EM 5020: Optimization

Basic Details:

Course Code: EM 5020
Credits: 3
Pre-requisites: None
Compulsory/Optional: Optional

Aim :

To introduce nonlinear programming methods and their application to solve engineering and real life problems.

Intended Learning Outcomes:

On successful completion of the course, the students should be able to;

ILO1: Apply and analyze optimization methods for functions of a single variable.
ILO2: Apply and analyze optimization methods for functions of several variables
with or without constraints.
ILO3: Formulate engineering problems as optimization problems and solve them by
adopting appropriate optimization algorithms.

Couse Content:

Theory of Optimization

Single variable, multivariable unconstrained and constrained, Lagrange multipliers, KKT conditions.

Single variable Optimization: Elimination methods, bracketing methods, interpolation
methods, root finding methods.
Multivariable Optimization (Unconstrained): Direct search methods, indirect search (descent)
methods.
Constrained Optimization: Direct search methods, indirect methods, penalty methods,
transformation methods, linearized methods.

Non-conventional Optimization Algorithms

Introduction to genetic algorithms, simulated annealing, particle swarm optimization algorithm, and ant algorithm.

Applications of Optimization

Mathematical modeling and design of, engineering and real-life systems, nonlinear curve fitting.

Time Allocation (Hours):

Lectures

Tutorials

Assignments

Practicals

Independent Learning & Assessment

Recommended Texts:

Singiresu S. Rao, Engineering Optimization, 4th edition, (2009), John Wiley & Sons Inc., NJ, USA.
Boyd, S.P. and Vandenberghe, L., (2004). Convex optimization. Cambridge university press.
Kalyanamoy Deb, Optimization for Engineering Design, (2005), Prentice Hall of India.

Assessment:

Continuous Assessment:

Assignments/Labs

10%