Analyzing self-adjusting linear list algorithms with deletions and unsuccessful searches

In (Hui and Martel, 1993), we designed and analyzed efficient self-adjusting linear list algorithms. Our analysis proves that a self-adjusting linear list algorithm, MP, is competitive to a large class of offline adversaries, where the operations are successful searches, unsuccessful searches, and i...

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Bibliographic Details
Published inInformation processing letters Vol. 58; no. 5; pp. 231 - 236
Main Authors Lucas Chi Kwong Hui, Martel, Charles U.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 10.06.1996
Elsevier Science
Elsevier Sequoia S.A
Subjects
Algorithmics. Computability. Computer arithmetics
Algorithms
Analysis of algorithm
Applied sciences
Competitive analysis
Computer science; control theory; systems
Data processing. List processing. Character string processing
Data structures
Exact sciences and technology
Information processing
Memory organisation. Data processing
Searches
Self-adjusting lists
Software
Studies
Theoretical computing
Data structures
Analysis of algorithm
Self-adjusting lists
Competitive analysis
Algorithm analysis
Algorithms
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Summary:In (Hui and Martel, 1993), we designed and analyzed efficient self-adjusting linear list algorithms. Our analysis proves that a self-adjusting linear list algorithm, MP, is competitive to a large class of offline adversaries, where the operations are successful searches, unsuccessful searches, and insertions. Analysis of deletions is listed as an open question. This paper presents an improved version of MP which is also able to handle deletions efficiently, and proves that the new MP algorithm is 6-competitive to offline adversaries when considering successful searches, unsuccessful searches, insertions, and deletions.
ISSN:0020-0190
1872-6119
DOI:10.1016/0020-0190(96)00064-6