Course Syllabus

[LAST UPDATED March 29, 2019]

  • ABOUT THIS CLASS. The goal of this course is give an introduction to the optimization in depth, will focus on duality of linear programming, constrained problem, stochastic algorithms, neural network.
  • PREREQUISITES. Please consult with the department.
    • An introduction to optimization (by Edwin K.P.Chong and Stanislaw H. Zak)
    • To be added.
  • LECTURES. MWF 9:00 -- 9:50am @ALP 3610.
  • DISCUSSION SECTION. TuTh 9:00 -- 9:50 @ ALP 3610.
  • INSTRUCTOR. Yimin Zhong,, Rowland Hall 510V.
  • TEACHING ASSISTANT. William Wood,, Rowland Hall 510T.
    • Lecturer: MWF 3:00-4:00 @ Rowland Hall 510V.
    • TA: TBA
  • GRADES. final project(30%), midterm (30%), regular projects (30%), homework (10%). 

          A+ ≥ 96.5 > A ≥ 93.5 > A- ≥ 90.0
          B+ ≥ 86.5 > B ≥ 83.5 > B- ≥ 80.0
          C+ ≥ 76.5 > C ≥ 73.5 > C- ≥ 70.0
          D+ ≥ 66.5 > D ≥ 63.5 > D- ≥ 60.0 > F

  • HOMEWORK. The homework exercises are mostly focused on the lectures. Proofs might be involved in homework and exam as well.
  • COLLABORATION. You are welcome to work in groups on projects, but you should write it up for your submission/exercises on your own. Code which is copied from either another student or from some other sources will NOT receive credit. On this and other matters, we will follow the university's academic honesty policy, which is linked at the end in the syllabus.
  • PROJECTS. There will be 3 or 4 regular projects and a final project in this course. The final project will be released around the midterm week. For all projects, I encourage the students to commit your work to places like Github or Bitbucket (or Gitlab). The language will be limited to Python. The submission will be just one link to your repository. So no uploading, and I can read the commit history from your repository. 
  • PROJECT REQUIREMENT. Your submission will be a link to your repository. Your repository must include:
    • A README file, describing your project in general, (suppose the user know nothing about your project) guide users to understand what your project is doing.
    • ALL necessary files to run the code.
  • EXAMS. There is 1 midterm and no final exam, closed book. The midterm  will be in-class 50 minutes long and non-cumulative. For the exam, no calculators or electronic devices are allowed. All calculations required will be simple enough so that you can do them by hand. The tentative dates for the exams are
    • Midterm: Wed. May 1. 9:00-9:50am @ALP 3610
    • Final project due: Wed. June 12 11:59PM.
  • EXAM MAKEUP POLICY. Requests for make-up midterm exam will be considered only under the following guidelines:
    • If the exam violates the student's religious creed.
    • If the exam time would result in loss of wages constituting financial hardship, need for child care resulting in financial hardship, inability to procure transportation or loss of employment.
    • If there is a verifiable emergency, which prevents the student from taking the final exam at the scheduled time (e.g., serious illness, death of immediate family member, or serious accident).
    • Python (Python 3 preferred). The easiest installation will be using anaconda.

Everyone in the classroom is expected to behave in a respectful and honest manner, both personally and academically. For a formal list of expected behavior and your rights at the university, please consult:

Course Summary:

Date Details