Review of classical optimization. NP-hardness and using NP-hardness to analyse discrete optimization problems. Design and analysis of algorithms for easy and hard problems, including dynamic programming with pseudo-polynomial complexity, simulated annealing, fully polynomial approximation schemes, genetic algorithms, and heuristics.