This paper reconsiders online packet scheduling in computer networks, where the goal is to minimize weighted packet loss and where the arrival distributions of packets, or approximations thereof, are available for sampling. Earlier work proposed an expectation approach, which chooses the next packet to schedule by approximating the expected loss of each decision over a set of scenarios. The exp...

Markov Decision Processes (MDPs) provide a good, robust formal framework for modeling a large variety of stochastic planning and decision problems, but they are unsuitable to realistic problems, in particular, to solve problems in rapidly changing environments. The existing approaches use Markov decision processes to produce a policy of execution for a static set of tasks; in a changing environ...

Minimum rank problems arise frequently in machine learning applications and are notoriously difficult to solve due to the non-convex nature of the rank objective. In this paper, we present the first online learning approach for the problem of rank minimization of matrices over polyhedral sets. In particular, we present two online learning algorithms for rank minimization our first algorithm is ...

In this paper, we address the online minimization knapsack problem, i. e., the items are given one by one over time and the goal is to minimize the total cost of items that covers a knapsack. We study the removable model, where it is allowed to remove old items from the knapsack in order to accept a new item. We obtain the following results. (i) We propose an 8-competitive deterministic algorit...

We introduce a natural variant of the (metric uncapacitated) k-median problem that we call the online median problem. Whereas the k-median problem involves optimizing the simultaneous placement of k facilities, the online median problem imposes the following additional constraints: the facilities are placed one at a time; a facility cannot be moved once it is placed, and the total number of fac...

We study the online convex optimization problem, in which an online algorithm has to make repeated decisions with convex loss functions and hopes to achieve a small regret. We consider a natural restriction of this problem in which the loss functions have a small deviation, measured by the sum of the distances between every two consecutive loss functions, according to some distance metrics. We ...

What is an oline problem Unlike the offline problems that we get the whole input at the beginning, in an online problem we just have some part of the input in advance. The rest of the input are queries that arrive one by one. Upon the arrival of each request, we need to process as it is received. In analyzing online algorithms, we usually, do not really care about the ruing time. The source of ...

We study the online specialization problem, where items arrive in an online fashion for processing by one of n different methods. Each method has two costs: a processing cost (paid once for each item processed), and a set-up cost (paid only once, on the method’s first use). There are n possible types of items; an item’s type determines the set of methods available to process it. Each method has...