Computer-readable recording medium storing arithmetic program, arithmetic method, and information processing device

An arithmetic method implemented by a computer, the arithmetic method including a repeatedly executed process comprising:

• creating an Ising model based on a training data set that includes a plurality of pieces of training data in which an input variable and an evaluation value are associated with each other;
• searching for a value of a recommended input variable based on the Ising model;
• giving a variation to the searched value of the recommended input variable and calculating the evaluation value for the value of the recommended input variable after the variation is given;
• and
• adding the value of the recommended input variable and the calculated evaluation value to the training data set as new training data when the value of the recommended input variable does not exist in the training data included in the training data set, and updating a statistical value of the evaluation value of the training data and the evaluation value for the value of the recommended input variable after the variation is given as the evaluation value of the training data when the value of the recommended input variable exists in the training data included in the training data set.

An arithmetic apparatus comprising:

• a memory, and
• a processor coupled to the memory and configured to repeatedly execute a process of
• creating an Ising model based on a training data set that includes a plurality of pieces of training data in which an input variable and an evaluation value are associated with each other;
• searching for a value of a recommended input variable based on the Ising model;
• giving a variation to the searched value of the recommended input variable and calculating the evaluation value for the value of the recommended input variable after the variation is given;
• and
• adding the value of the recommended input variable and the calculated evaluation value to the training data set as new training data when the value of the recommended input variable does not exist in the training data included in the training data set, and updating a statistical value of the evaluation value of the training data and the evaluation value for the value of the recommended input variable after the variation is given as the evaluation value of the training data when the value of the recommended input variable exists in the training data included in the training data set.