Course objectives

• to give students the tools and training to recognize convex optimization problems that arise in applications

• to present the basic theory of such problems, concentrating on results that are useful in computation

• to give students a thorough understanding of how such problems are solved, and some experience in solving them

• to give students the background required to use the methods in their own research work or applications

  
  

Intended audience

This course should benefit anyone who uses or will use scientific computing or optimization in engineering or related work (e.g., machine learning, finance). More specifically, people from the following departments and fields: Electrical Engineering (especially areas like signal and image processing, communications, control, EDA & CAD); Aero & Astro (control, navigation, design), Mechanical & Civil Engineering (especially robotics, control, structural analysis, optimization, design); Computer Science (especially machine learning, robotics, computer graphics, algorithms & complexity, computational geometry); Operations Research (MS&E at Stanford); Scientific Computing and Computational Mathematics. The course may be useful to students and researchers in several other fields as well: Mathematics, Statistics, Finance, Economics.

Textbook and optional references

The textbook is Convex Optimization, available online, or in hard copy form at the Stanford Bookstore.

Several texts can serve as auxiliary or reference texts:

• Bertsekas, Nedic, and Ozdaglar, Convex Analysis and Optimization

• Ben-Tal and Nemirovski, Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications

• Nesterov, Introductory Lectures on Convex Optimization: A Basic Course

• Ruszczynski, Nonlinear Optimization

• Borwein & Lewis, Convex Analysis and Nonlinear Optimization