Optimization consists of maximizing or minimizing a real-valued objective function. In many problems, the objective function may not yield closed-form solutions. Over many decades, optimization methods, both deterministic and stochastic, have been developed to provide solutions to these problems. However, some common limitations of these methods are the sensitivity to the initial value and that often current methods only find a local (non-global) extremum. In this article, we propose an alternative stochastic optimization method, which we call "Forward Slice", and assess its performance relative to available optimization methods.
Salim, Bob A. and Inoue, Lurdes Y. T., "Stochastic Optimization via Forward Slice" (May 2015). UW Biostatistics Working Paper Series. Working Paper 406.