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Author Manber, Udi ♦ Tompa, Martin
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©1985
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract This work generalizes decision trees in order to study lower bounds on the running times of algorithms that allow probabilistic, nondeterministic, or alternating control. It is shown that decision trees that are allowed internal randomization (at the expense of introducing a small probability of error) run no faster asymptotically than ordinary decision trees for a collection of natural problems. Two geometric techniques from the literature for proving lower bounds on the time required by ordinary decision trees are shown to be special cases of one unified technique that, in fact, applies to nondeterministic decision trees as well. Finally, it is shown that any lower bound on alternating decision tree time also applies to alternating Turing machine time.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1985-07-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 32
Issue Number 3
Page Count 13
Starting Page 720
Ending Page 732


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Source: ACM Digital Library