ORIE 5320

Optimization for AI

Course Description

Course information provided by the 2025-2026 Catalog.

From fitting regression models to training neural networks, many problems in artificial intelligence can be cast as optimization problems. This course studies the fundamentals of optimization as they apply to artificial intelligence. Much of the focus will be on convex optimization, covering topics such as convex analysis, gradient descent for constrained and unconstrained problems, proximal gradient descent, variants of Newton’s method, and the Frank-Wolfe algorithm. The treatment will be at a fairly sophisticated level, giving sound justifications for the algorithms studied.

Enrollment Priority Recommended prerequisite: integral calculus at the level of Math 1910 and linear algebra at the level of Math 2940.

Last 4 Terms Offered (None)

Learning Outcomes

• Formulate and analyze unconstrained and constrained nonlinear optimization problems, identifying convexity, feasibility, and optimality conditions.
• Apply and implement first- and second-order optimization algorithms, and evaluate their convergence behavior in practice.
• Model and solve real-world applications using nonlinear optimization techniques in areas such as machine learning, engineering, and operations research.

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