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CL 603: Optimization Y. Shastri

CL 603: Optimization

Instructor: Yogendra Shastri

Contact information:Room 311, Department of Chemical Engineering; Ph. 7203; E-mail:

yshastri@che.iitb.ac.in

Class meeting hours: Slot 3 (M 10:35-11:30 AM; T 11:35 AM-12:30 PM; Th 8:30-9:35 AM)

Location: 230 Chemical Engineering

Learning objectives:

At the completion of the course, the students should:

Acquire a clear understanding of the theory of optimization using mathematical basics Recall and compare different numerical techniques of optimization Solve simple optimization problems using hand calculations and computer programming Formulate optimization problems typically encountered in engineering applications Code the optimization model in a commercial software and solve it using appropriate solver Verify solution optimality and evaluate the performance of the solver

Course contents:

Introduction to optimization: Motivation and importance Problem formulation Basic Concepts of optimization: Convex and concave functions, degree of freedom analysis,

necessary and sufficient conditions Optimization of one-dimensional Functions

o Line search methods: Newton, steepest descent, quasi-Newton, conjugate directiono Trust region methods (brief overview)

Unconstrained multivariable optimization Constrained multivariable optimization Linear programming: Simplex method Nonlinear programming: Theory and application Integer programming: Theory and application Introduction to heuristic techniques and stochastic programming (if time permits)

Course evaluation:

The course evaluation will be done on a relative basis ("on the curve"). This means the student with

highest marks at the end of the semester will be awarded an AA, while the other students will be

graded relative to the top-ranked student. The grade distribution will depend on how close the

students are in terms of marks. The evaluation will be divided as follows:

Final (end-semester) examination: 30% Mid-semester examination: 20% Class project (individual or group, may involve presentation and report submission): 20% Assignments/Quiz: 30%

7/29/2019 CL 603 - Course Handout

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CL 603: Optimization Y. Shastri

Course policy:

80 % attendance rule will be enforced No formal class notes will be provided. If slides are used in the class, those will be made

available to you via Moodle.

I will extensively use Moodle to make course announcements and post material such as relevantpapers, assignments, and model answers.

Some assignments will require programming in relevant software, including MATLAB & SCILAB Submission of assignments must be done before the deadline. Late assignments will be graded

but will be penalized (25% of maximum points per day).

I will announce office hours for you to come and meet me regarding class issues, includingtechnical queries. Until then, please e-mail or call me to check my availability before coming.

Please do not sleep in the classroom!Plagiarism:

Plagiarism in class projects and assignments will be severely punished. Students found guilty of being

dishonest (both "recipient" and "provider") will be penalized, including an FR grade for appropriate

cases. The performance during the rest of the semester will not be taken into consideration while

deciding the punishment.

Suggested books:

T.F. Edgar, D.M. Himmelblau, and L.S. Lasdon. Optimization of chemical processes. McGraw Hill(2nd edition), 2001, ISBN 0-07-039359-1.

D.G. Luenberger. Linear and nonlinear programming. Kluwer Academic Publishers, 2008. ISBN:978-81-8128-934-6.

J. Nocedal and S.J. Wright. Numerical Optimization. Springer Verlag. ISBN:0-387-98793-2. G. Strang. Linear algebra and its application. Cengage Learning. ISBN: 978-81-315-0172-6. Any book on basics of calculus that you have previously used.You will also find a number of books on optimization in the library and many books are available

online with complete access. The subject matter will be same in all the books (with varying degrees

of focus on theory and applications). Therefore, you are free to use any book that you can find and

are comfortable with. I will use the books mentioned above as guidelines only and may use other

books at different times.

Please revise the basics of linear algebra, matrix algebra and calculus. These will be needed to

understand the theory of optimization.