) options(rmarkdown.html_vignette.check_title = FALSE) library("stokes") library("permutations") knit_print.function <- function(x, ...){dput(x)} registerS3method( "knit_print", "function", knit_print.function, envir = asNamespace("knitr") )
vector_cross_product
knit_print.function <- function(x, ...){ a <- capture.output(print(x)) paste(gsub(" " ,"",a[seq(from=1,to=length(a)-2)]),collapse="") } registerS3method( "knit_print", "function", knit_print.function, envir = asNamespace("knitr") )
vcp3
To cite the stokes
package in publications, please use
@hankin2022_stokes. The vector cross product of two vectors
$\mathbf{u},\mathbf{v}\in\mathbb{R}^3$, denoted
$\mathbf{u}\times\mathbf{v}$, is defined in elementary mechanics as
$|\mathbf{u}||\mathbf{v}|\sin(\theta)\,\mathbf{n}$, where $\theta$ is
the angle between $\mathbf{u}$ and $\mathbf{v}$, and $\mathbf{n}$ is
the unit vector perpendicular to $\mathbf{u}$ and $\mathbf{v}$ such
that $(\mathbf{u},\mathbf{v},\mathbf{n})$ is positively
oriented. Vector cross products find wide applications in physics,
engineering, and computer science. @spivak1965 considers the standard
vector cross product $\mathbf{u}\times\mathbf{v}=\det\begin{pmatrix} i
& j & k \ u_1&u_2&u_3\ v_1&v_2&v_3 \end{pmatrix}$ and places it in a
more general and rigorous context. In a memorable passage, he states:
::: {.warning style="padding:0.1em; background-color:#E9D8FD; color:#69337A"}
If $v_1,\ldots,v_{n-1}\in\mathbb{R}^n$ and $\phi$ is defined by $$ \phi(w)=\det\left(\begin{array}{c}v_1\\ \vdots\\ v_{n-1}\\w\end{array}\right) $$ then $\phi\in\Lambda^1\left(\mathbb{R}^n\right)$; therefore there is a unique $z\in\mathbb{R}^n$ such that $$ \left\langle w,z\right\rangle=\phi(w)= \det\left(\begin{array}{c}v_1\\ \vdots\\ v_{n-1}\\w\end{array}\right). $$ This $z$ is denoted $v_1\times\cdots\times v_{n-1}$ and is called the *cross product* of $v_1,\ldots,v_{n-1}$.
- Michael Spivak, 1969 (Calculus on Manifolds, Perseus books). Pages 83-84
:::
The reason for $\mathbf{w}$ being at the bottom rather than the top is to ensure that the $n$-tuple $(\mathbf{v}1,\ldots,\mathbf{v}{n-1},\mathbf{w})$ has positive orientation with respect to the standard basis vectors of $\mathbb{R}^n$. In $\mathbb{R}^3$ we get the standard elementary mnemonic for $\mathbf{u}=(u_1,u_2,u_3)$, $\mathbf{v}=(v_1,v_2,v_3)$:
$$ \mathbf{u}\times\mathbf{v}= \mathrm{det} \begin{pmatrix} i&j&k\ u_1&u_2&u_3\ v_1&v_2&v_3 \end{pmatrix}. $$
This is (universal) shorthand for the formal definition of the cross product, although sometimes it is better to return to Spivak's formulation and, writing $\mathbf{w}=(w_1,w_2,w_3)$, use the definition directly obtaining
$$ (\mathbf{u}\times\mathbf{v})\cdot\mathbf{w}= \mathrm{det} \begin{pmatrix} w_1&w_2&w_3\ u_1&u_2&u_3\ v_1&v_2&v_3 \end{pmatrix}. $$
In the stokes
package [@hankin2022_stokes], R function
vector_cross_product()
takes a matrix with $n$ rows and $n-1$
columns: the transpose of the work above. This is because stokes
(and R
) convention is to interpret columns of a matrix as
vectors. If we wanted to take the cross product of
$\mathbf{u}=(5,-2,1)$ with $\mathbf{v}=(1,2,0)$:
(M <- cbind(c(5,-2,1),c(1,2,0))) vector_cross_product(M)
But of course we can work with higher dimensional spaces:
vector_cross_product(matrix(rnorm(30),6,5))
In the case $n=2$ the vector cross product becomes a unary operator of a single vector $\left(u,v\right)\in\mathbb{R}^2$, returning its argument rotated counterclockwise by $\pi/2$; this case is discussed at the end, along with $n=1$.
We can demonstrate that the function has the correct orientation. We need to ensure that the vectors $\mathbf{v}_1,\ldots,\mathbf{v}_n,\mathbf{v}_1\times\cdots\times\mathbf{v}_n$ constitute a right-handed basis:
det(cbind(M,vector_cross_product(M)))>0
So it is right-handed in this case. Here is a more severe test of the right-handedness::
f <- function(n){ M <- matrix(rnorm(n^2+n),n+1,n) det(cbind(M,vector_cross_product(M)))>0 } all(sapply(sample(3:10,100,replace=TRUE),f))
Above, we see that in each case the vectors are right-handed. We may further verify that the rules for determinants are being obeyed by taking a dot product as follows:
M <- matrix(rnorm(42),7,6) crossprod(M,vector_cross_product(M))
Writing $M=[v_1,\ldots,v_6]$, $v_i\in\mathbb{R}^7$, we see that the
dot product $v_i\cdot v_1\times\cdots\times v_6$ as implemented by
crossprod()
vanishes (up to numerical precision), as the
determinants in question have two identical columns.
Spivak gives the following properties:
[ \mathbf{v}{\sigma(1)}\times\cdots\times\mathbf{v}{\sigma(n)} = \operatorname{sgn}\sigma\cdot \mathbf{v}{1}\times\cdots\times\mathbf{v}{n} ]
[ \mathbf{v}{1} \times\cdots\times a\mathbf{v}_i \times\cdots\times \mathbf{v}{n} = a\cdot \mathbf{v}{1} \times\cdots\times \mathbf{v}_i \times\cdots\times \mathbf{v}{n} ]
[ \mathbf{v}{1} \times\cdots\times \left(\mathbf{v}_i+{\mathbf{v}'}_i\right) \times\cdots\times \mathbf{v}{n} = \mathbf{v}{1} \times\cdots\times \mathbf{v}_i \times\cdots\times \mathbf{v}{n} + \mathbf{v}{1} \times\cdots\times {\mathbf{v}'}_i \times\cdots\times \mathbf{v}{n} ]
For the first we use a permutation sigma
from the permutations
package [@hankin2020] with a sign of $-1$:
M <- matrix(rnorm(30),6,5) sigma <- as.cycle("(12)(345)") sgn(sigma) Mdash <- M[,as.function(sigma)(seq_len(5))] vector_cross_product(M) + vector_cross_product(Mdash)
Above we see that the the two vector cross products add to zero (up to
numerical precision), as they should because sigma
is an odd
permutation. For the second:
Mdash <- M Mdash[,3] <- pi*Mdash[,3] vector_cross_product(Mdash) - vector_cross_product(M) * pi
Above we see that the second product is $\pi$ times the first (to numerical precision), by linearity of the vector cross product. For the third:
M1 <- M M2 <- M Msum <- M v1 <- runif(6) v2 <- runif(6) M1[,3] <- v1 M2[,3] <- v2 Msum[,3] <- v1+v2 vector_cross_product(M1) + vector_cross_product(M2) - vector_cross_product(Msum)
Above we see that the sum of the first two products is equal to that of the third (up to numerical precision), again by linearity of the vector cross product.
The cross product has a coordinate-free definition as the Hodge conjugate of the wedge product of its arguments. In $d$ dimensions:
[ \mathbf{v}1\times\cdots\times\mathbf{v}{d-1}={\star}\left( \mathbf{v}1\wedge\cdots\wedge\mathbf{v}{d-1}\right). ]
This is not used in function vector_cross_product()
because it is
computationally inefficient and (I think) prone to numerical roundoff
errors. We may verify that the definitions agree, using a
six-dimensional test case:
set.seed(2) M <- matrix(rnorm(30),6,5) (ans1 <- vector_cross_product(M))
We can see that vector_cross_product()
returns an R vector. To
verify that this is correct, we compare the output with the value
calculated directly with the wedge product:
(jj <- as.1form(M[,1]) ^ as.1form(M[,2]) ^ as.1form(M[,3]) ^ as.1form(M[,4]) ^ as.1form(M[,5])) (ans2 <- hodge(jj))
Above we see agreement between ans1
and ans2
although the elements
might appear in a different order (as per disordR
discipline).
Actually it is possible to produce the same answer using slightly
slicker idiom:
(ans3 <- hodge(Reduce(`^`,lapply(1:5,function(i){as.1form(M[,i])}))))
[again note the different order in the output]. Above, we see that
the output of vector_cross_product()
[ans1
] is an ordinary R
vector, but the direct result [ans2
] is a 1-form. In order to
compare these, we first need to coerce ans2
to a 1-form and then
subtract:
(diff <- as.1form(ans1) - ans3) coeffs(diff)
Above we see that ans1
and ans3
match to within numerical precision.
Taking Spivak's definition at face value, we could define the vector cross product $\mathbf{u}\times\mathbf{v}$ of three-vectors $\mathbf{u}$ and $\mathbf{v}$ as a map from the tangent space to the reals, with $\left(\mathbf{u}\times\mathbf{v}\right)(\mathbf{w})= \left(\mathbf{u}\times\mathbf{v}\right)\cdot\mathbf{w} =\left(I_\mathbf{u}\right)_\mathbf{v}(\mathbf{w})$, where $I$ is the 3-volume element and subscripts refer to contraction. Package idiom for this would be:
function(u,v){contract(volume(3),cbind(u,v))}
However, note that 3D vector cross products are implemented in the
package as function vcp3()
, which uses different idiom:
vcp3
This is preferable on the grounds that coercion to a 1-form is explicit. Suppose we wish to take the vector cross product of $\mathbf{u}=\left(1,4,2\right)^T$ and $\mathbf{v}=\left(2,1,5\right)^T$:
u <- c(1,4,2) v <- c(2,1,5) (p <- vcp3(u,v)) # 'p' for (cross) product
Above, note the order of the lines is implementation-specific as per
disordR
discipline [@hankin2022_disordR], but the form itself should
agree with basis vector evaluation given below. Object p
is the
vector cross product of $\mathbf{u}$ and $\mathbf{v}$, but is given as
a one-form. We can see the mnemonic in operation by coercing p
to a
function and then evaluating this on the three basis vectors of
$\mathbb{R}^3$:
ucv <- as.function(p) c(i=ucv(ex), j=ucv(ey), k=ucv(ez))
and we see agreement with the mnemonic
$\det\begin{pmatrix}i&j&k\1&4&2\2&1&5\end{pmatrix}$. Further, we may
evaluate the triple cross product
$(\mathbf{u}\times\mathbf{v})\cdot\mathbf{w}$ by evaluating ucv()
at
$\mathbf{w}$.
w <- c(1,-3,2) ucv(w)
This shows agreement with the elementary mnemonic $\det\begin{pmatrix}1&-3&2\1&4&2\2&1&5\end{pmatrix}=7$, as expected from linearity.
The following identities are standard results:
$$ \begin{aligned} \mathbf{u}\times(\mathbf{v}\times\mathbf{w}) &= \mathbf{v}(\mathbf{w}\cdot\mathbf{u})-\mathbf{w}(\mathbf{u}\cdot\mathbf{v})\ (\mathbf{u}\times\mathbf{v})\times\mathbf{w} &= \mathbf{v}(\mathbf{w}\cdot\mathbf{u})-\mathbf{u}(\mathbf{v}\cdot\mathbf{w})\ (\mathbf{u}\times\mathbf{v})\times(\mathbf{u}\times\mathbf{w}) &= (\mathbf{u}\cdot(\mathbf{v}\times\mathbf{w}))\mathbf{u} \ (\mathbf{u}\times\mathbf{v})\cdot(\mathbf{w}\times\mathbf{x}) &= (\mathbf{u}\cdot\mathbf{w})(\mathbf{v}\cdot\mathbf{x}) - (\mathbf{u}\cdot\mathbf{x})(\mathbf{v}\cdot\mathbf{w}) \end{aligned} $$
We may verify all four together:
x <- c(-6,5,7) # u,v,w as before c( hodge(as.1form(u) ^ vcp3(v,w)) == as.1form(v*sum(w*u) - w*sum(u*v)), hodge(vcp3(u,v) ^ as.1form(w)) == as.1form(v*sum(w*u) - u*sum(v*w)), as.1form(as.function(vcp3(v,w))(u)*u) == hodge(vcp3(u,v) ^ vcp3(u,w)) , hodge(hodge(vcp3(u,v)) ^ vcp3(w,x)) == sum(u*w)*sum(v*x) - sum(u*x)*sum(v*w) )
Function vector_cross_product()
takes a matrix with $n$ rows and
$n-1$ columns. Here I consider the cases $n=2$ and $n=1$. Firstly,
$n=2$. Going back to Spivak's definition, we see that the cross
product is a unary operation which takes a single vector
$\mathbf{v}\in\mathbb{R}^2$; we might write
${\times}(\mathbf{v})={\times}(v_1,v_2)$. Formally we define
$$ \phi(w)= \mathrm{det} \begin{pmatrix} v_1&v_2\ w_1&w_2 \end{pmatrix} $$
and seek a vector $\mathbf{z}=(z_1,z_2)\in\mathbb{R}^2$ such that $\left\langle\mathbf{w},\mathbf{z}\right\rangle=\phi(\mathbf{w})$. Thus $\phi(\mathbf{w})=v_1w_2-v_2w_1$ and we see $\mathbf{z}=(-v_2,v_1)$. Numerically:
vector_cross_product(rbind(4,7))
We see that the vector cross product of a single vector $\mathbf{v}\in\mathbb{R}^2$ is vector $\mathbf{v}$ rotated by $\pi/2$ counterclockwise; the dot product of $\mathbf{v}$ with ${\times}{\left(\mathbf{v}\right)}$ is zero.
Now we try the even more peculiar case $n=1$, corresponding to a matrix with one row and zero columns. Formally, the cross product is a nullary operation which takes zero vectors $\mathbf{v}\in\mathbb{R}^1$ and returns a "vector" $\mathbf{z}\in\mathbb{R}^1$. The vector cross product in the case $n=1$ is thus a scalar. Again following Spivak we see that $\phi$ is a map from $\mathbb{R}^1$ to the reals, with $\phi(w_1)=\det(w_1)=w_1$; we then seek $z_1\in\mathbb{R}$ such that $\phi(w_1)=\left\langle w_1,z_1\right\rangle$; so $w_1z_1=w_1$ and then $z_1=1$. Matrices with zero columns and one row are easily created in R:
M <- matrix(data=NA,nrow=1,ncol=0) M dput(M)
Function vector_cross_product()
takes such an argument:
vector_cross_product(M)
thus returning scalar 1 as intended. Examining the body of
vector_cross_product
at the head of the document we see that the
function boils down to returning the determinant of
M[-1,,drop=FALSE]
The determinant of a zero-by-zero matrix is equal to 1 [any zero by zero matrix maps $\left\lbrace 0\right\rbrace$ to itself and is thus the identity map, which has by definition a determinant of 1]. Numerically:
det(matrix(NA,0,0))
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