FACULTY OF ENGINEERING

Department of Computer Engineering

CE 485 | Course Introduction and Application Information

Course Name
Linear and Integer Programming
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 485
Fall/Spring
3
0
3
8

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course Problem Solving
Lecture / Presentation
Course Coordinator
Course Lecturer(s) -
Assistant(s) -
Course Objectives The primary objective is to develop both an understanding of the formulation techniques, and the algorithms used to solve the class of optimization problems that lend themselves to linear and integer linear programming.
Learning Outcomes The students who succeeded in this course;
  • create LP formulations to appropriate problems.
  • apply simplex and dual simplex methods.
  • express the run-time complexity of various algorithms for solving LP problems.
  • formulate Integer LP (ILP) formulations for various combinatorial problems.
  • employ techniques for relaxation of ILP to LP formulations.
Course Description LP Standard Form, Extreme Points and Basic Solutions, Rudimentary Simplex Algorithm, Interior Point Strategies for LP, Formulating Duals, Primal-to-Dual Relationships, LP-Based Branch and Bound, and Rounding.

 



Course Category

Core Courses
Major Area Courses
X
Supportive Courses
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Nature of Linear Programs Section 2.4
2 Formulation of Classic LP Model Types Chapter 4
3 LP Standard Form, Extreme Points and Basic Solutions, Rudimentary Simplex Algorithm Section 5.1, Section 5.2, Section 5.3
4 Two Phase Simplex, Degeneracy, Cycling and Finiteness of Simplex Section 5.5, Sections 5.6, Section 5.7
5 Revised Simplex, Lower- and Upper-Bounded Simplex Section 5.8, Section 5.9
6 Interior Point Strategies for LP, Affine Scaling of Solutions, Affine Scaling Search Section 6.1, Section 6.2, Section 6.3
7 Log Barrier Methods for LP, Primal-Dual Search Section 6.4, Section 6.5
8 Midterm
9 Activities vs. Resources, Qualititative Sensitivity Sections 7.1-7.2
10 Quantitative Sensitivity and Duality, Formulating Duals, Primal-to-Dual Relationships Section 7.3, Section 7.4, Section 7.5
11 Solving by Total Enumeration, Elementary Relaxations, Strengthening LP Relaxations Section 12.1, Section 12.2, Section 12.3
12 LP-Based Branch and Bound Section 12.4
13 Rounding, Parent Bounds, Enumeration Sequences and Stopping Early in Branch and Bound Section 12.5
14 Improving Heuristics for Discrete Optimization, Tabu, Simulated Annealing, Genetic Algorithms, Constructive Heuristics for Discrete Optimization Section 12.6, Section 12.7, Section 12.8
15 Review of semester
16 Final Exam

 

Course Notes/Textbooks Optimization in Operations Research, Ronald L. Rardin, Prentice Hall, ISBN-10: 0023984155 • ISBN-13: 9780023984150, 1998.
Suggested Readings/Materials

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
1
30
Seminar / Workshop
Oral Exams
Midterm
1
30
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
2
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
14
6
84
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
0
Presentation / Jury
0
Project
1
60
60
Seminar / Workshop
0
Oral Exam
0
Midterms
1
16
16
Final Exam
1
32
32
    Total
240

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science and Computer Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.

X
2

To be able to identify, define, formulate, and solve complex Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.

X
4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer Engineering applications; to be able to use information technologies effectively.

X
5

To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Computer Engineering research topics.

6

To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.

8

To have knowledge about global and social impact of Computer Engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Computer Engineering solutions.

9

To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.

10

To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)

12

To be able to speak a second foreign language at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Computer Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

 


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