IEOR 160: Nonlinear and Discrete Optimization
Instructor: Javad Lavaei
Time: Mondays and Wednesdays, 910 am
Location: 105 North Gate
Office Hours: Mondays and Wednesdays, 1011 am
TAs: Sheng Liu (lius10@berkeley.edu) and Salar Fattahi (fattahi@berkeley.edu)
Grading Policy:
15% homework
15% first midterm exam (October 7th)
15% second midterm exam (November 4th)
55% final exam (December 17th)
Description
Syllabus:
Main textbook: “Introduction to Mathematical Programming: Volume One“ by Wayne L. Winston and Munirpallam Venkataramanan (fourth edition)
Lecture Notes
Lecture 1: Overview of the course
Lecture 2: Modeling and mathematical formulation
Lecture 3: Unconstrained univariate optimization
Lecture 4: Constrained univariate optimization
Lecture 5: Golden Section Search
Lecture 6: Unconstrained multivariate optimization
Lecture 7: Unconstrained multivariate optimization
Lecture 8: Numerical Algorithms for unconstrained optimization
Lecture 9: Numerical Algorithms for unconstrained optimization
Lecture 10: Numerical Algorithms for unconstrained optimization; convex functions
Lecture 11 (not available online): Review for Midterm 1
Midterm 1
Lecture 12: Constrained optimization with equality constraints
Lecture 13: Constrained optimization with equality constraints
Lecture 14: Constrained optimization with equality constraints
Lecture 15: Constrained optimization with inequality constraints
Lecture 16: Constrained optimization with inequality constraints; convex optimization
Lecture 17: Convex optimization
Lecture 18 (not available online): Review for Midterm 2
Midterm 2
Lecture 19: Convex optimization; Lagrangian
Lecture 20: Duality
Lecture 21: Convexification
Lecture 22: Mixedinteger nonlinear program, branchandbound method
Lecture 23: Branchandbound method
Lecture 24: Cutting plane method
Homework
