Course: MAT 453 3.0 Optimization (Compulsory)
Course content: Introduction to Optimization Problem, Unconstrained Optimization Via Calculus, Optimization and Convexity, Optimization in Practice, Newton’s Method, The Method of Steepest Decent, Beyond Steepest Decent, Least Square Fit, Subspaces and Projections, Minimum Norm Solutions of Underdetermined Linear Systems, Convex programming; The Karush-Kuhn-Tucker Theorem, Karush-Kuhn-Tucker Theorem and Constrained Geometric Programming, Dual Convex Programming, History of Optimization- Classical Problems, Euler- Lagrange equation, Solutions of Classical Problems using Euler- Lagrange Equation.
Recommended Readings:
The Mathematics of Nonlinear Programming – A.L. Peressini, F.E. Sullivan, J.J. Uhl, Reliability: Modeling, Prediction and Optimization – W.R. Blischke, D.N.P. Murthy, The Mathematics of Nonlinear Programming (Springer-Verlag, 1998) A.L. Peressini, F.E. Sullivan, J.J