Online Convex Optimization for Caching Networks

Online Caching with Convex Costs

Modern software applications and services operate nowadays on top of large clusters and datacenters. To reduce the underlying infrastructure cost and increase utilization, different services share the same physical resources (e.g., CPU, bandwidth, I/O, memory). Consequently, the cluster provider often has to decide in real-time how to allocate resources in overbooked systems, taking into accoun...

Adaptive Bound Optimization for Online Convex Optimization

We introduce a new online convex optimization algorithm that adaptively chooses its regularization function based on the loss functions observed so far. This is in contrast to previous algorithms that use a fixed regularization function such as L2-squared, and modify it only via a single time-dependent parameter. Our algorithm’s regret bounds are worst-case optimal, and for certain realistic cl...

Online Convex Optimization

A convex repeated game is a two players game that is performed in a sequence of consecutive rounds. On round t of the repeated game, the first player chooses a vector wt from a convex set A. Next, the second player responds with a convex function gt : A → R. Finally, the first player suffers an instantaneous loss gt(wt). We study the game from the viewpoint of the first player. In offline conve...

Online convex optimization

1.1 Definitions We say a set S ⊆ Rd is convex if for any two points x,x′ ∈ S, the line segment conv{x,x′} := {(1−α)x+αx′ : α ∈ [0, 1]} between x and x′ (also called the convex hull of {x,x′}) is contained in S. Overloading terms, we say a function f : S → R is convex if its epigraph epi(f) := {(x, t) ∈ S × R : f(x) ≤ t} is a convex set (in Rd × R). Proposition 1. A function f : S → R is convex ...

Online Convex Optimization