"computational settings" is correct and usable in written English.
You can use it to refer to configurations and parameters used to define the environment where a computer algorithm is run. For example, "The researchers adjusted the computational settings for the neural network to better adapt to the data."
Computational settings were selected as described elsewhere [9].
This setting occurs frequently (e.g., in mobile applications/systems) and is gaining momentum even in advanced computational settings where social networks are the main representative.
Due to the structure of the analysis problems in computational settings, the complexity of the problem space manifests itself as a high-dimensional analysis problem, where dimensionality is most often associated with the number of measurements being considered simultaneously.
In particular, our results show that using approximation in multiagent settings can be much less successful than in traditional computational settings because of the game theoretic guarantees required of the outcomes.
To understand I/O performance of data-intensive applications in realistic computational settings, we develop a lightweight, portable benchmark called MADbench2, which is derived directly from a large-scale cosmic microwave background (CMB) data analysis package.
Algorithmic mechanism design is a subfield that lies on the border of mechanism design and computer science and deals with mechanism design in algorithmically complex scenarios that are often found in computational settings such as the Internet.
We have recently coined the term "translational systems biology" to refer to the process of creating, calibrating, and validating computational simulations in settings of complex diseases, simulations that are designed a priori for the purpose of modifying clinical treatment, carrying out in silico clinical trials, generating novel therapies, and refining diagnoses [55], [56].
We also discuss ACT-R implementation and theoretic causes of ACT-R's computational performance problems and settings that appear to avoid the performance problems in ACT-R.
More specifically, with the same computational environment and model settings, the computational time required by DHSVM-MacCormack can be reduced to several dozen minutes for a simulation period of three months (in contrast with one day and a half by the original DHSVM model) without noticeable sacrifice of the accuracy.
With these parameter settings, the computational costs scale like N 0.87 for large N and the method out-performs any direct-summation implementation for N ≳ 10 5. When computing only the forces for N a < N of N particles, the costs are roughly proportional to N a 0.87 for N a / N ≳ 0.01, but become independent of N a below that (where the costs for tree building dominate).
The rather straight-forward mathematical image processing operations avoid complex computational operations and parameter settings.
