Lala Septem Riza, Dendi Handian, Rani Megasari, Ade Gafar Abdullah, Asep Bayu Dani Nandiyanto, Shah Nazir
Nowadays, many variants of gradient descent (i.e., the methods included in machine learning for regression) have been proposed. Moreover, these algorithms have been widely used to deal with real-world problems. However, the implementations of these algorithms into a software library are few. Therefore, we focused on building a package written in R that includes eleven algorithms based on gradient descent, as follows: Mini-Batch Gradient Descent (MBGD), Stochastic Gradient Descent (SGD), Stochastic Average Gradient Descent (SAGD), Momentum Gradient Descent (MGD), Accelerated Gradient Descent (AGD), Adagrad, Adadelta, RMSprop and Adam. Additionally, experimental analysis on prediction of the CO2 compressibility factor were also conducted. The results show that the accuracy and computational cost are reasonable, which are 0.0085 and 0.142 second for the average of root mean square root and simulation time. © School of Engineering, Taylor’s University.
Department of Computer Science, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi 229, Bandung, 40154, Indonesia; Departmen Pendidikan Teknik Elektro, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi 229, Bandung, 40154, Indonesia; Departmen Kimia, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi 229, Bandung, 40154, Indonesia; Department of Computer Science, University of Swabi, Swabi, Pakistan
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