Results 1 
9 of
9
Optimal Parallel Algorithms for Periods, Palindromes and Squares (Extended Abstract)
, 1992
"... ) Alberto Apostolico Purdue University and Universit`a di Padova Dany Breslauer yyz Columbia University Zvi Galil z Columbia University and TelAviv University Summary of results Optimal concurrentread concurrentwrite parallel algorithms for two problems are presented: ffl Finding all the pe ..."
Abstract

Cited by 30 (14 self)
 Add to MetaCart
) Alberto Apostolico Purdue University and Universit`a di Padova Dany Breslauer yyz Columbia University Zvi Galil z Columbia University and TelAviv University Summary of results Optimal concurrentread concurrentwrite parallel algorithms for two problems are presented: ffl Finding all the periods of a string. The period of a string can be computed by previous efficient parallel algorithms only if it is shorter than half of the length of the string. Our new algorithm computes all the periods in optimal O(log log n) time, even if they are longer. The algorithm can be used to compute all initial palindromes of a string within the same bounds. ffl Testing if a string is squarefree. We present an optimal O(log log n) time algorithm for testing if a string is squarefree, improving the previous bound of O(log n) given by Apostolico [1] and Crochemore and Rytter [12]. We show matching lower bounds for the optimal parallel algorithms that solve the problems above on a general alphab...
Finding All Periods and Initial Palindromes of a String in Parallel

, 1991
"... An optimal O(log log n) time CRCWPRAM algorithm for computing all periods of a string is presented. Previous parallel algorithms compute the period only if it is shorter than half of the length of the string. This algorithm can be used to find all initial palindromes of a string in the same tim ..."
Abstract

Cited by 18 (10 self)
 Add to MetaCart
An optimal O(log log n) time CRCWPRAM algorithm for computing all periods of a string is presented. Previous parallel algorithms compute the period only if it is shorter than half of the length of the string. This algorithm can be used to find all initial palindromes of a string in the same time and processor bounds. Both algorithms are the fastest possible over a general alphabet. We derive a lower bound for finding palindromes by a modification of a previously known lower bound for finding the period of a string [3]. When p processors are available the bounds become \Theta(d n p e + log log d1+p=ne 2p).
Testing String Superprimitivity in Parallel
 Information Processing Letters
, 1992
"... A string w covers another string z if every symbol of z is within some occurrence of w in z. A string is called superprimitive if it is covered only by itself, and quasiperiodic if it is covered by some shorter string. This paper presents an O(log log n) time n log n log log n processor CRCW ..."
Abstract

Cited by 10 (1 self)
 Add to MetaCart
(Show Context)
A string w covers another string z if every symbol of z is within some occurrence of w in z. A string is called superprimitive if it is covered only by itself, and quasiperiodic if it is covered by some shorter string. This paper presents an O(log log n) time n log n log log n processor CRCWPRAM algorithm that tests if a string is superprimitive. The algorithm is the fastest possible with this number of processors over a general alphabet. 1 Introduction Quasiperiodicity, as defined by Apostolico and Ehrenfeucht [3], is an avoidable regularity of strings that is strongly related to other regularities such as periods and squares [12]. Apostolico, Farach and Iliopoulos [4] and Breslauer [7] gave lineartime sequential algorithms that tests if a string is superprimitive. Apostolico and Ehrenfeucht [3] presented an algorithm that finds all maximal quasiperiodic substrings of a string. This paper presents a parallel algorithm that tests if a string of length n is superprimitive i...
Complexity Results for Model Checking
, 1995
"... The complexity of model checking branching and linear time temporal logics over Kripke structures has been addressed in e.g. [SC85, CES86]. In terms of the size of the Kripke model and the length of the formula, they show that the model checking problem is solvable in polynomial time for CTL and ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
The complexity of model checking branching and linear time temporal logics over Kripke structures has been addressed in e.g. [SC85, CES86]. In terms of the size of the Kripke model and the length of the formula, they show that the model checking problem is solvable in polynomial time for CTL and NPcomplete for L(F ). The model checking problem can be generalised by allowing more succinct descriptions of systems than Kripke structures. We investigate the complexity of the model checking problem when the instances of the problem consist of a formula and a description of a system whose state space is at most exponentially larger than the description. Based on Turing machines, we define compact systems as a general formalisation of such system descriptions. Examples of such compact systems are Kbounded Petri nets and synchronised automata, and in these cases the wellknown algorithms presented in [SC85, CES86] would require exponential space in term of the sizes of the system descriptions and the formulas; we present polynomial space upper bounds for the model checking problem over compact systems and the logics CTL and L(X; U;S). As an example of an application of our general results we show that the model checking problems of both the branching time temporal logic CTL and the linear time temporal logics L(F ) and L(X;U;S) over Kbounded Petri nets are PSPACEcomplete.
Fast Parallel String PrefixMatching
 Theoret. Comput. Sci
, 1992
"... An O(log log m) time n log m log log m processor CRCWPRAM algorithm for the string prefixmatching problem over a general alphabet is presented. The algorithm can also be used to compute the KMP failure function in O(log log m) time on m log m log log m processors. These results improve on th ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
(Show Context)
An O(log log m) time n log m log log m processor CRCWPRAM algorithm for the string prefixmatching problem over a general alphabet is presented. The algorithm can also be used to compute the KMP failure function in O(log log m) time on m log m log log m processors. These results improve on the running time of the best previous algorithm for both problems, which was O(log m), while preserving the same number of operations. 1 Introduction String matching is the problem of finding all occurrences of a short pattern string P[1::m] in a longer text string T [1::n]. The classical sequential algorithm of Knuth, Morris and Pratt [12] solves the string matching problem in time that is linear in the length of the input strings. The KnuthMorrisPratt [12] string matching algorithm can be easily generalized to find the longest pattern prefix that starts at each text position within the same time bound. We refer to this problem as string prefixmatching. In parallel, the string matching p...
WorkTimeOptimal Parallel Algorithms for String Problems (Extended Abstract)
 In Proc. 27th ACM Symp. on the Theory of Computing
, 1995
"... ) Artur Czumaj Zvi Galil y Leszek G¸asieniec z Kunsoo Park x Wojciech Plandowski  Abstract A parallel algorithm is workoptimal if it uses the smallest possible work; a workoptimal algorithm is worktime optimal if it also uses the smallest possible time. We design worktimeoptimal al ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
(Show Context)
) Artur Czumaj Zvi Galil y Leszek G¸asieniec z Kunsoo Park x Wojciech Plandowski  Abstract A parallel algorithm is workoptimal if it uses the smallest possible work; a workoptimal algorithm is worktime optimal if it also uses the smallest possible time. We design worktimeoptimal algorithm for a number of string processing problems on the EREWPRAM and the hypercube. They include string matching and two dimensional pattern matching. No such algorithms have been known before for any of these problems. 1 Introduction We call a parallel algorithm workoptimal if it has smallest possible work. Notice that this definition is stricter than the one requiring only the same work as the best known sequential algorithm and it requires proving a lower bound. In most cases workoptimality means either linear work or O(n log n) work because no higher lower bounds are known. We call a workoptimal algo Heinz Nixdorf Institute, University of Paderborn, D33095 Paderborn, Germany....
Transforming comparison model lower bounds to the parallelrandomaccessmachine
 INFORMATION PROCESSING LETTERS
, 1997
"... We provide general transformations of lower bounds in Valiant's parallelcomparisondecisiontree model to lower bounds in the priority concurrentread concurrentwrite parallelrandomaccessmachine model. The proofs rely on standard Ramseytheoretic arguments that simplify the structure of th ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
We provide general transformations of lower bounds in Valiant's parallelcomparisondecisiontree model to lower bounds in the priority concurrentread concurrentwrite parallelrandomaccessmachine model. The proofs rely on standard Ramseytheoretic arguments that simplify the structure of the computation by restricting the input domain. The transformation of comparison model lower bounds, which are usually easier to obtain, to the parallelrandomaccessmachine, unifies some known lower bounds and gives new lower bounds for several problems.
Efficient String Matching on Coded Texts
 IN PROCEEDINGS OF COMBINATORIAL PATTERN MATCHING, 6TH ANNUAL SYMPOSIUM (CPM'95
, 1994
"... The so called "four Russians technique" is often used to speed up algorithms by encoding several data items in a single memory cell. Given a sequence of n symbols over a constant size alphabet, one can encode the sequence into O(n=) memory cells in O(log ) time using n= log processors. T ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
The so called "four Russians technique" is often used to speed up algorithms by encoding several data items in a single memory cell. Given a sequence of n symbols over a constant size alphabet, one can encode the sequence into O(n=) memory cells in O(log ) time using n= log processors. This paper presents an efficient CRCWPRAM stringmatching algorithm for coded texts that takes O(log log(m=)) time 1 making only O(n=) operations, an improvement by a factor of = O(logn) on the number of operations used in previous algorithms. Using this stringmatching algorithm one can test if a string is squarefree and find all palindromes in a string in O(log log n) time using n= log log n processors.
unknown title
"... Abstract. In this paper we study approximate seeds of strings, that is, substrings of agiven string x that cover (by concatenations or overlaps) a superstring of x, under a variety of distance rules (the Hamming distance, the edit distance, and the weighted edit distance). We solve the smallest dis ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract. In this paper we study approximate seeds of strings, that is, substrings of agiven string x that cover (by concatenations or overlaps) a superstring of x, under a variety of distance rules (the Hamming distance, the edit distance, and the weighted edit distance). We solve the smallest distance approximate seed problem and the restrictedsmallest approximate seed problem in polynomial time and we prove that the general smallest approximate seed problem is NPcomplete.