Showing papers by "Ran El-Yaniv published in 1997"

Online list accessing algorithms and their applications: recent empirical evidence

Abstract: This paper reports the results of an empirical test of the performances of a large set of online list accessing algorithms. The dgorithms’ access cost performances were tested with respect to request sequences generated from the Calgary Corpus. In addition to testing access costs within the traditional dynamic list accessing model we tested all algorithms’ relative performances as data compressors via the compression scheme of Bentley et al. Some of the results are quite surprising and stand in contrast to someco mpetitive analysis theoretical results. For example, the randomized algorithms that were tested, all attaining competitive ratio less than 2, performed consistently inferior to quite a few deterministic algorithms that obtained the best performance. In many instances the best performance was obtained by deterministic algorithms that are either not competitive or not optimal.

Agnostic Classification of Markovian Sequences

Abstract: Classification of finite sequences without explicit knowledge of their statistical nature is a fundamental problem with many important applications. We propose a new information theoretic approach to this problem which is based on the following ingredients: (i) sequences are similar when they are likely to be generated by the same source; (ii) cross entropies can be estimated via "universal compression"; (iii) Markovian sequences can be asymptotically-optimally merged. With these ingredients we design a method for the classification of discrete sequences whenever they can be compressed. We introduce the method and illustrate its application for hierarchical clustering of languages and for estimating similarities of protein sequences.

Nearly Optimal Competitive Online Replacement Policies

Abstract: This paper studies the online replacement problem. In this problem an online player is engaged at each time in one activity. Associated with each activity are a changeover cost and flow rate. While involved in an activity the player's budget is depleted at the activity's flow rate. The player can switch to a new activity whenever it is offered but he pays a changeover cost. The player's goal is to decide when to switch activities so that his total cost is minimized. Typical applications are: equipment, jobs and supplier replacement, mortgage refinancing, etc. With respect to the competitive ratio performance measure, this paper seeks to determine the best possible competitive ratio achievable by an online replacement policy. Our results include the following: a general lower bound on the performance of any deterministic policy, a policy that is optimal in several special cases and a simple policy that is approximately optimal.

On randomization in online computation

Abstract: This paper concerns two fundamental but somewhat neglected issues both related to the design and analysis of randomized online algorithms. Motivated by early results in game theory we define several types of randomized online algorithms discuss known conditions for their equivalence and give a natural example distinguishing between two kinds of randomizations. In particular we show that mixed randomized memoryless paging algorithms can achieve strictly better competitive performance than behavioral randomized algorithms. Next we summarize known-and derive new-"Yao Principle" theorems for lower bounding competitive ratios of randomized online algorithms. This leads to six different theorems for bounded/unbounded and minimization/maximization problems.