With the successful completion of this course, the students will have become familiar with Optimization techniques, which are applied in software and hardware problems in interactive systems.
Course Content (Syllabus)
Introduction to Optimization problems with motivations, examples and applications in Computer Science. Unconstrained optimization. Line search methods. Convex Optimization. Optimization with equality and inequality constraints. Evolutionary Optimization Techniques. Discrete Optimization. Applications to Optimizations in Software and Hardware—Techniques and designs for combinatorial testing.
Optimization Problems, Line-Search Methods, Constrained Optimization, Discrete Optimization, Optimizations in Software and Hardware, Combinatiorial Testing
Additional bibliography for study
 I. Griva, S. G. Nash, A. Sofer, Linear and Nonlinear Optimization, SIAM, 2009.
 R. Baldick, Applied Optimization, Cambridge University Press, 2006.
 C. T. Kelley, Iterative Methods for Optimization, Society for Industrial and Applied Mathematics (SIAM), 1999.
 J. Nocedal, S. J. Wright, Numerical Optimization, Springer, 2006.
 Γ. Α. Ροβιθάκης, Τεχνικές βελτιστοποίησης, Εκδόσεις Τζιόλα, 2007.
 Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014
 Steven Galbraith, Mathematics of Public Key Cryptography, 2012