• 50% Homeworks, 45% Final Exam, 5% Course Participation (in class or on the Discussion board)
  • Weekly homework assignments (total of 8 homework sets), which involve some Matlab (or Python) programming.
  • The final exam will be an open-book in-class exam on Friday December 16, 2016.
Course Description
This course concentrates on recognizing and solving convex optimization problems that arise in engineering and sciences. The syllabus includes:
  • Basics of convex analysis: Convex sets, functions, and optimization problems.
  • Optimization theory: Least-squares, linear, quadratic, geometric and semidefinite programming, and other problems. Optimality conditions, duality theory, and applications.
  • Applications: in machine learning, signal processing, control, statistics, finance and engineering design.
Course Objectives
  • give students the theoretical tools and training to recognize and formulate convex optimization problems (convex modeling).
  • present the basic theory of such problems, focusing on results that are useful in computation.
  • give students some experience in solving these problems, and the background required to use convex optimization in their research.
Textbook and Optional References
You are expected to read the main textbook,
  • S. Boyd and L. Vandenberghe, Convex Optimization, Available as free pdf online or hard copy in the bookstore.
  • Errata list for the textbook
Optional books that serve as secondary references:
  • A. Ben-Tal and A. Nemirovski, Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications. MPS/SIAM Series on Optimization.
  • J.B. Hiriart-Urruty and C. Lemarechal, Convex Analysis and Minimization Algorithms, Springer.
  • D. Bertsekas, A. Nedic, A. Ozdaglar, Convex Analysis and Optimization. Athena Scientific.
  • J. Borwein, A. Lewis, Convex Analysis and Nonlinear Optimization: Theory and Examples. Springer-Verlag.
  • D. Luenberger, Linear and Nonlinear Programming, Addison-Wesley.
  • J. Nocedal and S. Wright, Numerical Optimization, Springer.
  • R.T. Rokafellar, Convex Analysis, Princeton publishing.
  • Yu. Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, Kluwer. (--for Algorithms; not covered in this course.)