Optimization for Engineered Planning, Decisions and Operations

3 units

This course will introduce students to the philosophy and skillset of applied optimization with an emphasis on its utility in context. Students will engage with both exact, analytical methods and iterative, numerical methods in a variety of settings, all heavily drawing on MatLab or Python. We will explore linear and integer programming, dynamic programming, the simplex method, network optimization, critical point theory, gradient methods, line search methods, and least squares. Applications include: scheduling, signal denoising, urban planning, portfolio optimization, regression, machine learning.