COMPARISON OF OPTIMIZATION METHODS AIMED AT REDUCING CALCULATION TIME FOR LOCATION EQUILIBRIUM MODELS

Abstract

 As the population declines, it is expected that the scale of activity in the urban will shrink. In such the situation, it is effective to analyze and evaluate using an economic model that can express space in order to consider the development of infrastructure that exists in the urban for a long period of time. On the other hand, in recent years, as changes in the conditions for building the model, there are enhancement of the data environment and complexity of setting. Against this background, the computational load of the model tends to increase, and reducing it is one of the issues. This paper aims to show the improving the calculation time of the economic model that can express the space. We applied RMSprop and Adam, which are used in deep learning, to the optimization calculation of the existing model, and compared the results. As a result, it was verified that RMSprop and Adam contributed sufficiently to the efficiency of calculation time depending on the setting of the learning rate.