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NP-complete problem, any of a class of computational problems for which no efficient solution algorithm has been found.
A class of computational algorithms determining the structure of vector fields are stated.
Monte Carlo methods are a class of computational algorithms that numerically approximate functions of interest by random sampling.
GAs are a class of computational techniques to find approximate solutions in optimization and search problems [29, 30].
The classical approach in analog signal processing circuits is to implement, for each circuit function, a class of computational structures.
ABM is a class of computational models for simulating the actions and interactions of autonomous agents with each other and the environment.
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Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines or agents to simulate, from the bottom-up, macroscopic properties of a system.
We expect that related approaches will, in the future, be applicable to other physical quantities of interest as well as to a broader class of computational tools.
This article presents a new class of computational methods, known as dimensional decomposition methods, for calculating stochastic sensitivities of mechanical systems with respect to probability distribution parameters.
These are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results; typically one runs simulations/measures many times over in order to obtain the distribution of an unknown probabilistic entity [33, 43, 45].
It is noted that the H-Dispatch algorithm is quite general and can be applied to a wide class of computational tasks on heterogeneous architectures involving multi-core and GPGPU hardware.
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