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Given the computational complexity of SAT, it is therefore important to develop algorithms that either require the smallest possible number of calls to the SAT solver, or that involve simpler instances.
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We first consider a simpler instance of this problem where there is only one machine.
Every choice has a consequence, but so do accidents, mistakes and simple instances of bad luck.
Due to the size and complexity of the stochastic program and the impossibility of using commercial software in even the simplest instances, two solution methods have been proposed.
One of the simplest instance of relational (or context) effects in perception is that of brightness contrast.
In the human situation, learning to recognize the name of an object or a foreign word constitutes a simple instance of stimulus learning.
A simple instance of this sort proves little or nothing, but it may suggest what is needed namely, that Lucy should understand that changing the order of a set of signs alters their meaning in certain predictable ways.
A simple instance of the Classifier has been also developed.
First, PicHunter represents a simple instance of a general Bayesian framework we describe for using relevance feedback to direct a search.
We first show how a well-known efficient algorithm for computing stuttering equivalence can be viewed as a simple instance of GPT.
Garey and Johnson [11, 12] show that the simplest instance of the decision problem corresponding to this problem is NP-complete.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com