IEOR 262B: Mathematical Programming II

Instructor: Javad Lavaei
Time: Tuesdays and Thursdays, 12:30-2pm
Location: 3119 Etcheverry
TA: SangWoo Park (spark111 AT
Instructor's Office Hours: Mondays, 11am-noon (4121 Etcheverry)
TA's Office Hours: Wednesdays, 10:00-11:15am (4176-A Etcheverry)
Grading Policy:

  • 15% homework

  • 40% exam

  • 45% project


This course provides a fundamental understanding of general nonlinear optimization theory, convex optimization, conic optimization, numerical algorithms, and distributed computation. Some of the topics covered in this course are as follows:

  • Local and global optimality

  • Optimality conditions

  • Lagrangian and duality

  • Convex optimization

  • Conic optimization

  • Low-rank optimization

  • Convexification techniques and hierarchies of convex relaxation

  • Numerical algorithms and convergence analysis (including descent algorithms, interior-point methods, etc. )

  • Decomposition and distributed algorithms


  • “Nonlinear Programming” by Dimitri P. Bertsekas, Athena Scientific, 3rd Edition.

  • “Linear and Nonlinear Programming” by David Luenberger and Yinyu Ye, Springer, 4th Edition.

  • “Convex Optimization” by Stephen Boyd and Lieven Vandenberghe, Cambridge University Press, 2004 (click here to download the book).

  • ‘‘Low-Rank Semidefinite Programming: Theory and Applications" by Alex Lemon, Anthony Man-Cho So and Yinyu Ye, Foundations and Trends in Optimization, 2015 (click here to download the monograph).