The course content consists of three main parts:
+ Fundamental mathematical knowledge for nonlinear optimization: linear algebra, basic calculus, and convex analysis.
+ Optimization conditions and methods used to solve various nonlinear optimization problems, including unconstrained problems, problems with convex constraint sets, linearly constrained problems, and problems with equality and/or inequality constraints.
+ Selected MATLAB/Python applications for implementing and experimenting with the algorithms.