This is a graduate course in engineering optimization. We will study Optimization techniques with applications in various aspects of engineering design, which will include: Concepts of design variables, constraints, objective functions, penalty functions, Lagrange multipliers. Techniques for solving constrained and unconstrained optimization problems: classical approaches, steepest descent, conjugate directions, conjugate gradient, controlled random searches, etc. Discussion of generalized reduced gradient, sequential linear programming, and recursive quadratic programming strategies and augmented lagrange Lagrange multiplier method. Matalb based computer implementation (programming) of optimization schemes. Applications and examples in the design of engineering components and systems. You are expected to have access to and a working knowledge of MATLAB.
Required Text:
. Applied Optimization with Matlab Programming, P. Venkataraman, 2002, Wiley,ISBN 0-471-34958-5.
Selected References:
1. Engineering Optimization: Theory & Practice, Rao, S., 1996, Wiley.
2.Introduction to Matlab 7 for Engineers, Palm, 2001, McGraw Hill, ISBN 0-07-292242-7.
Prerequisite:
MER202 (Engineering analysis) or equivalent.
Matlab Knowledge (any level)
Grading:
Grading will be based on 10 graded homework assignments.
Homework 100%
All of the work required for this course must be completed to pass this course.
Tentative Course Outline: Fall 2004
Period
1 Introduction
2 Graphical Optimization
3 Linear Programming
4 Nonlinear Programming
5 Numerical Techniques
6 Unconstrained Optiminzation
7 Constrained Optimization
8 Discrete Optimization
9 Global Optimization
