Algorithm Design and Optimization 2

Algorithm Design and Optimization 2
Algorithms used to solve dynamic programming problems and advanced computing problems. Topics include finite-horizon and infinite-horizon dynamic programming, discrete transforms, compressed sensing, heuristics, branch and bound, conditioning and stability.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesMATH 320; concurrent enrollment in Math 323, 346.
 ProgramsContaining MATH 322
Course Outcomes

Algorithms for approximation and optimization

Algorithms for polynomial approximation and interpolation will be presented. Algorithms for optimization will be covered including those for unconstrained optimization, linear optimization, nonlinear constrained optimization, and convex optimization. Dynamic optimization and stochastic problems will also be covered. For a detailed description of desired learning outcomes visit the Math 322 Wiki page.