Special Topics in Theory: A Principled Approach to Optimization Course ID 15759 Description This is a course giving a rigorous treatment of several topics in the theory of convex optimization. There will be a particular focus on developing intuition for how to analyze many convex optimization processes from first principles. Topics may include: gradient descent, interior point methods, linear regression, linear programming, sparsification, and more. Key Topics Fundamental topics in convex optimization theory will be studied, as will as general tools for analyzing processes in high dimensions. Required Background Knowledge This course assumes familiarity with multivariable calculus, linear algebra, and some mathematical maturity. Course Relevance This course is open to advanced undergraduate and graduate students with an interest in mathematics and computer science. Course Goals To understand how to analyze algorithms in convex optimization, and how to apply them to solve specific objectives. To develop intuition and techniques for developing and analyzing processes in high dimensions. Learning Resources The course may reference research articles and notes available online. Assessment Structure Homework: 80%, and Final Project: 20% Extra Time Commitment n/a Course Link https://yangpliu.github.io/opt
