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Monte Carlo integration

Integrating functions on a variety

Consider the problem of numerically approximating an integral of the form

$$\int_{V} f(x) \mathrm{d}x,$$

where $V\subset \mathbb{R}^N$ is an open submanifold of an algebraic variety and $f:V\to \mathbb{R}$ is a measurable function.

For instance, for $f(x)=1$, this integral gives the volume of $V$. In this example, we want to use homotopy continuation to approximate this integral. We will use the Monte Carlo integration proposed in this article.

Integrating functions with homotopy continuation

Let $n=\mathrm{dim} V$, and let $A\in \mathbb{R}^{n\times N}$ and $b\in \mathbb{R}^n$ be a matrix-vector pair with independent standard Gaussian entries. Then, almost surely, $\{x\in \mathbb{R}^N : Ax=b\}$ is a linear space of dimension $N-n$, which intersects $V$ in finitely many points.

Consider the random variable

$$\overline{f}(A,b):= \sum_{x\in V: Ax=b} \frac{f(x)}{\alpha(x)},$$


$$\alpha(x) = \frac{\Gamma(\frac{n+1}{2})}{\sqrt{\pi}^{n+1}} \frac{\sqrt{1+\langle x, \pi_x x\rangle}}{(1+\Vert x\Vert)^{\frac{n+1}{2}}}$$

and where $n=\mathrm{dim}(V)$, and where $\pi_x$ is the orthogonal projection onto the normal space of $V$ at $x$. The main theorem of the article yields

$$\mathbb{E} \overline{f}(A,b) = \int_V f(x) \mathrm{d} x.$$

The empirical mean $\mathrm{E}(f,k) = \frac{1}{k}(\overline{f}(A_1,b_1) + \cdots \overline{f}(A_k,b_k))$ for $k$ sample pairs $(A_1,b_1),\ldots, (A_k,b_k)$ converges to the integral of $f$ over $V$ as $k\to \infty$.

Example: volume of a planar curve

Let us now use the empirical mean of $\overline{f}(A,b)$ for $f(x)=1$ to approximate the volume of the curve $C = \{x^4+y^4-3x^2-xy^2-x+1 = 0\}$. A plot of the curve is shown next.

Let us define the equation for $C$ in Julia.

using HomotopyContinuation, LinearAlgebra, Statistics
@var x[1:2]
F = System([x[1]^4+x[2]^4-3x[1]^2-x[1]*x[2]^2-x[2]+1])

We also define $\overline{f}$.

f(x) = 1.0 # so that ∫ f(x) dx gives the volume

using SpecialFunctions
N, n = 2, 1
Γ = SpecialFunctions.gamma((n+1)/2) / sqrt(pi)^(n+1)
J = differentiate(F, x)

#define f̄
    if nreal(R) == 0
        return 0.0
        return sum(z -> f(z) / α(z, J), real_solutions(R))
function α(z, J)
    N = normalize!([j(x=>z) for j in J])
    Γ * sqrt(1 + (z  N)^2) / sqrt(1 + z⋅z)^(n+1)

For computing $f(A,b)$ we have to intersect $V$ with linear spaces. Following this guide we first create a suitable start system by intersecting $V$ with a random complex linear space of dimension $N-n$.

L₀ = rand_subspace(N; dim = N - n)
start = solve(F, target_subspace = L₀)
start_sols = solutions(start)

Now, we track start_sols towards $10^7$ random Gaussian linear spaces.

k = 10^5
#track towards k random linear spaces
empirical_distribution = solve(
    start_subspace =  L₀,
    target_subspaces = [rand_subspace(N; dim = N - n, real = true) for _ in 1:k],
    transform_result = (R,p) ->(R)

We get the following estimate for the volume:

julia> mean(empirical_distribution)

Rate of convergence

Let $\sigma^2$ be the variance of $\overline{f}(A,b)$. By Chebychevs inequality we have the following rate of convergence for the $k$-th empirical mean $\mathrm{E}(f,k) = \frac{1}{k}(\overline{f}(A_1,b_1) + \cdots \overline{f}(A_k,b_k))$:

$$\mathrm{Prob}\{\vert \mathrm{E}(f,k) - \int_V f(x) \mathrm{d} x \vert \geq \varepsilon \} \leq \frac{\sigma^2}{\varepsilon^2k}.$$

The empirical variance of our sample is

julia> s² = std(empirical_distribution)

For $\varepsilon = 0.1$ the probability of $\vert \mathrm{E}(1,k) - \mathrm{vol}(V)\vert\geq \varepsilon$ is

julia> ε = 0.1
julia> s² / (ε^2 * k)

Therefore, we can safely conclude that $\mathrm{vol}( C ) \approx 11.2$.

Cite this example:
@Misc{ monte-carlo-integration2020 ,
    author =  { Paul Breiding },
    title = { Monte Carlo integration },
    howpublished = { \url{ } },
    note = { Accessed: July 13, 2020 }

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