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Teaching

IEOR 160: Nonlinear and Discrete Optimization

Instructor: Javad Lavaei
Time: Mondays and Wednesdays, 9-10 am
Location: 105 North Gate
Office Hours: Mondays and Wednesdays, 10-11 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:

  • Local and global optimality

  • Optimality conditions

  • Numerical algorithms

  • Convex optimization

  • Duality

  • Convex relaxation

  • Integer programming

  • Branch and bound method

  • Cutting plane method

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: Mixed-integer nonlinear program, branch-and-bound method

  • Lecture 23: Branch-and-bound method

  • Lecture 24: Cutting plane method

Homework