Some results of the K-means algorithm with a randomly generated instance where n = 2 ⋅ 106, k = 200, and d = 2.</p
The k-means method is a widely used clustering algorithm. One of its distinguished features is its s...
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization...
Random number generators are widely used in practical algorithms. Examples include simulation, numbe...
UnrestrictedAn algorithm can be defined as a set of computational steps that transform the input to ...
I returned, and saw under the sun, that the race is not to the swift, nor the battle to the strong, ...
Random number generators are widely used in practical algorithms. Examples include simulation, numbe...
The technique of randomization has been employed to solve numerous prob lems of computing both sequ...
Heuristic algorithms are often difficult to analyse theoretically; this holds in particular for adva...
AbstractResearch conducted over the past fifteen years has amply demonstrated the advantages of algo...
Introduction We have already seen some uses of randomization in the design of on-line algorithms. I...
•Consultation: Appointment by email General Information 3•How randomness and probability can help in...
We present polynomial upper and lower bounds on the number of iterations performed by the k-means me...
this report we shall present the fundamentals of random number generation on parallel processors. We...
Abstract: K-means is the most popular algorithm for clustering, a classic task in machine learning a...
In modern computer science, many problems are solved with the help of probabilistic algorithms. This...
The k-means method is a widely used clustering algorithm. One of its distinguished features is its s...
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization...
Random number generators are widely used in practical algorithms. Examples include simulation, numbe...
UnrestrictedAn algorithm can be defined as a set of computational steps that transform the input to ...
I returned, and saw under the sun, that the race is not to the swift, nor the battle to the strong, ...
Random number generators are widely used in practical algorithms. Examples include simulation, numbe...
The technique of randomization has been employed to solve numerous prob lems of computing both sequ...
Heuristic algorithms are often difficult to analyse theoretically; this holds in particular for adva...
AbstractResearch conducted over the past fifteen years has amply demonstrated the advantages of algo...
Introduction We have already seen some uses of randomization in the design of on-line algorithms. I...
•Consultation: Appointment by email General Information 3•How randomness and probability can help in...
We present polynomial upper and lower bounds on the number of iterations performed by the k-means me...
this report we shall present the fundamentals of random number generation on parallel processors. We...
Abstract: K-means is the most popular algorithm for clustering, a classic task in machine learning a...
In modern computer science, many problems are solved with the help of probabilistic algorithms. This...
The k-means method is a widely used clustering algorithm. One of its distinguished features is its s...
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization...
Random number generators are widely used in practical algorithms. Examples include simulation, numbe...
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