Difference between revisions of "Math 322: Algorithm Design and Optimization 2"
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m (moved Math 322 to Math 322: Computation & Optimization 2) 
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Revision as of 16:19, 3 April 2013
Contents
Catalog Information
Title
Computation and Optimization 2
(Credit Hours:Lecture Hours:Lab Hours)
(3:3:0)
Offered
W
Prerequisite
Math 320; concurrent with Math 346, Math 323
Description
Algorithms used to solve dynamic programming problems and advanced computing problems. Topics include finitehorizon and infinitehorizon dynamic programming, discrete transforms, compressed sensing, heuristics, branch and bound, conditioning and stability.
Desired Learning Outcomes
Prerequisites
Math 320; concurrent with Math 346, Math 323
Minimal learning outcomes
Students will have a solid understanding of the concepts listed below. They will be able to prove many of the theorems that are central to this material. They will understand the model specifications for the algorithms, and be able to recognize whether they apply in the context of a given application or not. They will be able to perform the relevant computations on small, simple problems. They will be able to describe the algorithms well enough that they could program simple versions of them, and will have a basic knowledge of the computational strengths and weaknesses of the algorithms covered.
 Dynamic Programming
 FiniteHorizon Problems
 InfiniteHorizon Problems
 Uncertain Stopping Times
 Value and Policy Iteration
 Numerical Techniques
 Applications
 Discrete Transforms
 zTransforms
 Discrete Cosine Transform
 Fast Fourier Transform (FFT) (including convolutions)
 ShannonNyquist Theorem, including Gibbs Phenomenon
 Discrete Wavelet Transforms
 Advanced Algorithms
 Compressed Sensing
 Heuristics, Branch and Bound
 Conditioning and Stability
 Conditioning
 Forward Stability
 Backward Stability
Textbooks
Possible textbooks for this course include (but are not limited to):