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Fall 2019
Dec 15, 2019
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CSCI 2950E - Stochastic Optimization
This advanced graduate course/seminar will focus on optimization under uncertainty, or optimization problems where some of the constrains include random (stochastic) components. Most practical optimization problems are stochastic (subject to future market conditions, weather, faults, etc.), and there has been substantial research (both theoretical and experimental) in efficient solution for such problems. We'll read and discuss some of the recent works in this area.
1.000 Credit hours
1.000 Lecture hours

Levels: Graduate, Undergraduate
Schedule Types: Primary Meeting

Computer Science Department

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