OR/ISE/MA505 Syllabus

OR/ISE/MA 505: Syllabus

Fall 2025

Time

Tu, Th 11:45 AM – 1:00 PM

* DELTA Classroom System will automatically record our lectures at the published class starting and ending times and on the published class days. Registered students can view the recorded lectures in our 505 classroom content folder at: <https://ncsu.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx#folderID=%22e5898091-78e1-4227-9c88-afee012e23c8%22>

* DELTA also provides a synchronized video service with a 40-second delay at the same link to enable you attending the class online.

* If a student opens the course folder before the start time of the webcast, then they will need to refresh their browser and select the title of the webcast. The webcast will then open in a new tab. Because of the 40 second delay, the following message will appear: “This page will update once the webcast begins”. After 40 seconds the webcast will begin.
* If a student opens an individual webcast link and attempts to view it before the webcast start time then the following message will appear: “This page will update once the webcast begins”. After 40 seconds the webcast will begin.

Room

2341 Fitts Woolard Hall

Instructor

Professor S.-C. Fang (fang@ncsu.edu)

  • Office Hours
    Tu, Th 2:00 – 3:00 PM (or by appointment)

Teaching Assistant

Prerequisite

  • Matrix Theory
  • Linear Algebra
  • OR 501 – Introduction to Operations Research (or equivalent)

Related Courses

  • OR 501 – Introduction to Operations Research
  • OR/MA 504 – Introduction to Mathematical Programming
  • OR 705 – Large Scale Linear Programming Systems
  • OR/MA/ST 706 – Nonlinear Programming
  • IE/OR/MA 766 – Network Flows
  • ISE789/OR791 – Support Vector Machines and Neural Networks
  • ISE789/OR791 – Optimization Models for Machine Learning
  • ISE589/OR591 – Introduction to Optimization and Machine Learning for Systems Analytics

Course Objectives

ISE/OR/MA 505 is a course that provides the fundamental understanding to the theory and algorithms of linear optimization. It involves mathematical analysis, theorem proving, algorithm design and numerical methods. It intends to lead students into more advanced optimization theory and applications. It is also a preparatory course for ISE/OR students to take their PhD Qualifying Exams on this subject.

 

Course Contents

  • Introduction to LP
  • Geometric Interpretation of LP
  • The Simplex Method
  • Duality and Sensitivity Analysis
  • Interior Point Methods
  • Robust Optimization and Machine Learning

 

Textbook

Linear Optimization and Extensions: Theory and Algorithms (S-C Fang and Puthenpura), Prentice Hall 1993. ISBN-13‎ 978-0139152658

 

Grading

  • Homeworks – 20%
  • First Exam – 25%
  • Second Exam – 25%
  • Final Exam – 30%

Evaluation Standard

  • A – 88 and above
  • B – 75 to 87
  • C – 60 to 74
  • Fail – under 60

Homework

  • About 10 homework assignments
  • TA will hold a recitation for each homework.
  • Rule 1: No late homework without TA’s approval
  • Rule 2: Convince TA for any grade changes.

Exams

  • Rule 3: No make-up exam without instructor’s pre-approval or “doctor’s notes” from hospital.
  • First Exam: Up to the Simplex Method, closed book exam.
  • Second Exam: Up to Duality and Sensitivity, closed book exam with one 4×6 index card.
  • Final Exam: Comprehensive with emphasis on the Interior Point Methods, closed book with one sheet of 8×11 paper.

End-of-Semester Class Evaluation

Online class evaluations will be available for students to complete during the last two weeks of class. Students will receive an email message directing them to a website where they can login using their Unity ID and complete evaluations. All evaluations are confidential; instructors will never know how any one student responded to any question, and students will never know the ratings for any particular instructors.

Evaluation Website: https://classeval.ncsu.edu

Student help desk: classeval@ncsu.edu

More information about ClassEval: http://www2.acs.ncsu.edu/UPA/classeval/index.htm

Academic Integrity

A student is expected to know what constitutes academic misconduct found in the Code of Student Conduct Policy ( POL11.35.1) , and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor.

Students with Disabilities

North Carolina State University retains authority, through the Disability Services Office (located in Student Health Services Building, Suite 2221), in determining appropriate accommodations after giving consideration to the preferences of the student, the documentation provided, and institutional expertise in working with students with disabilities. If you require academic accommodations to lessen the impact of your disability, please register with the Disability Services Office at the beginning of each academic term.

 

Need Help?

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