In this research, stochastic computing techniques based on artificial neural networks are
applied to the proposed singular nonlinear differential equation to explain thermoregulation …

We propose a trust-region stochastic sequential quadratic programming algorithm (TR-
StoSQP) to solve nonlinear optimization problems with stochastic objectives and …

We study nonlinear optimization problems with a stochastic objective and deterministic
equality and inequality constraints, which emerge in numerous applications including …

A sequential quadratic optimization algorithm for minimizing an objective function defined by
an expectation subject to nonlinear inequality and equality constraints is proposed …

Backpropagation neural networks are commonly utilized to solve complicated issues in
various disciplines. However, optimizing their settings remains a significant task. Traditional …

A step-search sequential quadratic programming method is proposed for solving nonlinear
equality-constrained stochastic optimization problems. It is assumed that constraint function …

This study introduces a novel method for addressing the pentagonal quadratic fractional
programming problem (PQFPP). We employ pentagonal neutrosophic numbers for the …

This paper presents a methodology for using varying sample sizes in sequential quadratic
programming (SQP) methods for solving equality constrained stochastic optimization …

In this paper, we propose a method that has foundations in the line search sequential
quadratic programming paradigm for solving general nonlinear equality constrained …

We apply a stochastic sequential quadratic programming (StoSQP) algorithm to solve
constrained nonlinear optimization problems, where the objective is stochastic and the …