Natural gradient descent and mirror descent

In this post, we discuss the natural gradient, which is the direction of steepest descent in a Riemannian manifold [1], and present the main result of Raskutti and Mukherjee (2014) [2], which shows that the mirror descent algorithm is equivalent to natural gradient descent in the dual Riemannian manifold.


Suppose we want to minimize some convex differentiable function \(f: \Theta \rightarrow {\mathbb{R}}\) . A common approach is to do a line search using the steepest descent algorithm where \(\alpha_t\) is the step-size and the direction we step in is the gradient of the function.

However, in applications where the parameters lie on a non-Euclidean Riemannian manifold1 with metric tensor \(H = (h_{ij})\) (an inner product on the tangent space of the manifold at a point), the direction of steepest descent is actually the natural gradient:

where \(H\) is the Fisher information matrix for a statistical manifold. This update equation is called natural gradient descent.

Natural Gradients

Recall that in an Euclidean space (with a orthonormal coordinate system) \(\Theta={\mathbb{R}}^n\) , the squared length of a small incremental vector \(d\theta\) that connects \(\theta\) and \(\theta + d\theta\) is given by

But for a Riemannian manifold, the squared length is given by the quadratic form

where \(H = (h_{ij})\) is an \(n \times n\) matrix called the Riemannian metric tensor (which depends on \(\theta\). In the Euclidean case, we just get that \(H = I_n\).

In this Riemannian manifold, the direction of steepest descent of a function \(f(\theta)\) at \(\theta \in \Theta\) is defined by the vector \(d\theta\) that minimizes \(f(\theta + d\theta)\), where \(||d\theta||\) has a fixed length, i.e., under the constraint

Theorem (Amari, 1998) [1]:

The steepest descent direction of \(f(\theta)\) in a Riemannian space is given by

where \(H^{-1} = (h^{ij})\) is the inverse of the metric \(H = (h_{ij})\), and \(\nabla f\) is the conventional gradient:

Proof: Write \(d\theta = \epsilon v\), i.e., we decompose the vector into its magnitude \(\epsilon\) and direction \(v\), and search for the vector \(v\) that decreases the value of the function the most.

Writing the linearized function, we have

subject to the constraint that \(||v||^2 = v^\top H v =1\), i.e., \(v\) is a unit vector representating just the direction.

Setting the derivative with respect to \(v\) of the Lagrangian to 0, i.e.,

so we have that

which is the direction that causes the function’s value to decrease the most. Lastly, plugging \(v\) above into the constraint \(v^\top H v = 1\), we can solve for \(\lambda\).

Thus, the direction of steepest descent is \(H^{-1} \nabla f(\theta)\), i.e., the natural gradient.

Lastly, note that in a Euclidean space, the metric tensor is just the identity matrix, so we get back standard gradient descent.

Mirror descent and Bregman divergence

Mirror descent is a generalization of gradient descent that takes into account non-Euclidean geometry. The mirror descent update equation is

Here \(\Psi: {\mathbb{R}}^p \times {\mathbb{R}}^p \rightarrow {\mathbb{R}}_+\), and when

we get back the standard gradient descent update2.

The proximity function \(\Psi\) is commonly chosen to be the Bregman divergence. Let \(G: \Theta \rightarrow {\mathbb{R}}\) be a strictly convex, twice-differentiable function, then the Bregman divergence \(B_G: \Theta \times \Theta \rightarrow {\mathbb{R}}^+\) is:

Equivalence of natural gradient and mirror descent

Bregman divergences and convex duality

The convex conjugate3 of a function \(G\) is defined to be

Now, if \(G\) is a lower semi-continuous function, then we have that \(G\) is the convex conjugate of \(H\). This implies that \(G\) and \(H\) have a dual relationship. If \(G\) is strictly convex and twice differentiable, so is \(H\). Let \(g = \nabla G\) and \(h = \nabla H\). Then \(g = h^{-1}\).

Now let \(\mu = g(\theta) \in \Phi\) be the point at which the supremum for the dual function is attained be the dual coordinate system to \(\theta\). The dual Bregman divergence \(B_H: \Phi \times \Phi \rightarrow {\mathbb{R}}^+\) induced by the strictly convex differentiable function \(H\)

The dual coordinate relationship gives us

For exponential families, the function \(G\) is the log partition function4. We’ll now work through some examples.

Example: [Normal]

Consider the family \(N(\theta, I_p)\). The log partition function is

and the conjugate function of \(G\) is

since the expression in the supremum is maximized when \(\theta = \mu\) (so we plug this in for \(\theta\) to get the second line). Then we can easily write the Bregman divergence induced by \(G,H\) as

Example: [Poisson]

Now suppose we have the family \(\text{Poisson}(\exp(\theta))\). We have

where we plugged in \(\theta = \log \mu\) above. So the Bregman divergence induced by \(G,H\) is

Bregman divergences and Riemannian manifolds

Now that we have introduced what the Bregman divergence of an exponential family looks like (with respect to the log partition function \(G\) and its dual Bregman divergence with respect to \(H\), where \(H\) is the conjugate of \(G\), we are ready to discuss the respective primal and dual Riemannian manifolds induced by these divergences.

Theorem (Raskutti and Mukherjee, 2014) [2]:

The mirror descent step with Bregman divergence defined by \(G\) applied to the function \(f\) in the space \(\Theta\) is equivalent to the natural gradient step along the dual Riemannian manifold \((\Phi, \nabla^2 H)\).

(Proof Sketch):

Step 1: Rewrite the mirror descent update in terms of the dual Riemannian manifold coordinates \(\mu \in \Phi\):

since minimizing by differentiation and the dual coordinates relationship gives us

Step 2: Apply the chain rule:

and plugging this in to the update above, we get

which corresponds to the natural gradient step.

High-level summary:

The main result of [2] is that the mirror descent update with Bregman divergence is equivalent to the natural gradient step along the dual Riemannian manifold.

What does this mean? For natural gradient descent we know that

but neither of these things are known for mirror descent.

The paper also outlines potential algorithmic benefits: natural gradient descent is a second-order method, since it requires the computation of an inverse Hessian, while the mirror descent algorithm is a first-order method and only involves gradients.


  1. Amari. Natural gradient works efficiently in learning. 1998

  2. Raskutti and Mukherjee. The information geometry of mirror descent. 2014.


  1. a smooth manifold equipped with an inner product on the tangent space \(T_p \Theta\) 

  2. this can be seen by writing down the projected gradient descent update and rearranging terms 

  3. aka Fenchel dual, Legendre transform. Also e.g. for a convex optimization function, we can essentially write the dual problem (i.e., the inf of the Lagrangian), as the negative conjugate function plus linear functions of the Langrange multipliers. 

  4. see the table listing the log partition function for exponential family distributions