Algorithm Design and Optimization 1

Algorithm Design and Optimization 1
A treatment of algorithms used to solve these problems. Topics include complexity and data, approximation theory, recursive algorithms, linear optimization, unconstrained optimization, constrained optimization, global optimization.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesMATH 290 & MATH 313 & MATH 314; concurrent enrollment in Math 321, 334, 344.
 ProgramsContaining MATH 320
Course Outcomes

Introduction to Algorithms and Approximation

Coverage of the fundamentals of algorithm analysis including, convergence, stability, mathematics for algorithm analysis, data structures, probability, and introductory statistics. Discrete optimization and algorithms employing stochastic guessing are investigated. Additionally, students will learn about approximation methods including Fourier series and wavelets. For detailed information about desired learning outcomes visit the Math 320 Wiki page.