Course overview
Main topics
- classification of problems: linear/nonlinear, continuous/discrete, convex/nonconvex, constrained/unconstrained, smooth/non-smooth, global/local, deterministic/stochastic
- unconstrained optimization: gradient descent, Newton's method, line search algorithms, variations of Newton's method
- constrained optimization: active set, feasible directions, descent directions, optimality conditions
- linear programming: generic/general/standard form, geometric interpretations, basic feasible solutions, degeneracy, simplex methods