The first explicit example of nonnegative polynomial that is not a sum of squares was found by Motzkin in 1967. By the Arithmetic-geometric mean, $$ \frac{x^4y^2 + x^2y^4 + 1}{3} \ge \sqrt[3]{x^4y^2 \cdot x^2y^4 \cdot 1} = x^2y^2 $$ hence $$ x^4y^2 + x^2y^4 + 1 - 3x^2y^2 \ge 0. $$ The code belows construct the Motzkin polynomial using DynamicPolynomials.

In [1]:
using DynamicPolynomials
@polyvar x y
motzkin = x^4*y^2 + x^2*y^4 + 1 - 3x^2*y^2
x^4y^2 + x^2y^4 - 3x^2y^2 + 1

The Motzkin polynomial is nonnegative but is not a sum of squares as we can verify numerically as follows. We first need to pick an SDP solver, see here for a list of the available choices.

In [ ]:
using CSDP
solver = CSDPSolver();
In [2]:
using Mosek
solver = MosekSolver(LOG=0);
In [3]:
using SumOfSquares
using JuMP
m = SOSModel(solver = solver)
@constraint m motzkin >= 0 # We constraint `motzkin` to be a sum of squares
solve(m) # Returns the status `:Infeasible`
Warning: Not solved to optimality, status: Infeasible

Even if the Motzkin polynomial is not a sum of squares, it can still be certified to be nonnegative using sums of squares. Indeed a polynomial is certified to be nonnegative if it is equal to a fraction of sums of squares. The Motzkin polynomial is equal to a fraction of sums of squares whose denominator is $x^2 + y^2$. This can be verified numerically as follows:

In [4]:
using SumOfSquares
using JuMP
m = SOSModel(solver = solver)
@constraint m (x^2 + y^2) * motzkin >= 0 # We constraint the `(x^2 + y^2) * motzkin` to be a sum of squares
solve(m) # Returns the status `:Optimal` which means that it is feasible

One may consider ourself lucky to have had the intuition that $x^2 + y^2$ would work as denominator. In fact, the search for the denominator can be carried out in parallel to the search of the numerator. In the example below, we search for a denominator with monomials of degrees from 0 to 2. If none is found, we can increase the maximum degree 2 to 4, 6, 8, ... This gives a hierarchy of programs to try in order to certify the nonnegativity of a polynomial by identifying it with a fraction of sum of squares polynomials. In the case of the Motzkin polynomial we now that degree 2 is enough since $x^2 + y^2$ works.

In [5]:
using SumOfSquares
using JuMP
using MultivariatePolynomials
m = SOSModel(solver = solver)
X = monomials([x, y], 0:2)
# We create a quadratic polynomial that is not necessarily a sum of squares
# since this is implied by the next constraint: `deno >= 1`
@variable m deno Poly(X)
# We want the denominator polynomial to be strictly positive,
# this prevents the trivial solution deno = 0 for instance.
@constraint m deno >= 1
@constraint m deno * motzkin >= 0

We can check the denominator found by the program using JuMP.getvalue

In [6]:
0.8994524919313149x^2 - 8.417376223825856e-11xy + 0.8994524979159756y^2 + 6.987367755126592e-16x - 1.0014392160847468e-15y + 1.9999999943329119

Because a picture is worth a thousand words let's plot the beast. We can easily extend Plots by adding a recipe to plot bivariate polynomials.

In [ ]:
using RecipesBase
using MultivariatePolynomials
@recipe function f(x::AbstractVector, y::AbstractVector, p::Polynomial)
    x, y, (x, y) -> p(variables(p) => [x, y])
In [ ]:
using Plots
plot(linspace(-2, 2, 100), linspace(-2, 2, 100), motzkin, st = [:surface], seriescolor=:heat, colorbar=:none, clims = (-10, 80))
In [ ]: