MA632
Optimization
0.5 Credit

Fundamental concepts of linear and non-linear optimization theory for developing algorithms and models are discussed. Topics include: duality and problem structure for finding, recognizing and interpreting solutions; network optimization problems; problems with integer constraints; combinatorial optimization problems; the simplex algorithm for linear programming; linear programming duality and complementary slackness; the network simplex algorithm; Newton and gradient methods for unconstrained optimization; Lagrange multipliers; penalty and barrier methods for constrained optimization; and an introduction to interior-point methods for linear and convex programming. Search techniques for hard problems may be included. The output of appropriate computer packages is analyzed.