Nonlinear Programming

Nonlinear programming (NLP) focuses on finding optimal solutions to problems where the objective function or constraints are nonlinear, encompassing a wide range of applications from robotics and control to machine learning and supply chain optimization. Current research emphasizes efficient algorithms, including those based on sequential quadratic programming (SQP), augmented Lagrangian methods, and graph neural networks, to tackle increasingly complex problems, particularly those involving mixed-integer variables and stochasticity. These advancements are improving the speed and reliability of solving NLPs, leading to more effective solutions in diverse fields and enabling the analysis of larger-scale, more realistic models.