Lecture14

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Unformatted text preview: IE 495 – Lecture 13 Bounds in Stochastic Programming Prof. Jeff Linderoth March 3, 2003 March 3, 2003 Stochastic Programming – Lecture 14 Slide 1 Outline • Review • Bounds Distribution Problem Numerical Integration Lower Bound—Jensen's inequality Tightening the Lower Bound A numerical example March 3, 2003 Stochastic Programming – Lecture 14 Slide 2 Review Homework not due until Wed. No homework assignment until after break. You're welcome! (especially those of you taking your qualifying exam). • Who wants to use high-performance computing? • What is an “SMP” machine? • What is Condor? • Where is the fastest computer in the world located? March 3, 2003 Stochastic Programming – Lecture 14 Slide 3 Bounds • Think of the case in which we are trying to solve a stochastic program containing random variables that are drawn from a continuous distribution. min Q(x) ≡ Eω Q(x, ω ) = Q(x, ω )dF (ω ) Ω • Keep in mind that Ω is one of those fancy Lebesgue-Stieltjes integrals, so it...
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  • Spring '08
  • Linderoth
  • Optimization, stochastic programming

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