The course cover theory and algorithms for nonlinear continuous optimization. We start with some examples of applications of nonlinear programming. Then we proceed with characterization of necessary and sufficient conditions for and optimum of unconstrained and constrained optimization problems, the KKT conditions, and specific results that hold under convexity assumptions. We then study local and global convergence theory with application to specific algorithms and explore the impact of structural properties of the objective function on algorithms' performance.
