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Las Vegas algorithm

About: Las Vegas algorithm is a research topic. Over the lifetime, 130 publications have been published within this topic receiving 4340 citations.


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01 Jan 2014
TL;DR: A remarkable feature of these algorithms is the use of only constant space per processor, which constitutes a signicant improvement upon previous algorithms whose space requirements per processor depend on the (possibly huge) tree to be explored.
Abstract: We present space-ecient parallel strategies for two fundamental combinatorial search problems, namely, backtrack search and branch-and-bound, both involving the visit of an n-node tree of height h under the assumption that a node can be accessed only through its father or its children. For both problems we propose ecient algorithms that run on a p-processor distributed-memory machine. For backtrack search, we give a deterministic algorithm running in O (n=p + h logp) time, and a Las Vegas algorithm requiring optimal O (n=p + h) time, with high probability. Building on the backtrack search algorithm, we also derive a Las Vegas algorithm for branch-and-bound which runs in O (n=p + h logp logn)h log 2 n time, with high probability. A remarkable feature of our algorithms is the use of only constant space per processor, which constitutes a signicant improvement upon previous algorithms whose space requirements per processor depend on the (possibly huge) tree to be explored.
Proceedings ArticleDOI
13 Nov 2020
TL;DR: Wang et al. as mentioned in this paper proposed a novel wrapped feature selection framework based on the Las Vegas algorithm, which can improve the detection accuracy by strengthening the coupling between the feature selection and the model training.
Abstract: The power system measurement data has high-dimensional features and strong noise, which is difficult to be directly used for intrusion detection Traditional feature extraction and selection methods take the feature processing as a preprocessing step and perform separately from the model training, which makes the features not well adapted to the model Therefore, we propose a novel wrapped feature selection framework based on the Las Vegas algorithm, which can improve the detection accuracy by strengthening the coupling between the feature selection and the model training The Las Vegas algorithm can evaluate a feature subset through a specified model In this paper, a heuristic thinking is integrated into the Las Vegas algorithm, which greatly improves the search performance of the original method Finally, the proposed framework is examined on the IEEE 14-bus, 39-bus and 57-bus test systems for two attacks and the experimental results proves the effectiveness and stability of the proposed framework
01 Jan 2014
TL;DR: This paper implements and enhances performance of software random testing and uses both Monte Carlo and Las Vegas Randomized algorithms to improve the efficiency of random testing.
Abstract: this paper implements and enhances performance of software random testing. Random testing is a base software testing technique that can be used to improve the software reliability as well as to discover software failures. Random testing is a black-box software testing technique where programs are tested by generating random, independent inputs. In proposed methods uses both Monte Carlo and Las Vegas Randomized algorithms. Monte Carlo has fast execution while Las Vegas has low execution time, but sometimes Monte Carlo algorithm gives false result while Las Vegas gives always correct result. In proposed method has two result sets, in first result set has executed test cases and in second has fails test cases. Initially test cases are tested using Monte Carlo algorithm and produced executed and fail result sets. The fail result set is again tested using Las Vegas algorithm because sometimes Monte Carlo gives false result. We present a technique that improves performance random testing. These results are very hopeful, given that evidences that our perception is likely to be useful in improving the efficiency of random testing.
Proceedings ArticleDOI
23 Nov 2020
TL;DR: This poster proposes an improvement to the q-MAX algorithm that leverages sampling to accelerate the computation and runs up to 62% faster when evaluated on real packet traces and tasks.
Abstract: The q-MAX problem, which seeks to find the q largest elements in a data stream, has numerous networking applications including sketches, network-wide heavy hitters, and others. In this poster, we propose an improvement to the q-MAX algorithm [5] that leverages sampling to accelerate the computation. Despite being randomized, our algorithm never fails (i.e., it is a Las Vegas algorithm) and runs up to 62% faster when evaluated on real packet traces and tasks. Moreover, on a real networking application and workload, our algorithm provides an 11-53% higher throughput.
Proceedings ArticleDOI
TL;DR: In this paper, principal subfields and fast subfield-intersection techniques are used to compute the subfield lattice of a rational function, which yields a Las Vegas algorithm with improved complexity and better run times for finding all nonequivalent complete decompositions of the function.
Abstract: Let $f\in K(t)$ be a univariate rational function. It is well known that any non-trivial decomposition $g \circ h$, with $g,h\in K(t)$, corresponds to a non-trivial subfield $K(f(t))\subsetneq L \subsetneq K(t)$ and vice-versa. In this paper we use the idea of principal subfields and fast subfield-intersection techniques to compute the subfield lattice of $K(t)/K(f(t))$. This yields a Las Vegas algorithm with improved complexity and better run times for finding all non-equivalent complete decompositions of $f$.
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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
20214
20209
20199
20184
20177