Learning_Objectives_for_Prelim_1 2011

Learning_Objectives_for_Prelim_1 2011 - CEE 5290/CS...

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Unformatted text preview: CEE 5290/CS 5722/ORIE 5340 Heuristic Methods for Optimization Learning Objectives for Prelim on October 25, 2011 1 . Optimization and Heuristics: Definition of optimization problem, formulation, examples of application of optimization; Impact of Convexity or multimodality of an optimization problem; Heuristic methods – definition and characteristics, neighborhood, improvement strategies 2. Heuristic Algorithms In general for all algorithms covered in class, students are expected to know formulation of problem, selection of decision variables, pros and cons of different algorithms for binary and real decision variables, neighborhood definitions, algorithm parameters and selection, impact of algorithm parameters on algorithm performance, possible variations of algorithms The following are applicable to the specific algorithms: § Greedy Search – algorithm, neighborhood definitions, improvement strategy, variations § Simulated Annealing – algorithm; neighborhood definitions; selection of algorithm parameters -...
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This note was uploaded on 11/29/2011 for the course ORIE 5140 taught by Professor Shoemaker during the Fall '11 term at Cornell.

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