Machine learning method and information processing apparatus

A machine learning method comprising:

• acquiring, by a processor, a coefficient matrix representing coefficients included in a linear system;
• generating, by the processor, a hypergraph based on the coefficient matrix, the hypergraph including a plurality of nodes corresponding to a plurality of variables included in the linear system and a plurality of edges corresponding to a plurality of linear equations included in the linear system, each of the plurality of edges connecting nodes corresponding to positions of non-zero coefficients included in the coefficient matrix among the plurality of nodes;
• transforming, by the processor, a plurality of first features associated with the plurality of nodes into a plurality of second features using a machine learning model including a parameter value;
• computing, by the processor, a plurality of third features to be associated with the plurality of nodes, from the plurality of second features, based on a connection relationship between the plurality of nodes represented by the plurality of edges;
• and
• updating, by the processor, the parameter value based on an error obtained from the plurality of third features.

An information processing apparatus comprising:

• a memory configured to store a coefficient matrix representing coefficients included in a linear system;
• and
• a processor coupled to the memory and the processor configured to:
• generate a hypergraph based on the coefficient matrix, the hypergraph including a plurality of nodes corresponding to a plurality of variables included in the linear system and a plurality of edges corresponding to a plurality of linear equations included in the linear system, each of the plurality of edges connecting nodes corresponding to positions of non-zero coefficients included in the coefficient matrix among the plurality of nodes;
• transform a plurality of first features associated with the plurality of nodes into a plurality of second features using a machine learning model including a parameter value;
• compute a plurality of third features to be associated with the plurality of nodes, from the plurality of second features, based on a connection relationship between the plurality of nodes represented by the plurality of edges;
• and
• update the parameter value based on an error obtained from the plurality of third features.