# Antiassociative algebras with R: the `evitaicossa` package

#### Robin K. S. Hankin

Here I introduce the `evitaicossa` R package for antiassociative algebras. An algebra is a vector space in which the vectors possess a bilinear product. Formally, a vector space is a set \(V\) of vectors which form an Abelian group under addition and also satisfy the following axioms:

- Compatibility, \(a(b\mathbf{v})=(ab)\mathbf{v}\)
- Identity, \(1\mathbf{v}=\mathbf{v}\)
- Distributivity WRT vector addition, \(a(\mathbf{u}+\mathbf{v}) = a\mathbf{u}+a\mathbf{v}\)
- Distributivity WRT field addition, \((a+b)\mathbf{u} = a\mathbf{u}+b\mathbf{u}\)

Above, \(\mathbf{u},\mathbf{v}\in V\) are vectors, \(a,b\) are scalars [here the real numbers], and \(1\) is the multiplicative identity. We also require a bilinear vector product, mapping pairs of vectors to vectors; vector multiplication is denoted using juxtaposition, as in \(\mathbf{u}\mathbf{v}\), which satisfies the following axioms:

- Right distributivity, \((\mathbf{u}+\mathbf{v})\mathbf{w} = \mathbf{u}\mathbf{w}+ \mathbf{u}\mathbf{w}\)
- Left distributivity, \(\mathbf{w}(\mathbf{u}+\mathbf{v}) = \mathbf{w}\mathbf{u}+ \mathbf{w}\mathbf{v}\)
- Compatibility, \((a\mathbf{u})(b\mathbf{v})=(ab)(\mathbf{u}\mathbf{v})\)

Note the absence of commutativity and associativity. Associative algebras seem to be the most common, and examples would include multivariate polynomials Hankin (2022e), Clifford algebras (Hankin 2022a), Weyl algebras (Hankin 2022d), and free algebras (Hankin 2022f). Non-associative algebras would include the octonions (Hankin 2006) and Jordan algebras (Hankin 2023). Here I consider antiassociative algebras in which the usual associativity relation \(\mathbf{u}(\mathbf{v}\mathbf{w})=(\mathbf{u}\mathbf{v})\mathbf{w}\) is replaced by the relation \(\mathbf{u}(\mathbf{v}\mathbf{w})=-(\mathbf{u}\mathbf{v})\mathbf{w}\).

## Antiassociative algebras

Algebras satisfying \(\mathbf{u}(\mathbf{v}\mathbf{w})=-(\mathbf{u}\mathbf{v})\mathbf{w}\) exhibit some startling behaviour. Firstly, in the vector space there are no scalars except for \(0\in\mathbb{R}\). Proof: for any \(x\in\mathbb{R}\), we have \(x^3=x(xx)=-(xx)x=-x^3\); thus \(x^3=-x^3\), so \(x=0\). Secondly, antiassociative algebras are nilpotent of order 4:

\[ (\mathbf{a}\mathbf{b})(\mathbf{c}\mathbf{d}) = -\mathbf{a}(\mathbf{b}(\mathbf{c}\mathbf{d})) = \mathbf{a}((\mathbf{b}\mathbf{c})\mathbf{d}) = -(\mathbf{a}(\mathbf{b}\mathbf{c}))\mathbf{d} = ((\mathbf{a}\mathbf{b})\mathbf{c})\mathbf{d} = -(\mathbf{a}\mathbf{b})(\mathbf{c}\mathbf{d}) \]

We see that \((\mathbf{a}\mathbf{b})(\mathbf{c}\mathbf{d})=-(\mathbf{a}\mathbf{b})(\mathbf{c}\mathbf{d})\) so \(\mathbf{a}\mathbf{b}\mathbf{c}\mathbf{d}\) (however bracketed) must be zero.

## The free antiassociative algebra

We consider vector spaces generated by a finite alphabet of symbols \(\mathbf{x}_1,\ldots,\mathbf{x}_n\). These will be denoted generally by a single letter, as in \(\mathbf{a},\mathbf{b},\ldots,\mathbf{z}\). We now consider the algebra spanned by products of linear combinations of these symbols, subject only to the axioms of an algebra [and the antiassociative relation \(\mathbf{u}(\mathbf{v}\mathbf{w})=-(\mathbf{u}\mathbf{v})\mathbf{w}\)]. Given an alphabet \(\mathbf{x}_1,\ldots,\mathbf{x}_n\), the general form of an element of an antiassociative algebra will be

\[ \sum_{i}\alpha_i\mathbf{x}_i + \sum_{i,j}\alpha_{ij}\mathbf{x}_i\mathbf{x}_j+ \sum_{i,j,k}\alpha_{ijk}(\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k \]

(see Remm (2024) for a proof, but note that she uses \(\mathbf{x}_i(\mathbf{x}_j\mathbf{x}_k)\) rather than \((\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k\) for the triple products; a brief discussion is given in the appendix). In the package, the components of the first term \(\sum_{i}\alpha_i\mathbf{x}_i\) are known as “single-symbol” terms [\(\mathbf{x}_i\)] and coefficients [\(\alpha_i\)] respectively. Similarly, the components of \(\sum_{i,j}\alpha_{ij}\mathbf{x}_i\mathbf{x}_j\) are known as the “double-symbol” terms and coefficients; and the components of \(\sum_{i,j,k}\alpha_{ijk}(\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k\) are the “triple-symbol” terms and coefficients.

Addition is performed elementwise among the single-, double-, and triple- components; the result is the (formal) composition of the three results. Given

\[A= \sum_{i}\alpha_i\mathbf{x}_i + \sum_{i,j}\alpha_{ij}\mathbf{x}_i\mathbf{x}_j+ \sum_{i,j,k}\alpha_{ijk}(\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k \]

\[B= \sum_{i}\beta_i\mathbf{x}_i + \sum_{i,j}\beta_{ij}\mathbf{x}_i\mathbf{x}_j+ \sum_{i,j,k}\beta_{ijk}(\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k \]

(where the sums run from \(1\) to \(n\)), we define the sum \(A+B\) to be

\[ \sum_{i}(\alpha_i+\beta_i)\mathbf{x}_i + \sum_{i,j}(\alpha_{ij}+\beta_{ij})\mathbf{x}_i\mathbf{x}_j+ \sum_{i,j,k}(\alpha_{ijk}+\beta_{ijk})(\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k \]

Multiplication is slightly more involved. We define the product \(AB\) to be

\[ \sum_{i,j}\alpha_i\beta_{ij}\mathbf{x}_i\mathbf{x}_j +\sum_{i,j,k}\alpha_{ij}\beta_{k}(\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k -\sum_{i,j,k}\alpha_i\beta_{jk}(\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k. \]

The minus sign in front of the third term embodies antiassociativity.

## The `evitaicossa` package

The `evitaicossa` package implements these relations in the context of an R-centric suite of software. I give some examples of the package in use. A good place to start is function `raaa()`, which returns a simple random element of the free antiassociative algebra:

    raaa()

    ## free antiassociative algebra element:
    ## +1a +1c +2d +1b.c +1c.b +1c.c +2(b.a)a +1(b.b)c +1(b.c)a

(the default alphabet for this command is \(\left\lbrace\mathbf{a},\mathbf{b},\mathbf{c},\mathbf{d}\right\rbrace\)). We see the print method for the package which shows some of the structure of the object. This one has some single-symbol elements, some double-symbol and some triple-symbol elements.

It is possible to create elements using the `aaa()` or `as.aaa()` functions:

    x  <- as.aaa(c("p","q","r"))
    x1 <- aaa(s1 = c("p","r","x"),c(-1,5,6))
    y <- aaa(d1 = letters[1:3],d2 = c("foo","bar","baz"),dc=1:3)
    z <- aaa(
        t1 = c("bar","bar","bar"),
        t2 = c("q","r","s"),
        t3 = c("foo","foo","bar"),
        tc = 5:7)

And then apply arithmetic operations to these objects:

    x

    ## free antiassociative algebra element:
    ## +1p +1q +1r

    x1

    ## free antiassociative algebra element:
    ## -1p +5r +6x

    x+x1

    ## free antiassociative algebra element:
    ## +1q +6r +6x

(above, note the cancellation in `x+x1`). Multiplication is also implemented (package idiom is to use an asterisk `*`):

    x*(x1+y)

    ## free antiassociative algebra element:
    ## -1p.p +5p.r +6p.x -1q.p +5q.r +6q.x -1r.p +5r.r +6r.x -1(p.a)foo -2(p.b)bar
    ## -3(p.c)baz -1(q.a)foo -2(q.b)bar -3(q.c)baz -1(r.a)foo -2(r.b)bar -3(r.c)baz

Check:

    x*(x1+y) == x*x1 + x*y

    ## [1] TRUE

We end with a remarkable identity:

\[ (\mathbf{a}+\mathbf{a}\mathbf{x})(\mathbf{b} + \mathbf{x}\mathbf{b})=\mathbf{a}\mathbf{b} \]

Numerically:

    a <- raaa()
    b <- raaa()
    x <- raaa()
    (a+a*x)*(b+x*b) == a*b

    ## [1] TRUE

## Extract and replace methods

Because of the tripartite nature of antiassociative algebra, the package provides three families of extraction methods: `single()`, `double()` and `triple()`, which return the different components of an object:

    a

    ## free antiassociative algebra element:
    ## +4a +1b +2a.b +2c.c +2d.d +1(c.d)a +4(d.b)c +4(d.d)b

    single(a)

    ## free antiassociative algebra element:
    ## +4a +1b

    double(a)

    ## free antiassociative algebra element:
    ## +2a.b +2c.c +2d.d

    triple(a)

    ## free antiassociative algebra element:
    ## +1(c.d)a +4(d.b)c +4(d.d)b

The corresponding replacement methods are also implemented:

    single(a) <- 0
    a

    ## free antiassociative algebra element:
    ## +2a.b +2c.c +2d.d +1(c.d)a +4(d.b)c +4(d.d)b

    double(a) <- double(b) * 1000
    a

    ## free antiassociative algebra element:
    ## +2000b.b +1000b.d +2000c.d +1(c.d)a +4(d.b)c +4(d.d)b

Square bracket extraction and replacement is also implemented:

    (a <- raaa(s=5))

    ## free antiassociative algebra element:
    ## +4a +9c +3b.c +1b.d +2c.b +3d.a +2d.c +2(a.d)d +3(b.a)a +3(b.c)d +1(c.d)d
    ## +3(d.c)b

    a[s1=c("c","e"),t1="c",t2="d",t3="d"]

    ## free antiassociative algebra element:
    ## +9c +1(c.d)d

Above we pass named arguments ( et seq.) and the appropriate `aaa` object is returned. Zero coeffients are discarded. This mode also implements replacement methods:

    (a <- raaa(s=5))

    ## free antiassociative algebra element:
    ## +8a +3b +1a.b +3b.c +4c.c +4c.d +2(a.c)c +4(b.c)a +4(b.c)d +4(c.a)c +1(c.d)d

    a[s1="a",d1=c("c","w"),d2=c("d","w")] <- 888
    a

    ## free antiassociative algebra element:
    ## +888a +3b +1a.b +3b.c +4c.c +888c.d +888w.w +2(a.c)c +4(b.c)a +4(b.c)d +4(c.a)c
    ## +1(c.d)d

The other square bracket method is to pass an (unnamed) character vector:

    (a <- raaa())

    ## free antiassociative algebra element:
    ## +1a +2c +2d +3b.a +3b.b +3c.c +2(a.d)d +2(b.c)a +4(c.d)b

### Note on `disordR` discipline

If we try to access the symbols or coefficients of an `aaa` object [functions `s1()` and `sc()` respectively], we get a `disord` object (Hankin 2022b). Suppose we wish to extract the single-symbol terms and the single-symbol coefficients:

    x

    ## free antiassociative algebra element:
    ## +5c +1d +3a.c +4d.a +1d.b +1(a.a)d +2(b.d)a +3(d.d)a

    s1(x)

    ## A disord object with hash 059117d244e1291a3c3e4e94c5ad5bbc0ed7c254 and elements
    ## [1] "c" "d"
    ## (in some order)

    sc(x)

    ## A disord object with hash 059117d244e1291a3c3e4e94c5ad5bbc0ed7c254 and elements
    ## [1] 5 1
    ## (in some order)

See how the hash codes of the symbols and coeffients match. However, the double-symbol terms and coefficients, while internally matching, differ from the single-symbol stuff:

    list(d1(x),d2(x),dc(x))

    ## [[1]]
    ## A disord object with hash c3d0273c48d84843b58af77622164b0c0a319848 and elements
    ## [1] "a" "d" "d"
    ## (in some order)
    ##
    ## [[2]]
    ## A disord object with hash c3d0273c48d84843b58af77622164b0c0a319848 and elements
    ## [1] "c" "a" "b"
    ## (in some order)
    ##
    ## [[3]]
    ## A disord object with hash c3d0273c48d84843b58af77622164b0c0a319848 and elements
    ## [1] 3 4 1
    ## (in some order)

Above, see how the double-symbol terms and double-symbol coefficients have consistent hashes, but do not match the single-symbol objects (or indeed the triple-symbol objects).

## Matrix index extraction

If square bracket extraction is given an index that is a matrix, it is interpreted rowwise:

    l <- letters[1:3]
    (a <- aaa(s1=l,sc=1:3, d1=l,d2=rev(l),dc=3:1,t1=l,t2=l,t3=rev(l),tc=1:3))

    ## free antiassociative algebra element:
    ## +1a +2b +3c +3a.c +2b.b +1c.a +1(a.a)c +2(b.b)b +3(c.c)a

    a[cbind(l,l)]

    ## free antiassociative algebra element:
    ## +2b.b

    a[cbind(rev(l),l,l)] <- 88
    a

    ## free antiassociative algebra element:
    ## +1a +2b +3c +3a.c +2b.b +1c.a +1(a.a)c +88(a.c)c +88(b.b)b +88(c.a)a +3(c.c)a

## Note on generalized antiassociativity

We may generalize antiassociativity to \(\mathbf{a}(\mathbf{b}\mathbf{c})=k(\mathbf{a}\mathbf{b})\mathbf{c}\). Thus associativity is recovered if \(k=1\) and antiassociativity if \(k=-1\). Then the nilpotence argument becomes:

\[ (\mathbf{a}\mathbf{b})(\mathbf{c}\mathbf{d}) = k^{-1}\mathbf{a}(\mathbf{b}(\mathbf{c}\mathbf{d})) = \mathbf{a}((\mathbf{b}\mathbf{c})\mathbf{d}) = k(\mathbf{a}(\mathbf{b}\mathbf{c}))\mathbf{d} = k^2((\mathbf{a}\mathbf{b})\mathbf{c})\mathbf{d} = k(\mathbf{a}\mathbf{b})(\mathbf{c}\mathbf{d}) \]

The value of \(k\) may be set at compile-time by editing file `src/anti.h`. The line in question reads:

    #define K -1 // a(bc) == K(ab)c

but it is possible to change the value of `K`. Note that this will cause `test_aac.R`, one of the `testthat` suite, to fail `R CMD check`.

### Appendix

As noted above, Remm (2024) uses \(\mathbf{x}_i(\mathbf{x}_j\mathbf{x}_k)\) rather than \((\mathbf{x}_i\mathbf{x}_j)\mathbf{x}_k\) for the triple products. I chose the latter because R idiom for multiplication is left associative:

    x <- 3
    class(x) <- "foo"
    `*.foo` <- function(x,y){x + y + x}
    print.foo <- function(x){print(unclass(x))}
    c(`(x*x)*x` = (x*x)*x,  `x*(x*x)` = x*(x*x),  `x*x*x` = x*x*x)

    ## (x*x)*x x*(x*x)   x*x*x
    ##      21      15      21

Above we see that `x*x*x` is interpreted as `(x*x)*x`, which is why the sign convention in the package was adopted.

## References

Hankin, R. K. S. 2006. “Normed Division Algebras with R: Introducing the Onion Package.” R News 6 (2): 49–52.
———. 2022a. “Clifford Algebra in R.” https://arxiv.org/abs/2209.13659; arXiv. https://doi.org/10.48550/ARXIV.2209.13659.
———. 2022b. “Disordered Vectors in R: Introducing the `disordR` Package.” https://arxiv.org/abs/2210.03856; arXiv. https://doi.org/10.48550/ARXIV.2210.03856.
———. 2022c. “Fast Multivariate Polynomials in R: The `mvp` Package.” arXiv. https://doi.org/10.48550/ARXIV.2210.15991.
———. 2022d. “Quantum Algebra in R: The Weyl Package.” arXiv. https://doi.org/10.48550/ARXIV.2212.09230.
———. 2022e. “Sparse Arrays in R: The `spray` Package.” https://arxiv.org/abs/2210.03856; arXiv. https://doi.org/10.48550/ARXIV.2210.10848.
———. 2022f. “The Free Algebra in R.” arXiv. https://doi.org/10.48550/ARXIV.2211.04002.
———. 2023. “Jordan Algebra in R.” arXiv. https://doi.org/10.48550/arXiv.2303.06062.
Remm, Elisabeth. 2024. “Anti-Associative Algebras.” https://arxiv.org/abs/2202.10812.