Graduate Studies Reports Access
Graduate Course Proposal Form Submission Detail - ESI6447
Tracking Number - 2471
Edit function not enabled for this course.
Approved, Permanent Archive - 2011-07-17
Submission Type: New
Course Change Information (for course changes only):
Comments: COEN app 5/10/11; to GC 5/10/11; why Is title variable? Emailed 6/28/11. cleared 6/29/11; GC approved 7/5/11. To USF Syst 7/5/11; to SCNS 7/13/11. Appd Eff 8/1/11
- Date & Time Submitted: 2011-02-17
- Department: Industrial and Management Systems Engineering
- College: EN
- Budget Account Number:
- Contact Person: Bo Zeng
- Phone: 9745588
- Email: firstname.lastname@example.org
- Prefix: ESI
- Number: 6447
- Full Title: Large-scale and Computational Optimization
- Credit Hours: 3
- Section Type: C -
Class Lecture (Primarily)
- Is the course title variable?: N
- Is a permit required for registration?: Y
- Are the credit hours variable?: N
- Is this course repeatable?:
- If repeatable, how many times?: 0
- Abbreviated Title (30 characters maximum): Large-scale Optimization
- Course Online?: C -
Face-to-face (0% online)
- Percentage Online: 0
- Grading Option:
R - Regular
- Prerequisites: ESI 6491
- Course Description: Efficient algorithm development for large-scale and computationally intensive optimization problems. Specific topics include Lagrangian relaxation, Benders' decomposition, column generation and primal-dual approximation algorithms.
- Please briefly explain why it is necessary and/or desirable to add this course: Replacing Selected Topics with Permanent number; already listed in program
- What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service? It was started to taught in Spring 2010. 11 doctoral students were enrolled in this course. It is anticipated that the demand will be kept or be higher given the current national trend in "Operations Research", one of the core area in Industrial Engineering, as well as the computational demand in bio-informatics, power systems and transportation systems.
- Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? Yes, 1 time
- What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.)
- Objectives: Developing analytical mathematical programming models for designing and operating large-scale systems, such as those from logistic, transportation, power, telecommunication systems as well as portfolio optimization.
Understand the computational complexity theory and concepts of algorithm design
Master typical algorithms for those challenging problems, such as various decomposition algorithms, Lagrangian Relaxation method, and several approximation algorithms.
Be able to apply methods and skills to solve practical system design and operation issues using popular software and commercial solvers
- Learning Outcomes: Strong modeling ability for practical systems
Efficient solution algorithm development for typical models
Insights into the computational complexity and approximation strategies
- Major Topics: Lagrangian relaxation/decomposition
Benders decomposition and L-shape method in stochastic programming
Dantzig-Wolfe decomposition/ column generation
- Textbooks: Large Scale Linear and Integer Optimization-by R. Kipp Martin
Integer Programming- by L. Wolsey
- Course Readings, Online Resources, and Other Purchases:
- Student Expectations/Requirements and Grading Policy: 3-4 homework assignments and one course project.
- Assignments, Exams and Tests: 3-4 homework assignments and one course project.
- Attendance Policy: Course Attendance at First Class Meeting Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation Registration - 4.0101,
Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (http://usfweb2.usf.edu/usfgc/gc_pp/acadaf/gc10-045.htm)
In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. Its the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.
- Policy on Make-up Work:
- Program This Course Supports: Department of Industrial and Management Systems Engineering
- Course Concurrence Information: 1, Computer Science and Engineering
2, Electric Engineering
3, Civil and Environmental Engineering
4, Chemical Engineering