An overview of practical optimization problems that can be posed as scheduling problems. Characterizations of optimal schedules. Simple and efficient combinatorial algorithms for easy problems. A brief overview of computational complexity, definition of P, NP, NP-Complete and NP-hard. Integer programming formulations, the Traveling Salesman Problem, heuristics, dynamic programming and branch-and-bound approaches. Polynomial-time approximation algorithms. [Offered: S] Prereq: MATH 229 or 239 or 249 and (One of CO 227, 250/350, 255/355, CO 352/CM 340); Not open to General Mathematics students
