46-976 Financial Optimization
6 units, Carnegie Mellon University
Optimization techniques play an increasingly important role in a range of financial and data science problems. Many computational finance problems ranging from asset allocation to risk management, from option pricing to model calibration, can be efficiently solved using modern optimization techniques. Similarly, many models and tasks in data analysis including a variety of regression models, maximum likelihood estimation, and clustering and deep learning models, can be formulated and tackled as optimization models. This course covers several classes of optimization models (linear, quadratic, stochastic, and dynamic optimization) encountered in financial and data science contexts. For each model class, after a survey of the main theory and solution methods, we will discuss some applications in mathematical finance that are amenable to that problem class. The course will also cover first-order methods, which are currently the most popular and effective class of algorithms to solve large-scale optimization problems. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with the instructor's permission.
Prerequisites: 46-972 and 46-921
Offered: Fall 2026, Fall 2025
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