Online algorithms are a hallmark of worst case optimization under uncertainty. On the other
hand, in practice, the input is often far from worst case, and has some predictable …

We study approximation algorithms for problems of scheduling precedence constrained jobs
with the objective of minimizing total weighted completion time, in identical and related …

In many optimization problems, a feasible solution induces a multi-dimensional cost vector.
For example, in load-balancing a schedule induces a load vector across the machines. In k …

A well-known problem in scheduling and approximation algorithms is the Santa Claus
problem. Suppose that Santa Claus has a set of gifts, and he wants to distribute them among …

We study the problem of fairly allocating a set of $ m $ indivisible goods to a set of $ n $
agents. Envy-freeness up to any good (EFX) criteria--which requires that no agent prefers …

In the max-min allocation problem a set P of players are to be allocated disjoint subsets of a
set R of indivisible resources, such that the minimum utility among all players is maximized …

Recent advances in algorithmic design show how to utilize predictions obtained by machine
learning models from past and present data. These approaches have demonstrated an …

Let there be a set M of m parallel machines and a set J of n jobs, where each job j takes pi, j
time units on machine M i. In makespan minimization the goal is to schedule each job non …

We study the classical scheduling problem of assigning jobs to machines in order to
minimize the makespan. It is well-studied and admits an EPTAS on identical machines and a …

Abstract Recently, Chakrabarty and Swamy (STOC 2019) introduced the minimum-norm
load-balancing problem on unrelated machines, wherein we are given a set J of jobs that …