The Hutchson Estimator
In this note (timestamp 20220407), our main goal was to consider computing the log determinant of the Jacobian of a function, in particular of the following form
\[f(z) := z + g(z; \theta)\]where \(g:\mathbb{R}^d \rightarrow \mathbb{R}^d\), parameterized by some parameters \(\theta\), which we do not explicitly consider here. This form is quite common, especially if one is working with residual connections in neural net layers.
1. Series approximation of log determinant
The Jacobian matrix with of \(f\) with respect to input \(z\) is given by:
\[\frac{df}{dz} = I_d + \frac{dg_{\theta}}{dz}\]or in an alternative notation
\[J_f = I_d + J_g\]where we assume \(g(\cdot;\theta) = g_{\theta}(\cdot)\) is Lipschitz with constant \(<1\). Then necessarily, \(\det J_f > 0\). We can then apply the identity that \(\log \det(A) = \text{tr} \log A\) for \(A\) nonsingular. In particular:
\[\text{tr}(\log J_f) = \text{tr}(\log(I + J_g)) = \sum_{k=1}^{\infty}(1)^{k+1}\frac{\text{tr}(J_g^k)}{k}\]which converges with the Lipschitz constraint.
2. Hutchinson trace estimator
Without delving into the details, we claim that a classic trick (1990) to compute the trace of a matrix \(B\) is to approximate:
\[\text{tr}(B) = \mathbb{E}[v^TBv] \approx \frac{1}{m}\sum_{i=1}^m(v^{(i)})^T(Bv^{(i)})\]where \(v\) denotes a random vector, \(v_i\) is a realization.
\[\mathbb{E}[v] = 0, \text{Var}[v] = I\]An example of such \(v\) can be that each entry is independently drawn from a Radamacher distribution.
Accepting that the above approach works, the situation in which one wants to consider such an approximation is when \(d\) is exceptionally large, and \(B\) is dense. In this case, computing the trace of \(B^k\) is at least the cost of doing eigenvalue decomposition, which is \(O(d^3)\).
In the case of the estimator, suppose \(O(m)\) samples are sufficient for convergence, computing the trace of \(B^k\) amounts to repeated matrixvector multuplications, which is only \(O(kmd^2)\) in total, since we’d have to accumulate the multiplied random vectors from indices \(j=1, \ldots, k1, k\).
We provide an implementation below and show that it converges. In this quick note, we did not discuss the following good questions:

Convergence order of the series.

Special structures of Jacobian \(J_g\) and speedups.

Distributions of \(v\) and associated biases, variances.