Theory and practical algorithms for nonlinear continuous optimization. Fundamentals of unconstrained optimization: conjugate gradient methods and Newton-type methods. Nonlinear least squares problems. Fundamentals of constrained optimization: optimality conditions, quadratic programming, penalty and barrier methods, interior-point methods, sequential quadratic programming. Prereq: (CO 367/CM 442 and one of CO 250/350, 352 or CM 340) or CO 255/355; Cumulative overall average of at least 80%; Not open to General Mathematics students
