Continuous Optimization

Element 1 (4.5 hp) Theory This part contains theory for nonlinear optimization. The course starts with a discussion of basic notions like classification of optimization problems, objective functions, constraints, feasible solutions, optimal solutions. Then fundamental convexity theory is addressed. A general optimization algorithm is defined, and notions like convergence rate, line search, descent- and ascent directions and optimality conditions are discussed. Optimality conditions for free optimization problems are introduced, and Newton's method for free optimization problems is studied. Next, optimization problems with constraints, Lagrange functions and Lagrange multipliers are defined, and optimility conditions (The Karush-Kuhn-Tucker conditions) are introduced. The duality concept and weak and strong duality theorems are discussed. Finally, some mathematical modeling examples leading to optimization problems are discussed. Element 2 (3 hp) Computer labs This part contains implementation of some approximation methods for free optimization problems (steepest descent, quasi-Newton method), and constrained optimization problmes (feasible-point methods, the SQP method). Furthermore, one assignment includes both mathematical modeling, formulation of an optimization model and finding an approximate solution of the model with suitable software.