SearcharxivSearch

arXiv · 1109.2231

Characterization of Request Sequences for List Accessing Problem and New Theoretical Results for MTF Algorithm

Abstract

List Accessing Problem is a well studied research problem in the context of linear search. Input to the list accessing problem is an unsorted linear list of distinct elements along with a sequence of requests, where each request is an access operation on an element of the list. A list accessing algorithm reorganizes the list while processing a request sequence on the list in order to minimize the access cost. Move-To-Front algorithm has been proved to be the best performing list accessing online algorithm till date in the literature. Characterization of the input request sequences corresponding to practical real life situations is a big challenge for the list accessing problem. As far as our knowledge is concerned, no characterization for the request sequences has been done in the literature till date for the list accessing problem. In this paper, we have characterized the request sequences for the list accessing problem based on several factors such as size of the list, size of the request sequence, ordering of elements and frequency of occurrence of elements in the request sequence. We have made a comprehensive study of MTF list accessing algorithm and obtained new theoretical results for our characterized special class of request sequences. Our characterization will open up a new direction of research for empirical analysis of list accessing algorithms for real life inputs.

Explore related subjects

Keep this discovery

BibTeXRIS

Rakesh Mohanty, Burle Sharma, Sasmita Tripathy. 2011-09-10. Characterization of Request Sequences for List Accessing Problem and New Theoretical Results for MTF Algorithm. https://doi.org/10.5120/2601-3627

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS