4 edition of **Input sensitive, optimal parallel randomized algorithms for addition and identification.** found in the catalog.

- 264 Want to read
- 34 Currently reading

Published
**1985**
by Courant Institute of Mathematical Sciences, New York University in New York
.

Written in English

The Physical Object | |
---|---|

Pagination | 15 p. |

Number of Pages | 15 |

ID Numbers | |

Open Library | OL17979362M |

Equivalently, A0 is also optimal. An algorithm is strongly optimal if it is optimal, and its time T(n) is minimum for all parallel algorithms solving the same problem. For example, assume we have a problem that needs Workseq(n) = O(n) for an optimal single processor algorithm. If X and Y are two parallel algorithms for this problem and X runs in. A parallel algorithm that is efficient (or optimal) when run using a certain number of processors win remain an efficient (or optimal) parallel algorithm when implemented on a smaller number of processors while running proportionately slower.

A randomized algorithm Ais an algorithm that at each new run receives, in addition to its input i, a new stream/string r of random bits which are then used to specify outcomes of the subsequent random choices (or coin tossing) during the execution of the algorithm. Streams r of random bits are assumed to be independent of the input i for the. Efficient randomized pattern-matching algorithms by Richard M. Karp Michael 0. Rabin We present randomized algorithms to solve the following string-matching problem and some of its generalizations: Given a string X of length n (the pattern) and a string Y (the text), find theFile Size: 1MB.

Randomized Algorithms A randomized algorithm is an algorithm that incorporates randomness as part of its operation. Often aim for properties like Good average-case behavior. Getting exact answers with high probability. Getting answers that are close to the right answer. Often find very simple algorithms with dense but clean Size: KB. R.M. Karp 2. Introduction A randomized algorithm is one that receives, in addition to its input data, a stream of random bits that it can use for the purpose of making random choices. Even for a fixed input, different runs of a randomized algorithm may give different.

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Although many sophisticated parallel algorithms now exist, it is not at all clear if any of them is sensitive to properties of the input which can be determined only at run-time. For example, in the case of parallel addition in shared memory models, we intuitively understand that we should not add those inputs whose value is : P G Spirakis.

OPTIMAL PARALLEL RANDOMIZED ALGORITHMS 2. THE CASE OF PARALLEL ADDITION The Algorithm Let the array M represent the shared memory, Let a > 4 be a positive integer constant. Let each processor P; be equipped with a local variable, TIME;, intended to keep the current parallel step.

Initially, each processor P; (1 Cited by: 6. OPTIMAL PARALLEL RANDOMIZED ALGORITHMS 2. THE CASE OF PARALLEL ADDITION The Algorithm Let the array M represent the shared memory.

Let a 2 4 be a positive integer constant. Let each processor Pi be equipped with a local variable, TIME, intended to keep the current parallel step. Initially, each processor. Optimal parallel randomized algorithms for sparse addition and identification☆. Author links open overlay panel Paul Input sensitive.

Spirakis a b. Show moreCited by: 6. AbstractAlthough many sophisticated parallel algorithms now exist, most of them are not sensitive to properties of the input which can be determined only at run-time. For example, in the case of parallel addition in shared memory models, we intuitively understand that we should not add those inputs whose value is by: 6.

An Optimal Randomized Parallel Algorithm for Finding Connected Components in a Graph. Related Databases. Web of Science You must be logged in with an active subscription to view this. () Optimal Randomized EREW PRAM Algorithms for Finding Spanning Forests.

Journal of Algorithms Cited by: The previously known output-sensitive work-optimal algorithms for convex hulls have running times Ω(log n) (expected) and Ω(log3 n) in two and three dimensions respectively. • Combining branch and bound algorithms with our scheme to obtain input sensitive, fast in practice, algorithms for any trans-formation, and for point sets in any dimension.

• Showing empirically that there is an optimal size of the base that gives the best runtime, depending on the configuration of the sets.

by: arithmetic addition on big-integer numbers are presented. The first algorithm is sequential while the second is parallel. Both algorithms, unlike existing ones, perform addition on blocks or tokens of 60 bits (18 digits), and thus boosting the execution time by a factor of significance.

A fast and efficient parallel algorithm for this problem remains a major goal in the design of parallel graph algorithms. In this paper, we describe a parallel randomized algorithm for comput-ing single-source shortest paths.

Our algorithm achieves a significant speed-up even when only a linear number of processors is available. Randomized Algorithm INPUT OUTPUT ALGORITHM Random Number In addition to the input, the algorithm uses a source of pseudo random numbers.

During execution, it takes random choices depending on those random numbers. The behavior (output) can vary if the algorithm is run multiple times on the same Size: KB. We present deterministic and randomized selection algorithms for parallel disk systems. The algorithms to be presented, in addition to being asymptotically optimal.

CONTENTS vi Approximatenearestneighborsearch Locality-sensitivehashfunctions Constructingan(r1,r 2)-PLEB File Size: 2MB. prehensive introduction to randomized algorithms. PARADIGMSFORRANDOMIZED ALGORITHMS In spite of the multitude of areas in which randomized algorithms find ap-plication, a handful of general the summary in Karp [], we present these principles in the follow-ing.

FoilinganAdversary. Intheclassical. Some randomized algorithms have deterministic time complexity. For example, this implementation of Karger’s algorithm has time complexity as O(E).

Such algorithms are called Monte Carlo Algorithms and are easier to analyse for worst case. On the other hand, time complexity of other randomized algorithms (other than Las Vegas) is dependent on /5.

Optimal randomized parallel algorithms for computing the row maxima of a totally monotone matrix. In Proc. 5th ACM-SIAM Symposium on Discrete Algorithms, pp. –, Google ScholarAuthor: Rajeev Raman. problem, in which lines are only available as input one after another. It is a randomized algorithm for the EREW PRAM that constructs an arrangement of n lines on-line, so that each insertion is done in optimal O(log n) time using n/ log n processors.

Both of our algorithms develop new methods for. Bryson, Joshua T., and Agrawal, Sunil K. "Using Randomized Algorithms to Quantify Uncertainty in the Optimal Design of Cable-Driven Manipulators." Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.

Volume 5A: 39th Mechanisms and Robotics Conference. Boston Author: Joshua T. Bryson, Sunil K. Agrawal. Randomized algorithms for very large matrix problems have received a great deal of attention in recent years.

Much of this work was motivated by problems in large-scale data analysis, and this work was performed by individuals from many different research communities. This monograph will provide a detailed overview of recent work on the theory of randomized matrix algorithms as well as Cited by: computer science: randomized algorithms and the probabilistic analysis of algorithms.

Randomized algorithms: Randomized algorithms are algorithms that make random choices during their execution. In practice, a randomized program would use values generated by a random number generator to decide the next step at several branches of its execution.

ForFile Size: KB. Randomized Algorithm INPUT OUTPUT ALGORITHM Random Number Randomized Algorithm In addition to the input, the algorithm uses a source of pseudo random numbers.

During execution, it takes random choices depending on those random numbers. The behavior (output) can vary if the algorithm is run multiple times on the same input. How do we analyze?You may find the text Randomized Algorithms by Motwani and Raghavan to be useful, but it is not required.

Homework policy: There will be a homework assignment every weeks. Collaboration policy: You are encouraged to collaborate on homework. However, you must write up your own solutions.

Randomized Algorithm A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic. The algorithm typically uses uniformly random bits as an auxiliary input to.