
Altfunction (S, give_kform = FALSE)
{
if (is.kform(S)) {
return(S)
}
if (give_kform) {
return(kform(S)/factorial(arity(S)))
}
out <- kill_trivial_rows(S)
if (nrow(index(out)) == 0) {
return(S * 0)
}
ktensor(include_perms(consolidate(out))/factorial(ncol(index(out))))
}
To cite the stokes package in publications, please use
Hankin (2022); this function monograph
discusses function Alt(). Spivak (1965), in a memorable passage,
states:
A -tensor is called alternating if
The set of all alternating -tensors is clearly a subspace of . Since it requires considerable work to produce the determinant, it is not surprising that alternating -tensors are difficult to write down. There is, however, a uniform way of expressing all of them. Recall that the sign of a permutation , denoted , is if is even and if is odd. If , we define by
where is the set of all permutations of numbers to .
- Michael Spivak, 1969 (Calculus on Manifolds, Perseus books). Page 78
For example, if we have
$$\operatorname{Alt}(T)\left(v_1,v_2,v_3\right)= \frac{1}{6}\left(\begin{array}{cc} +T{\left(v_1,v_2,v_3\right)}& -T{\left(v_1,v_3,v_2\right)}\cr -T{\left(v_2,v_1,v_3\right)}& +T{\left(v_2,v_3,v_1\right)}\cr +T{\left(v_3,v_1,v_2\right)}& -T{\left(v_3,v_2,v_1\right)} \end{array} \right) $$
In the stokes package, function Alt()
performs this operation on a given
-tensor
.
Idiom is straightforward:
S <- as.ktensor(rbind(c(1,7,8)))*6 # the "6" is to stop rounding error
S## A linear map from V^3 to R with V=R^8:
## val
## 1 7 8 = 6
Above, object
,
where
,
with
[
are basis vectors in
].
We may calculate
in the package using Alt():
Alt(S)## A linear map from V^3 to R with V=R^8:
## val
## 8 7 1 = -1
## 8 1 7 = 1
## 7 8 1 = 1
## 7 1 8 = -1
## 1 8 7 = -1
## 1 7 8 = 1
Above we see that
, and
we can see the pattern clearly, observing in passing that the order of
the rows in the R object is arbitrary (as per disordR
discipline). Now, Alt(S) is an alternating form, which is
easy to verify, by making it act on an odd permutation of a set of
vectors:
V <- matrix(rnorm(24),ncol=3)
c(as.function(Alt(S))(V),as.function(Alt(S))(V[,c(2,1,3)]))## [1] -0.1645455 0.1645455
Observing that is linear, we might apply it to a more complicated tensor, here with two terms:
(S <- as.ktensor(rbind(c(1,2,4),c(2,2,3)),c(12,1000)))## A linear map from V^3 to R with V=R^4:
## val
## 2 2 3 = 1000
## 1 2 4 = 12
Above we have . Calculating :
S## A linear map from V^3 to R with V=R^4:
## val
## 2 2 3 = 1000
## 1 2 4 = 12
Alt(S)## A linear map from V^3 to R with V=R^4:
## val
## 4 2 1 = -2
## 4 1 2 = 2
## 2 4 1 = 2
## 2 1 4 = -2
## 1 4 2 = -2
## 1 2 4 = 2
Note that the 2 2 3 term (with coefficient 1000)
disappears in Alt(S): the even permutations (being
positive) cancel out the odd permutations (being negative) in pairs.
This must be the case for an alternating map by definition. We can see
why this cancellation occurs by considering
,
a linear map corresponding to the [2 2 3] row of
S:
(where is any real number and ββ denotes the dot product), and so
Thus if we require
to be alternating [that is,
], we must have
,
which is why the [2 2 3] term with coefficient 1000
vanishes (such terms are killed in the function by applying
kill_trivial_rows()). We can check that terms with repeated
entries are correctly discarded by taking the
of a tensor all of whose entries include at least one repeat:
S <- as.ktensor(matrix(c(
3,2,1,1,
1,4,1,4,
1,1,2,3,
7,7,4,7,
1,2,3,3
),ncol=4,byrow=TRUE),1:5)
S## A linear map from V^4 to R with V=R^7:
## val
## 1 2 3 3 = 5
## 7 7 4 7 = 4
## 1 1 2 3 = 3
## 1 4 1 4 = 2
## 3 2 1 1 = 1
Alt(S)## The zero linear map from V^4 to R with V=R^n:
## empty sparse array with 4 columns
We should verify that Alt() does indeed return an
alternating tensor for complicated tensors (this is trivial
algebraically because
is linear, but it is always good to check):
S <- rtensor(k=5,n=9)
S## A linear map from V^5 to R with V=R^9:
## val
## 6 1 1 1 2 = 9
## 8 6 7 6 6 = 8
## 7 6 8 7 3 = 7
## 8 2 7 7 7 = 5
## 9 8 6 9 9 = 4
## 6 6 8 9 1 = 3
## 9 2 6 4 7 = 6
## 7 3 3 8 6 = 2
## 9 7 3 4 5 = 1
Then we swap columns of , using both even and odd permutations, to verify that is in fact alternating:
V_even <- V[,c(1,2,5,3,4)] # an even permutation
V_odd <- V[,c(2,1,5,3,4)] # an odd permutation
V_rep <- V[,c(2,1,5,2,4)] # not a permutation
c(as.function(AS)(V),as.function(AS)(V_even)) # should be identical (even permutation)## [1] 0.226959 0.226959
c(as.function(AS)(V),as.function(AS)(V_odd)) # should differ in sign only (odd permutation)## [1] 0.226959 -0.226959
as.function(AS)(V_rep) # should be zero## [1] -1.01644e-20
Alt()
In his theorem 4.3, Spivak proves the following statements:
We have demonstrated the first point above. For the second, we need
to construct a tensor that is alternating, and then show that
Alt() does not change it:
P <- as.ktensor(1+diag(2),c(-7,7))
P## A linear map from V^2 to R with V=R^2:
## val
## 1 2 = 7
## 2 1 = -7
P == Alt(P)## [1] TRUE
The third point, idempotence is also easy:
## [1] TRUE
(Main article: wedge product). Spivak defines the wedge product as follows. Given alternating forms and we have
So for example:
omega <- as.ktensor(2+diag(2),c(-7,7))
eta <- Alt(ktensor(6*spray(matrix(c(1,2,3,1,4,7,4,5,6),3,3,byrow=TRUE),1:3)))
omega## A linear map from V^2 to R with V=R^3:
## val
## 2 3 = 7
## 3 2 = -7
eta## A linear map from V^3 to R with V=R^7:
## val
## 1 4 7 = 2
## 3 2 1 = -1
## 4 6 5 = -3
## 1 7 4 = -2
## 4 5 6 = 3
## 7 1 4 = 2
## 4 1 7 = -2
## 4 7 1 = 2
## 6 5 4 = -3
## 6 4 5 = 3
## 5 4 6 = -3
## 1 3 2 = -1
## 2 1 3 = -1
## 1 2 3 = 1
## 2 3 1 = 1
## 7 4 1 = -2
## 3 1 2 = 1
## 5 6 4 = 3
Above we see that omega is alternating by construction,
and eta is alternating by virtue of Alt().
Thus tensor
is defined as per Spivakβs definition, and we may calculate it
directly:
## An alternating linear map from V^5 to R with V=R^7:
## val
## 2 3 4 5 6 = 2.1
## 1 2 3 4 7 = 1.4
f <- as.function(Alt(omega %X% eta))Observe that the tensor
(which would be returned if the default argument
give_kform=FALSE was sent to Alt()) is quite
long, having
nonzero components, which is why it is not printed in full. We may
verify that f() is alternating by evaluating it on a
randomly chosen point in
:
## [1] -0.852457 0.852457
Above we see a verification that f() is fact
alternating: writing the matrix V in terms of its five
vectors as
,
where
,
we see that
.
Alt()
Spivak goes on to prove the following three statements. If are tensors and are alternating tensors of arity respectively, then
Taking the points in turn. Firstly :
(S <- as.ktensor(rbind(c(1,2,3,3),c(1,1,2,3)),1000:1001)) ## A linear map from V^4 to R with V=R^3:
## val
## 1 1 2 3 = 1001
## 1 2 3 3 = 1000
Alt(S) # each row of S includes repeats## The zero linear map from V^4 to R with V=R^n:
## empty sparse array with 4 columns
## [1] TRUE TRUE
secondly, :
omega <- Alt(as.ktensor(rbind(1:3),6))
eta <- Alt(as.ktensor(rbind(4:5),60))
theta <- Alt(as.ktensor(rbind(6:7),14))
omega## A linear map from V^3 to R with V=R^3:
## val
## 3 2 1 = -1
## 3 1 2 = 1
## 2 3 1 = 1
## 2 1 3 = -1
## 1 3 2 = -1
## 1 2 3 = 1
eta## A linear map from V^2 to R with V=R^5:
## val
## 5 4 = -30
## 4 5 = 30
theta## A linear map from V^2 to R with V=R^7:
## val
## 7 6 = -7
## 6 7 = 7
f1 <- as.function(Alt(Alt(omega %X% eta) %X% theta))
f2 <- as.function(Alt(omega %X% eta %X% theta))
f3 <- as.function(Alt(omega %X% Alt(eta %X% theta)))
V <- matrix(rnorm(9*14),ncol=9)
c(f1(V),f2(V),f3(V))## [1] -3.154794 -3.154794 -3.154794
Verifying the third identity needs us to coerce from a -form to a -tensor:
omega <- rform(2,2,19)
eta <- rform(3,2,19)
theta <- rform(2,2,19)
a1 <- as.ktensor(omega ^ (eta ^ theta))
a2 <- as.ktensor((omega ^ eta) ^ theta)
a3 <- Alt(as.ktensor(omega) %X% as.ktensor(eta) %X% as.ktensor(theta))*90
c(is.zero(a1-a2),is.zero(a1-a3),is.zero(a2-a3))## [1] TRUE TRUE TRUE
give_kform
Function Alt() takes a Boolean argument
give_kform. We have been using Alt() with
give_kform taking its default value of FALSE,
which means that it returns an object of class ktensor.
However, an alternating form can be much more efficiently represented as
an object of class kform, and this is returned if
give_kform is TRUE. Here I verify that the two
options return identical objects:
(rand_tensor <- rtensor(k=5,n=9)*120)## A linear map from V^5 to R with V=R^9:
## val
## 7 3 6 4 7 = 120
## 8 1 3 4 4 = 240
## 2 6 4 5 7 = 360
## 8 4 2 8 8 = 840
## 7 1 3 5 1 = 480
## 1 6 4 7 6 = 600
## 6 7 3 3 4 = 1080
## 9 2 3 5 3 = 720
## 6 5 6 5 4 = 960
## A ktensor object with 120 terms. Summary of coefficients:
##
## a disord object with hash 866c13390582e2f7e562be3e046abda44be30079
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3 -3 0 0 3 3
##
##
## Representative selection of index and coefficients:
##
## A linear map from V^5 to R with V=R^7:
## val
## 7 6 5 4 2 = 3
## 2 6 4 7 5 = -3
## 7 4 2 6 5 = 3
## 2 7 6 4 5 = -3
## 4 6 2 5 7 = -3
## 6 5 2 4 7 = -3
Above, we see that S1 is a rather extensive object, with
120 terms. However, if argument give_kform = TRUE is passed
to Alt() we get a kform object which is much
more succinct:
(SA1 <- Alt(rand_tensor,give_kform=TRUE))## An alternating linear map from V^5 to R with V=R^7:
## val
## 2 4 5 6 7 = 3
Verification that objects S1 and SA1 are
the same object:
V <- matrix(rnorm(45),ncol=5)
LHS <- as.function(S1)(V)
RHS <- as.function(SA1)(V)
c(LHS=LHS,RHS=RHS,diff=LHS-RHS)## LHS RHS diff
## 2.386928e+01 2.386928e+01 -1.065814e-14
Above we see agreement to within numerical error.