Upcoming Events

 

ISE Seminar Series Spring 2022

 

Time: Tuesday April 19, 2022 11:00 a.m. - 12:00 p.m.

Speaker: Ruiwei Jiang, University of Michigan

TitleConvex Chance-Constrained Programs with Wasserstein Ambiguity

Location: Mohler Laboratory, Room 453 

 

Abstract:

Chance constraints yield non-convex feasible regions in general. In particular, when the uncertain parameters are modeled by a Wasserstein ball, prior studies showed that the distributionally robust (pessimistic) chance constraint admits a mixed-integer representation. This study identifies sufficient conditions that lead to *convex* feasible regions of chance constraints with Wasserstein ambiguity. First, when uncertainty arises from the left-hand side of a pessimistic individual chance constraint, we derive a convex and conic representation if the Wasserstein ball is centered around a radial and symmetric distribution (e.g., Gaussian). Second, when uncertainty arises from the right-hand side of a pessimistic joint chance constraint, we show that the ensuing feasible region is convex if the Wasserstein ball is centered around a log-concave distribution. In addition, we propose a block coordinate ascent algorithm for this class of chance constraints and prove its convergence to global optimum. Furthermore, we extend these convexity results and conic representation to optimistic chance constraints. This is a joint work with Haoming Shen (IOE, University of Michigan).

Bio:

Ruiwei Jiang is an Associate Professor of Industrial & Operations Engineering at the University of Michigan. He conducts research on stochastic optimization, robust optimization, and integer programming. Ruiwei’s research has been recognized with an NSF Career Award, two INFORMS Junior Faculty Interest Group paper prizes, and an INFORMS George Nicholson student paper award.

 

 

 

 

 

 

 

 

 

 

 

 

 


Past Events

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Tuesday, April 5, 2022, 11:00 a.m.
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Tuesday, March 22, 2022, 11:00 a.m.
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Tuesday, March 1, 2022, 11:00 am
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Tuesday, February 15, 2022, 11:00 am
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