STOCHASTIC INFLUENCE ON GRADIENT NUMERICAL METHODS FOR NONLINEAR LEAST SQUARES PROBLEMS - Christopher, Gerend
The development of knowledge in the fields of mathematics, science and technology has brought humans into the information and digital era. This change leads to changes in the amount of data collected to be extracted and used for various purposes, one of them through mathematical models. There are many forms of mathematical models that are built to solve cases in certain domains. In this research, the focus will be on solving nonlinear least squares problems. In building a model, the model that wanted to be built is an optimal model, that is, it has good accuracy and is efficient. In practice, models are built using numerical methods. The subject of this research study is the influence of stochastic in numerical methods that utilize gradients, namely Gradient Descent and Levenberg-Marquardt, on accuracy and computational efficiency. The experimental results show that overall accuracy is not significantly different when compared to non-stochastic methods and that computing time can be more effective when the maximum iteration of the stochastic method is used without significantly affecting accuracy.