We evaluate the average-case performance of three approximation algorithms for online non-clairvoyant scheduling of parallel tasks with precedence constraints. We show that for a ...
We consider the problem of scheduling a maximum profit selection of equal length jobs on m identical machines. Jobs arrive online over time and the goal is to determine a non-pre...
Sven Oliver Krumke, Alfred Taudes, Stephan Westpha...
In this work, we extend the ellipsoid method, which was originally designed for convex optimization, for online learning. The key idea is to approximate by an ellipsoid the classi...
We describe a novel framework for the design and analysis of online learning algorithms based on the notion of duality in constrained optimization. We cast a sub-family of universa...