Topics include convexity analysis, nonlinear duality theory, Kuhn-Tucker conditions, algorithms for quadratic programming, separable programming: gradient and penalty methods.
Goals:
To learn both the theoretical and practical aspects of models and algorithms for nonlinear optimization problems. To provide a thorough grounding in the fundamental
mathematics that underpin the analysis and explore industrial applications of this important class of decision problems.
