Real-Time and Embedded Computing Systems and Applications by Jing Chen, Seongsoo Hong

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By Jing Chen, Seongsoo Hong

This Presentation: Transitions from non-stop to Discrete1. classes from winning algorithms• Convexity and Decomposition• detailed constitution• Sampling• Inexact “solves”2. “Informal” exploration of demanding situations in multi-stage difficulties

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58 Thank you for your interest. Comments and Questions, Most Welcome! 59 In appreciation of the SP community ... 60 Top 5 reasons to work on Stochastic Programming Problems 5. Can work with “cosmic distances” without leaving home! 4. One begins to easily distinguish musicians from mathematicians: one composes; the other “decomposes” 61 3. One learns that “Log-concavity” has nothing in common with either lumber or cavities! 2. One also learns that “clairvoyance” requires connections in very high places!

Sampling is new to SIP, but will emerge as we solve larger problems 54 Important Trends which should continue ... • Algorithmic approach to tree generation and output analysis • Computer implementations should find easier interfaces with simulation/validation software 55 For SP, algorithms if there is one word that deserves its own slide it is ... 56 Scalability Scalability Scalability Scalability Scalability 57 And finally, Two Stage and Multi-stage Stochastic Integer Programming Problems Remain One of the Grand Challenges in Optimization....

Comments and Questions, Most Welcome! 59 In appreciation of the SP community ... 60 Top 5 reasons to work on Stochastic Programming Problems 5. Can work with “cosmic distances” without leaving home! 4. One begins to easily distinguish musicians from mathematicians: one composes; the other “decomposes” 61 3. One learns that “Log-concavity” has nothing in common with either lumber or cavities! 2. One also learns that “clairvoyance” requires connections in very high places! 1. The word “non-anticipativity” makes you appreciate what President Bush must go through!

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