Analysis of Randomized Selection Algorithm Motivated by the LZ'77 Scheme
نویسندگان
چکیده
We consider a randomized selection algorithm that has n initial participants and a moderator. In each round of the process, each participant and the moderator throw a biased coin. Only the participants who throw the same result as the moderator stay in the game for subsequent rounds. With probability 1, all participants are eliminated in finitely many rounds. We let Mn denote the number of participants remaining in the game in the last nontrivial round. This simple algorithm has surprisingly many interesting applications. In particular, it models (asymptotically) the number of longest prefixes in the Lempel–Ziv ’77 data compression scheme. Such multiplicity was used recently in [13] to design an error-resilient LZ’77 scheme. We give precise asymptotic characteristics of the jth factorial moment of Mn for all j ∈ N. Also, we present a detailed asymptotic description of the exponential generating function for Mn. In particular, we exhibit periodic fluctuation in the distribution of Mn, and we prove that no limiting distribution exists (however, we observe that the asymptotic distribution follows the logarithmic series distribution plus some fluctuations). The results we develop are proved by probabilistic and analytical techniques of the analysis of algorithms. In particular, we utilize recurrence relations, analytical poissonization and depoissonization, the Mellin transform, and complex analysis.
منابع مشابه
Analysis of a Randomized Selection Algorithm Motivated by the LZ’77 Scheme
We consider a randomized selection algorithm that has n initial participants and a moderator. In each round of the process, each participant and the moderator throw a biased coin. Only the participants who throw the same result as the moderator stay in the game for subsequent rounds. With probability 1, all participants are eliminated in finitely many rounds. We let Mn denote the number of part...
متن کاملA Novel Scheme for Improving Accuracy of KNN Classification Algorithm Based on the New Weighting Technique and Stepwise Feature Selection
K nearest neighbor algorithm is one of the most frequently used techniques in data mining for its integrity and performance. Though the KNN algorithm is highly effective in many cases, it has some essential deficiencies, which affects the classification accuracy of the algorithm. First, the effectiveness of the algorithm is affected by redundant and irrelevant features. Furthermore, this algori...
متن کاملAn Assessment Scheme for ELT Performance: An Iranian Case of Farhangian University
Accountability concerns in language education call for the development of more valid and authentic measures of assessment. In light of these concerns, performance assessment has received increasing interest in the context of teacher education programs and teacher licensing over the last decade. In Iran, a recent policy adopted by Farhangian University aims at assessing the professional competen...
متن کاملPerformance Analysis of Wireless Cooperative Networks with Iterative Incremental Relay Selection
In this paper, an iterative incremental relay selection (IIRS) scheme is considered for wireless cooperative networks in order to increase the reliability of transmission. Different from the conventional incremental relay selection which incrementally selects a best relay for only one iteration; the IIRS scheme iteratively applies the incremental relaying and relay selection processes. To evalu...
متن کاملTutorial on Lempel-Ziv Data Compression Algorithm
In many scenario of digital communication and data processing, we may deal with strings of data which have certain structural regularities, making it possible for time-saving techniques of data compression. Given a discrete data source, the data compression problem is first to identify the limitations of the source, and second to devise a coding scheme which will best compress it subject to cer...
متن کامل