Similarly to how Gaussian elimination putting linear equations into LU form solves most linear algebra problems one might care about, Buchberger’s algorithm for finding a Grobner basis of a system of multivariate polynomial equations solves most questions you might ask. Some fun applications

  • Linkages
  • Geometrical Theorem proving. Circles are x^2 + y^2 - 1 = 0 and so on.
  • Optics
  • Constraint satisfaction problems. x^2 - 1 = 0 gives you a boolean variable. It’s a horrible method but it works if your computer doesn’t explode.
  • Energy and momentum conservation. “Classical Feynman Diagrams” p1 + p2 = p3 + p4 and so on.
  • Frequency domain circuits and linear dynamical systems ;) more on this another day

To learn more about Grobner bases I highly recommend Cox Little O’Shea

To understand what a Grobner basis is, first know that univariate polynomial long division is a thing. It’s useful for determining if one polynomial is a multiple of another. If so, then you’ll find the remainder is zero.

One could want to lift the problem of determining if a polynomial is a multiple of others to multivariate polynomials. Somewhat surprisingly the definition of long division has some choice in it. Sure, x^2 is a term that is ahead of x, but is x a larger term than y? y^2? These different choices are admissible. In addition now one has systems of equations. Which equation do we divide by first? It turns out to matter and change the result. That is unless one has converted into a Grobner Basis.

A Grobner basis is a set of polynomials such that remainder under multinomial division becomes unique regardless of the order in which division occurs.

How does one find such a basis? In essence kind of by brute force. You consider all possible polynomials that could divide two ways depending on your choice.

Julia has packages for multivariate polynomials. defines an abstract interface and generic functions. DynamicPolynomials gives flexible representation for construction. TypedPolynomials gives a faster representation.

These already implement a bulk of what we need to get a basic Buchberger going: Datastructures, arithmetic, and division with remainder. With one caveat, there is already a picked monomial ordering. And it’s not lexicographic, which is the nice one for eliminating variables. This would not be too hard to change though?

Polynomial long division with respect to a set of polynomials is implemented here

Unfortunately, (or fortunately? A good learning experience. Learned some stuff about datastructures and types in julia so that’s nice) quite late I realized that a very similar Grobner basis algorithm to the below is implemented inside of of SemiAlgebraic.jl package. Sigh.

using MultivariatePolynomials
using DataStructures

function spoly(p,q)
    pq = lcm(leadingmonomial(p),leadingmonomial(q))
    return div(  pq , leadingterm(p) ) * p - div(pq , leadingterm(q)) * q

function isgrobner(F::Array{T}) where {T <: AbstractPolynomialLike} # check buchberger criterion
    for (i, f1) in enumerate(F)
        for f2 in F[i+1:end]
            s = spoly(f1,f2)
            _,s = divrem(s,F)
            if !iszero(s)
                return false
    return true

function buchberger(F::Array{T}) where {T <: AbstractPolynomialLike}
    pairs = Queue{Tuple{T,T}}()
    # intialize with all pairs from F
    for (i, f1) in enumerate(F)
        for f2 in F[i+1:end]
            enqueue!(pairs, (f1,f2))

    # consider all possible s-polynomials and reduce them
    while !isempty(pairs)
        (f1,f2) = dequeue!(pairs)
        s = spoly(f1,f2)
        _,s = divrem(s,F)
        if !iszero(s) #isapproxzero? Only add to our set if doesn't completely reduce
            for f in F
                enqueue!(pairs, (s,f))

    # reduce redundant entries in grobner basis.
    G = Array{T}(undef, 0)
    while !isempty(F)
        f = pop!(F)
        _,r = divrem(f, vcat(F,G))
        if !iszero(r)

    return G

Some usage. You can see here that Gaussian elimination implemented by the backslash operator is a special case of taking the Grobner basis of a linear set of equations

using DynamicPolynomials
@polyvar x y

buchberger( [ x + 1.0 + y   , 2.0x + 3y + 7  ] )
2-element Array{Polynomial{true,Float64},1}:
 -0.5y - 2.5
 x - 4.0

[ 1 1 ; 2  3 ] \ [-1 ; -7]
2-element Array{Float64,1}:

buchberger( [ x^3 - y , x^2 - x*y ])
3-element Array{Polynomial{true,Int64},1}:
 -xy + y²
 y³ - y
 x² - y²


Many. This is not a good Buchberger implementation, but it is simple. See for some tips, which include criterion for avoiding unneeded spolynomial pairs, and smart ordering. Better Buchberger implementations will use the f4 or f5 algorithm, which use sparse matrix facilities to perform many division steps in parallel. My vague impression of this f4 algorithm is that you prefill a sparse matrix (rows correspond to an spolynomial or monomial multiple of your current basis, columns correspond to monomials) with monomial multiples of your current basis that you know you might need.

In my implementation, I’m tossing away the div part of divrem. It can be useful to retain these so you know how to write your Grobner basis in terms of the original basis.

You may want to look at the julia bindings to Singular.jl