MATP6640 / ISYE6770 Linear and Conic Optimization
Spring 2020

Course outline.

Grades, software, notes, and other material will be posted on LMS.

Office hours: In Amos Eaton 325 On Webex Meetings on Mondays 2-4pm, Wednesdays 11am-1pm, or by appointment.

Material on reserve in the library.

Scores are available on LMS

Projects:

Midterm Exam: In class on Thursday, March 19.
It will cover everything seen in class through Monday, March 16 (Lecture 16).
You can bring one sheet of handwritten notes, no larger than 8.5" x 11". You can write on both sides.

Here are the solutions. Here are the robust ampl model and run file, where you can just change the seed. Here are the robust ampl model and run file, which require the use of a data file containing the matrix A.
Your graded exam should be availale on LMS.

Old exams:

Homework:

Information about AMPL.

Notes: These are typed pdf notes. Handwritten scanned copies of my notes from previous semesters can be found here.


  • SDP and SOCP.
    Lecture 28: Pages 35/72 to 53/72 and 67/72 to 69/72 from this talk on interior point cutting plane methods should be covered. It is based on a paper.
    The book Convex Optimization, by Boyd and Vandenberghe, contains a wealth of material on SDP, SOCP, and conic programming.
  • The material on linear programs with complementarity constraints in Lecture 28 is from two talks: slides 4/54 to 14/54 from this one and slides 12/41 to 24/41 from this one.

    Handouts:

    1. Linear algebra. (Lecture 1.)
    2. Subspaces, affine sets, convex sets, and cones. (Lecture 1.)
    3. Dimension, polyhedra, and faces. (Lecture 1.)
    4. An iteration of the simplex algorithm and the algorithm. (Lecture 4.)
    5. Handling upper bounds in the simplex algorithm. (Lecture 5.)
    6. The dual simplex algorithm. (Lecture 6.)
    7. Extreme points and extreme rays of polyhedra. (Lecture 8.)
    8. An example of Dantzig-Wolfe decomposition. (Lecture 9.)

    Papers and resources:


    Return to John Mitchell's homepage.