Convex Optimization | Stanford University Course

Gain a solid foundation in convex optimization and learn practical applications in fields like machine learning, signal processing, and more.

Introduction

The course concentrates on recognizing and solving convex optimization problems that arise in applications. Topics addressed include convex sets, functions, and optimization problems, basics of convex analysis, least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems, optimality conditions, duality theory, theorems of alternative, and applications, as well as interior-point methods. The course covers applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

Highlights

• Covers a wide range of convex optimization problems and techniques
• Focuses on practical applications in various fields
• Taught by experienced Stanford faculty

Recommendation

This course is highly recommended for students and professionals interested in optimization, machine learning, and their applications in various domains. It provides a solid foundation in convex optimization and equips learners with the necessary tools to tackle real-world optimization challenges.