Information processing apparatus which obtains submatrices based on an input sparse matrix

An information processing apparatus, comprising:

• a memory containing program instructions;
• and
• a processor coupled to the memory, wherein the processor is configured to execute the program instructions to:
• store a first submatrix for rows having a predetermined number or more of non-zero elements among rows in a sparse matrix in row-major order dense matrix format;
• store a second submatrix for columns having a predetermined number or more of non-zero elements among columns of a third submatrix other than the first submatrix in the sparse matrix in column-major order dense matrix format;
• store a fourth submatrix for a part of rows in a fifth submatrix other than the second submatrix among the third submatrix in row-major order sparse matrix compression format;
• store a sixth submatrix other than the fourth matrix among the fifth submatrix in column-major order sparse matrix compression format;
• store a vector that can be multiplied by the sparse matrix to calculate a product;
• individually calculate a product of the submatrix stored in the column-major order dense matrix format and the vector, a product of the submatrix stored in the row-major order dense matrix format and the vector, a product of the submatrix in the row-major order sparse matrix compression format and the vector, and a product of the submatrix stored in the column-major order sparse matrix and the vector;
• add the calculated products to each other, thereby calculating a product of the sparse matrix and the vector;
• and
• obtain a learned model by performing statistical machine learning according to a parameter vector indicating a set of parameters of a model to be learned, by using the product of the sparse matrix and the vector.