In the last lecture, we saw some algorithms that, while simple and appealing, were somewhat unmotivated. We now try to derive them from general principles, and in a setting that will allow us to attack other problems in competitive analysis. $\def\K{\mathsf{K}} \def\R{\mathbb{R}} \def\seteq{\mathrel{\vcenter{:}}=} \def\cE{\mathcal{E}} \def\argmin{\mathrm{argmin}} \def\llangle{\left\langle} \def\rrangle{\right\rangle} \def\1{\mathbf{1}} \def\e{\varepsilon}$

## Gradient descent: The proximal view

Let us first recall the upper bound we derived for the regret in the last lecture:

\begin{equation}\label{eq:regret} R_T \leq \sum_{t=1}^T \left[ \|p_t-p_{t+1}\|_1 + \left\langle p_{t+1}, \ell_t - \ell_t(x_T^*) \right\rangle\right]. \end{equation}

Trying to minimize this expression leads to the question of how we should update our probability distribution $p_t \to p_{t+1}$ to simultaneously be stable (control the first term) and competitive (the second term).

A very natural algorithm in this setting is gradient descent. Indeed, suppose that $\ell : \R^n \to \R$ is differentiable, and consider the optimization

$\min \left\{ \frac12 \|x-x_0\|^2 + \eta \ell(x) : x \in \R^n \right\},$

where $\eta > 0$ is a small constant and $\|\cdot\|$ denotes the Euclidean norm. Then first-order optimality conditions dictate that the optimizer satisfies [ x^* = x_0 - \eta \nabla \ell(x_0) + O(\eta^2)\,. ]

Two questions immediately arise: Why do we use the Euclidean norm when our reference problem \eqref{eq:regret} refers to the $\ell_1$ norm, and if $x$ is meant to encode a probability distribution, how do we maintain this constraint for $x^*$?

Let’s address the feasibility problem first. Suppose $\K \subseteq \R^n$ is a closed convex set and $F : \R^n \to \R^n$ is a sufficiently smooth vector field (think of $F = \nabla \ell$). How should we move in the direction of $F$ while simultaneously remaining inside $\K$?

The unconstrained flow along $F$ can be described as a trajectory $x : [0,\infty) \to \R^n$ given by [ x’(t) = F(x(t))\,. ] The most natural way to keep this flow inside $\K$ is to project back into the body whenever we leave. Define the Euclidean projection

$P_{\K}(y) \seteq \argmin \left\{ \|y-z\|^2 : z \in \K \right\},$

and the result of taking an infinitesimal step in direction $v$ and and then projecting: [ \Pi_{\K}(x,v) \seteq \lim_{\e \to 0} \frac{P_{\K}(x+\e v) -x}{\e}\,. ] Then the projected dynamics looks like [ x’(t) = \Pi_{\K} \left(x(t), F(x(t))\right)\,. ] This is an example of a projected dynamical system. Having now addressed feasibility, we are left to consider the role of the Euclidean norm.

## A Riemannian version

One can view $\Pi_{\K}(x, \cdot)$ as a function on the tangent space at $x$. To specify such a projection, we only need a local Euclidean structure. An inner product $\langle \cdot,\cdot\rangle_x$ that varies smoothly over $x \in \K$ is precisely a Riemannian metric.

Equivalently, we specify at every point $x \in \K$, a smoothly varying positive-definite matrix $M(x)$ so that

\begin{align*} \langle u,v\rangle_{M,x} &= \langle u, M(x) v\rangle \\ \|u\|^2_{M,x} &= \langle u,u\rangle_{M,x}. \end{align*}

The associated projection operator is then given by

\begin{align*} P_{\K}^M(y; x) &\seteq \argmin \left\{ \left\|y-z\right\|_{M,x}^2 : z \in \K \right\} \\ \Pi_{\K}^M(x,v) &\seteq \lim_{\e \to 0} \frac{P_{\K}^M(x+\e v,x)-x}{\e}\,. \end{align*}

This leads to the dynamical system:

\begin{align*} x'(t) &= \Pi^M_{\K}\left(x(t),F(x(t))\right) \\ x(0) &= x_0 \in \K\,. \end{align*}

## Lyapunov functions

The problem with stating things at this level of generality is that even when $F = \nabla \ell$ is the gradient of a convex function $\ell : \R^n \to \R$, we don’t have a global way of controlling convergence of $F(x(t))$ to $\min \{ F(x) : x \in \K \}$. In the Euclidean setting ($M(x) \equiv \mathrm{Id}$), there is a natural Lyapunov function: If $\ell$ is convex and $\ell(x^*) = \min \{ \ell(x) : x \in \K \}$, then for every $x \in \K$:

$\langle - \nabla \ell(x), x^* - x\rangle \geq 0\,.$

In other words, gradient descent always makes progress toward $x^*$.

If $x’(t) = \Pi_{\K}\left(x(t), \nabla \ell(x(t))\right)$, then in the language of competitive analysis, the quantity $\frac12 \|x(t)-x^*\|^2$ acts a potential function (a global measure of progress).

We will consider geometries that come equipped with such a Lyapunov function. In a sense that can be formalized in various ways, these are the Hessian structures on $\R^n$, i.e., those arising when $M(x) = \nabla^2 \Phi(x)$ for some strictly convex function $\Phi : \K \to \R$.

# Mirror descent dynamics

Consider now a compact, convex set $\K \subseteq \R^n$, a strictly convex function $\Phi : \K \to \R$, and a continuous time-varying vector field $F : [0,\infty) \times \K \to \R^n$. We will refer to continuous-time mirror descent as the dynamics specified by

\begin{align*} x'(t) &= \Pi_{\K}^{\nabla^2 \Phi}\left(\vphantom{\bigoplus} x(t), F(t, x(t))\right) \\ x(0) &= x_0 \in \K. \end{align*}

We will sometimes refer to $\Phi$ as the mirror map.

As one might expect, we can decompose $x’(t)$ into two components: One flowing in the direction $F(t,x(t))$, and the other component arising from the normal forces that are keeping $x(t)$ inside $\K$. We recall the normal cone to $\K$ at $x$ is given by

$N_{\K}(x) = \left\{ p \in \R^n : \langle p,y -x \rangle \leq 0 \textrm{ for all } y \in \K \right\}.$

This is the set of directions that point out of the body $\K$. The next theorem is proved in the paper k-server via multiscale entropic regularization.

If $\nabla^2 \Phi(x)^{-1}$ is continuous on $\K$, then for any $x_0 \in \K$, there is an absolutely continuous trajectory $x : [0,\infty) \to \K$ satisfying \begin{align} \nabla^2 \Phi(x(t)) x'(t) &\in F(t,x(t)) - N_{\K}(x(t)), \label{eq:inclusion}\\ x(0) &= x_0.\nonumber \end{align} Moreover, if $\nabla^2 \Phi(x)$ is Lipschitz on $\K$ and $F$ is locally Lipschitz, then the solution is unique.

Note that \eqref{eq:inclusion} is a differential inclusion: We only require that the derivative lies in the specified set.

## Lagrangian multipliers

If $\K$ is a polyhedron, the one can write

\begin{equation}\label{eq:polyhedron} \K = \{ x \in \R^n : Ax \leq b \}, \qquad A \in \R^{m \times n}, b \in \R^m\,. \end{equation}

In this case, the normal cone at $x$ is the cone spanned by the normals of the tight constraints at $x$:

\begin{equation}\label{eq:cone-poly} N_{\K}(x) = \left\{ A^T y : y \geq 0 \textrm{ and } y^T(b-Ax)=0 \right\}. \end{equation}

Consider now the application of Theorem MD to a polyhedron and a solution $x : [0,\infty) \to \K$, $\lambda : [0,\infty) \to \R^n$ such that

\begin{equation}\label{eq:traj} \nabla^2 \Phi(x(t)) x'(t) = F(t,x(t)) - \lambda(t), \end{equation}

and $\lambda(t) \in N_{\K}(x(t))$ for $t \geq 0$.

Let us consider the dual variables to the constraints \eqref{eq:polyhedron}: We can fix a measurable $\hat{\lambda} : [0,\infty) \to \R^m_+$ such that [ A^T \hat{\lambda}(t) = \lambda(t), \quad t \geq 0. ] Now \eqref{eq:cone-poly} and $\lambda(t) \in N_{\K}(x(t))$ yield the complementary-slackness conditions: For all $i=1,2,\ldots,m$ and $t \geq 0$: [ \hat{\lambda}_i(t) > 0 \implies \langle A_i,x(t)\rangle = b_i, ] where $A_i$ is the $i$th row of $A$.

## The Bregman divergence as a Lyapunov function

We promised earlier the existence of a functional to control the dynamics, and this is provided by the Bregman divergence associated to $\Phi$:

$D_{\Phi}(y; x) \seteq \Phi(y) - \Phi(x) - \langle \nabla \Phi(x), y-x\rangle\,.$

Let $x(t)$ be a trajectory satisfying \eqref{eq:traj}. Then for any $y \in \K$:

\begin{align} \partial_t D_{\Phi}(y; x(t)) &= - \langle \nabla \Phi(x(t)), x'(t)\rangle + \langle \nabla \Phi(x(t)), x'(t) \rangle -\langle \partial_t \Phi(x(t)), y-x(t)\rangle \nonumber \\ &= - \langle \nabla^2 \Phi(x(t)) x'(t), y - x(t) \rangle \nonumber \\ &= - \langle F(t,x(t)) - \lambda(t), y-x(t)\rangle \nonumber \\ &\leq - \langle F(t,x(t)), y-x(t)\rangle \label{eq:div}\,, \end{align}

where the last inequality used that $y \in \K$ and $\lambda(t) \in N_{\K}(x(t))$.

If $F(t,x(t)) = - c(t)$ is a cost function, say, then this inequality aligns with a goal stated at the beginning of the first lecture: As long as the algorithm $x(t)$ is suffering more cost than some feasible point $y \in \K$, we would like to be “learning” about $y$.

## The algorithm from last time

In the next lecture, we will use this framework to derive and analyze algorithms for metrical task systems (MTS) and the $k$-server problem. For now, let us show that the algorithm and analysis from last time (for MTS on uniform metrics) fit precisely into our framework.

Suppose that $\K = \{ x \in \R_+^n : \sum_{i=1}^n x_i = 1 \}$ is the probability simplex and

$\Phi(x) = \sum_{i=1}^n (x_i+\delta) \log (x_i+\delta)$

is the (negative) entropy with some shift by $\delta > 0$. In the next lecture, we will see why the negative entropy arises naturally as a mirror map.

Then $\nabla^2 \Phi(x)$ is a diagonal matrix with $\left(\nabla^2 \Phi(x)\right)_{ii} = \frac{1}{x_i+\delta}$. Let $F(t,\cdot) = -c(t)$ be a time-varying cost vector with $c(t) \geq 0$.

Therefore \eqref{eq:traj} gives

\begin{equation}\label{eq:shifted} x_i'(t) = (x_i(t)+\delta) \left(-c_i(t) + \hat{\mu}(t) - \hat{\lambda}_i(t)\right). \end{equation} Here, $\hat{\lambda}_i(t)$ is the Lagrangian multiplier corresponding to the constraint $x_i \geq 0$, and $\hat{\mu}(t)$ is the multiplier corresponding to $\sum_{i=1}^n x_i = 1$.

This is precisley the algorithm described before (as an exercise, one might try rewriting it to match exactly), and \eqref{eq:div} constitutes half of the analysis. In the next lecture, we will discuss some general methods for the other half: Tracking the movement cost.