The advice model of online computation captures the setting in which the online algorithm is
given some information concerning the request sequence. This paradigm allows to establish …

Bin packing is a classic optimization problem with a wide range of applications, from load
balancing to supply chain management. In this work, we study the online variant of the …

The online bin packing problem is a well-known optimization challenge that finds application
in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called …

In the online (time-series) search problem, a player is presented with a sequence of prices
which are revealed in an online manner. In the standard definition of the problem, for each …

In online interval scheduling, the input is an online sequence of intervals, and the goal is to
accept a maximum number of non-overlapping intervals. In the more general disjoint path …

A variant of the online knapsack problem is considered in the settings of trusted and
untrusted predictions. In Unit Profit Knapsack, the items have unit profit, and it is easy to find …

We study the classic problem of searching for a hidden target in the line and the $ m $-ray
star, in a setting in which the searcher has some prediction on the hider's position. We first …

This paper leverages machine learned predictions to design online algorithms for the k-max
and k-min search problems. Our algorithms can achieve performances competitive with the …

We study the discrete bin covering problem where a multiset of items from a fixed set $
S\subseteq (0, 1] $ must be split into disjoint subsets while maximizing the number of …

The online linear optimization paradigm is important to many real-world network
applications as well as theoretical algorithmic studies. Recent studies have made attempts …