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Intellectualization Of Computation Based On Neural Networks For Mathematical Modeling

The problem of automation of efficient algorithm choice for solving linear algebra problems with a priori undefined structure of sparse matrices is considered. An approach of matrix classification and selection of an effective algorithm based on a visual portrait of a sparse matrix using a neural networks is proposed. Datasets for training of convolutional neural network «Sparse Matrix Vision» were prepared. Computational experiments, conducted in a scope of a current research showed that the algorithm chosen by neural network is effective for a specific problem.