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When a label is the result of a very expensive computer experiment (in terms of computational time), sequential selection of samples can be used to limit the overall cost of acquiring the labeled data.
In this paper, we compare the use of selective reorthogonalization with other model order reduction techniques, both in terms of computational time and in accuracy, using three computer architectures.
Successful use of surrogates shall result in significant savings in terms of computational time and resources.
Both approaches can be extremely expensive in terms of computational time.
The results indicate that the proposed algorithm is superior to an off-the-shelf solver in terms of computational time.
Also, high efficiency in terms of computational time is achieved when the proposed method is used as compared with FEM.
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The performance of both optimization techniques in term of computational time and convergence rate is also compared.
The performance of both optimization techniques in terms of computational effort, computational time and convergence rate is compared.
Despite their simplicity, these algorithms are robust and very fast in terms of computational running time.
We also implement a prototype of the credential system and analyze the feasibility of our solution in terms of computational cost and time to perform such telematic controls.
In terms of computational efficiency and time processing, Big Data motivate the development of new computational tools and data storage methods [17, 20, 27, 29].
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in terms of computational domain
in terms of computational efficiency
in terms of computational power
in terms of computational cost
in terms of computational expense
in terms of computational performance
in terms of computational effort
in terms of computational burden
in terms of computational speed
in terms of computation time
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