Syllabus Application
IE 501
Linear Programming and Extensions
Faculty
Faculty of Engineering and Natural Sciences
Semester
Fall 2026-2027
Course
IE 501 -
Linear Programming and Extensions
Time/Place
Time
Week Day
Place
Date
14:40-15:30
Tue
FENS-L067
Sep 28-Dec 31, 2026
10:40-12:30
Wed
FENS-L067
Sep 28-Dec 31, 2026
Level of course
Masters
Course Credits
SU Credit:3, ECTS:10
Prerequisites
-
Corequisites
-
Course Type
Lecture
Instructor(s) Information
Burak Kocuk
- Email: burakkocuk@sabanciuniv.edu
Course Information
Catalog Course Description
Theory of linear programming; convexity; simplex and algorithmic aspects; duality and sensitivity; computational issues; decomposition and column generation; introduction to integer and nonlinear programming.
Learning Outcomes:
| 1. | to identify, model, and solve linear programming problems |
|---|---|
| 2. | to describe the connection of the structure of linear programming problems to linear algebra and polyhedral theory |
| 3. | to leverage on LO-2 in order to design and implement an efficient solution method for linear programming |
| 4. | to describe the relationship between primal and dual linear programming problems and exploit it in sensitivity analysis |
| 5. | to describe the main difficulties associated with solving large-scale linear programming problems and overcome them by employing decomposition methods |
Course Objective
This course focuses on the theory of linear programming (LP) although we will discuss some practical implementation issues as well. In the first part of the course, we will review LP modeling, convexity, some essential linear algebra, certain aspects of polyhedral theory, and then present the foundations of linear programming. In the second part of the course, we will discuss the simplex method for solving linear programs and its algorithmic aspects, some related computational issues, duality and sensitivity. Finally, we will cover Dantzig-Wolfe decomposition and column generation for solving large-scale linear programs.
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Course Materials
Resources:
Introduction to Linear Optimization, A. Nemirovski (Lecture Notes) http://www.isye.gatech.edu/~nemirovs/
Linear Programming and Network Flows, M. S. Bazaraa, J. J. Jarvis and H. D. Sherali (John Wiley).
Introduction to Linear Optimization, D. Bertsimas and J. N. Tsitsiklis (Athena Scientific).
Lectures on Modern Convex Optimization, A. Ben-Tal and A. Nemirovski (SIAM).
Linear Algebra and Its Applications, G. Strang (Harcourt, Brace, Jovanovich, Publishers).
Linear Programming and Network Flows, M. S. Bazaraa, J. J. Jarvis and H. D. Sherali (John Wiley).
Introduction to Linear Optimization, D. Bertsimas and J. N. Tsitsiklis (Athena Scientific).
Lectures on Modern Convex Optimization, A. Ben-Tal and A. Nemirovski (SIAM).
Linear Algebra and Its Applications, G. Strang (Harcourt, Brace, Jovanovich, Publishers).
Technology Requirements:
You will need to model, implement and solve linear programs in some homework questions. Although you are free to use any solver or language, you are strongly suggested to download and use the Gurobi solver with Python interface. A step-by-step installation guide is uploaded to Sucourse.