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IEMS 314: Nonlinear Optimization for Decision Making

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Prerequisites

GEN_ENG 150 or COMP_SCI 150 GEN_ENG 231 GEN_ENG 241 MATH 228-1; or equivalent

Description

Theory and algorithms of nonlinear optimization as a bridge between deterministic linear models and data‐driven
decision making under uncertainty. This course covers convex and nonconvex modeling, first‐ and second‐order
optimality conditions, duality theory, and numerical methods including gradient‐based, stochastic, Newton‐type, and
constrained optimization algorithms. Case studies from machine learning, finance, and engineering motivate the
formulation and analysis of real‐world nonlinear models. Students implement foundational solvers from first
principles, construct more advanced solvers using generative‐AI–assisted development, and deploy these methods
on applications such as nonlinear regression and classification, portfolio optimization, and parameter estimation in
complex systems.Syllabus