Computational Scaling‌ For Gaussian Sample Clauses

Computational Scaling‌ For Gaussian. Process prediction with a dynamically growing database, a pronounced problem is the computational effort required to invert the large-dimensional covariance matrix CN , which typically scales as O(N 3), where N is the rank of CN . To address this problem, one suggested approach is to use selected sub-database, keeping in mind the fact that the sorting/selecting algorithms have optimal scaling factor of O(Ndata log Ndata), with Ndata being the overall size of the teaching database. The database can be sorted in such way that only the most relevant configurations are selected for the GP teaching process. Since only Nteach-dimensional (typically around 500 in our calculation) matrices are involved in the inverting calculation, the force prediction becomes robust. There are three parts which are majorly involved in the time cost of the MLOTF force calculation: T = Tdist + Tsort + Tinvt.
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