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In the sensitivity techniques, the adjoint variable method is quite popular because it reduces computation time and save computer resources.
Our approach reduces computation time and enhances the practical applications of the SVR technique.
Using fewer pairs reduces computation time, but may lead to a less-precise determination of source properties.
An important feature of this algorithm is a variable reordering scheme that greatly reduces computation time.
The NSPE algorithm is more accurate and reduces computation time significantly when compared to the traditional methods.
First, we introduce a new SPP algorithm, a random scan sampling algorithm (RSSA), that significantly reduces computation time.
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Therefore cooperation reduces computation times by more than 85%.
We also show how to reduce computation time on computers with a cache-based architecture.
Both of these computing architectures reduce computation time by exploiting the parallelism inherent in the application.
The use of parallel computing could drastically reduce computation time for MDR, particularly on a cluster machine.
This innovative approach has undeniable improvements such us computational load distribution and reduced computation time.
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