Results 1 
9 of
9
SelfOrganizing Linear Search
 ACM Computing Surveys
, 1985
"... this article. Two examples of simple permutation algorithms are movetofront, which moves the accessed record to the front of the list, shifting all records previously ahead of it back one position; and transpose, which merely exchanges the accessed record with the one immediately ahead of it in th ..."
Abstract

Cited by 34 (6 self)
 Add to MetaCart
this article. Two examples of simple permutation algorithms are movetofront, which moves the accessed record to the front of the list, shifting all records previously ahead of it back one position; and transpose, which merely exchanges the accessed record with the one immediately ahead of it in the list. These will be described in more detail later. Knuth [1973] describes several search methods that are usually more efficient than linear search. Bentley and McGeoch [1985] justify the use of selforganizing linear search in the following three contexts:
SelfOrganizing Data Structures
 In
, 1998
"... . We survey results on selforganizing data structures for the search problem and concentrate on two very popular structures: the unsorted linear list, and the binary search tree. For the problem of maintaining unsorted lists, also known as the list update problem, we present results on the competit ..."
Abstract

Cited by 22 (0 self)
 Add to MetaCart
(Show Context)
. We survey results on selforganizing data structures for the search problem and concentrate on two very popular structures: the unsorted linear list, and the binary search tree. For the problem of maintaining unsorted lists, also known as the list update problem, we present results on the competitiveness achieved by deterministic and randomized online algorithms. For binary search trees, we present results for both online and offline algorithms. Selforganizing data structures can be used to build very effective data compression schemes. We summarize theoretical and experimental results. 1 Introduction This paper surveys results in the design and analysis of selforganizing data structures for the search problem. The general search problem in pointer data structures can be phrased as follows. The elements of a set are stored in a collection of nodes. Each node also contains O(1) pointers to other nodes and additional state data which can be used for navigation and selforganizati...
Amortized Efficiency Of List Update . . .
, 1985
"... In this article we study the amortized efficiency of the “movetofront” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes e(i) time, we show that movetofront is within a constant factor of optimum amon ..."
Abstract
 Add to MetaCart
In this article we study the amortized efficiency of the “movetofront” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes e(i) time, we show that movetofront is within a constant factor of optimum among a wide class of list maintenance rules. Other natural heuristics, such as the transpose and frequency count rules, do not share this property. We generalize our results to show that movetofront is within a constant factor of optimum as long as the access cost is a convex function. We also study paging, a setting in which the access cost is not convex. The paging rule corresponding to movetofront is the “least recently used“ replacement rule. We analyze the amortized complexity of LRU, showing that its efficiency differs from that of the offline paging rule (Belady’s MlN algorithm) by a factor that depends on the size of fast memory. No online paging algorithm has better amortized performance.
Amortized Efficiency of List Update and Paging Rules
"... ABSTRACT: In this article we study the amortized efficiency of the “movetofront ” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes 0(i) time, we show that movetofront is within a consfant factor of o ..."
Abstract
 Add to MetaCart
(Show Context)
ABSTRACT: In this article we study the amortized efficiency of the “movetofront ” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes 0(i) time, we show that movetofront is within a consfant factor of optimum among a wide class of list maintenance rules. Other natural heuristics, such as the transpose and frequency count rules, da not share this property. We generalize our results to show that movetofront is within a constant factor of optimum as long as the access cost is a convex function. We also study paging, a setting in which the access cost is not convex. The paging rule corresponding to movetofront is the “least recently used ” (LRU) replacement rule. We analyze the amortized complexity of LRU, showing that its efficiency differs from that of the offline paging rule (Belady’s MIN algorithm) by a factor that depends on the size of fast memory. No online paging algorithm has better amortized performance. 1.
An Asymptotic Optimality of the Transposition Rule for Linear Lists
, 2008
"... The transposition rule is an algorithm for selforganizing linear lists. Upon a request for a given item, the item is transposed with the preceding one. The cost of a request is the distance of the requested item from the beginning of the list. An asymptotic optimality of the rule with the respect t ..."
Abstract
 Add to MetaCart
(Show Context)
The transposition rule is an algorithm for selforganizing linear lists. Upon a request for a given item, the item is transposed with the preceding one. The cost of a request is the distance of the requested item from the beginning of the list. An asymptotic optimality of the rule with the respect to the optimal static arrangement is demonstrated for two families of request distributions. The result is established by considering an associated constrained asymmetric exclusion process.
(D WorstCase Analyses of SelfOrganizing Sequential Search Ieuristics 1
, 1983
"... Thio&_ument h.9 C)iut a oCds t ..."