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<title>Algorithms for Generating Small Random Samples - Software: Practice and Experience - 2024</title>
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<meta name="description" content="We present algorithms for generating small random samples without replacement.
We consider two cases. We present an algorithm for sampling a pair of distinct
integers, and an algorithm for sampling a triple of distinct integers. The
worst-case runtime of both algorithms is constant, while the worst-case
runtimes of common algorithms for the general case of sampling k elements from
a set of n increase with n. Java implementations of both algorithms are
included in the open source library &rho;&mu;.">
<meta itemprop="description" content="We present algorithms for generating small random samples without replacement.
We consider two cases. We present an algorithm for sampling a pair of distinct
integers, and an algorithm for sampling a triple of distinct integers. The
worst-case runtime of both algorithms is constant, while the worst-case
runtimes of common algorithms for the general case of sampling k elements from
a set of n increase with n. Java implementations of both algorithms are
included in the open source library &rho;&mu;.">
<meta name="citation_title" content="Algorithms for Generating Small Random Samples">
<meta name="citation_author" content="Vincent A. Cicirello">
<meta name="citation_publication_date" content="2024">
<meta name="citation_date" content="2024">
<meta name="citation_journal_title" content="Software: Practice and Experience">
<meta name="citation_volume" content="Early view">
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<meta property="og:description" content="We present algorithms for generating small random samples without replacement.
We consider two cases. We present an algorithm for sampling a pair of distinct
integers, and an algorithm for sampling a triple of distinct integers. The
worst-case runtime of both algorithms is constant, while the worst-case
runtimes of common algorithms for the general case of sampling k elements from
a set of n increase with n. Java implementations of both algorithms are
included in the open source library &rho;&mu;.">
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<h2><a itemprop="url" href="/publications/cicirello2024spe.pdf"><span itemprop="name headline">Algorithms for Generating Small Random Samples</span></a></h2>
<h3><span itemprop="author" itemscope itemtype="https://schema.org/Person"><a itemprop="url mainEntityOfPage" href="/"><span itemprop="name">Vincent A. Cicirello</span></a></span></h3>
<h4><span itemprop="isPartOf" itemscope itemtype="https://schema.org/Periodical"><i><span itemprop="name">Software: Practice and Experience</span></i></span>,
<span itemprop="isPartOf" itemscope itemtype="https://schema.org/PublicationVolume"><span itemprop="volumeNumber">Early view</span></span>: <span itemprop="pageStart">1</span>-<span itemprop="pageEnd">9</span>,
<time datetime="2024-09" itemprop="datePublished">September 2024</time>. doi:10.1002/spe.3379</h4>
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<h4>Abstract</h4>
<div itemprop="description"><p>We present algorithms for generating small random samples without replacement.
We consider two cases. We present an algorithm for sampling a pair of distinct
integers, and an algorithm for sampling a triple of distinct integers. The
worst-case runtime of both algorithms is constant, while the worst-case
runtimes of common algorithms for the general case of sampling k elements from
a set of n increase with n. Java implementations of both algorithms are
included in the open source library &rho;&mu;.</p></div>
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<h3><a itemprop="mainEntityOfPage url" href="/"><span itemprop="name">Vincent A. Cicirello</span>, <span itemprop="honorificSuffix">Ph.D.</span></a></h3>
<h4>Current Position:</h4><br>
<span itemprop="jobTitle">Professor of Computer Science</span><br><br>
<h4>Mailing Address:</h4><br>
School of Business<br>
Stockton University<br>
101 Vera King Farris Dr<br>
Galloway, NJ 08205<br><br>
<h4>E-mail:</h4>
<a rel="nofollow" href="/e/">contact</a>
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<h4>Phone:</h4>
(609) 626-3526<br><br>
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