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In this work, we consider a class of risk-averse maximum weighted subgraph problems (R-MWSP). Namely, assuming that each vertex of the graph is associated with a stochastic weight, such that the joint distribution is known, the goal is to obtain a subgraph of minimum risk satisfying a given hereditary property. We employ a stochastic programming framework… (More)

We present an efficient scenario decomposition algorithm for solving large-scale convex stochas-tic programming problems that involve a particular class of downside risk measures. The considered risk functionals encompass coherent and convex measures of risk that can be represented as an infimal convolution of a convex certainty equivalent, and include… (More)

We propose a two-stage stochastic programming framework for designing or identifying " resilient " , or " repairable " structures in graphs whose topology may undergo a stochastic transformation. The repairability of a subgraph satisfying a given property is defined in terms of a budget constraint, which allows for a prescribed number of vertices to be… (More)

- Bo Sun, Pavlo Krokhmal, BO SUN, Andrew Kusiak, Amaury Lendasse, Raghuraman Mudumbai +8 others
- 2016

Sun, Bo. "Risk-averse design and operation of renewable energy power grids." PhD (Doctor of Philosophy) thesis, To my parents ii ACKNOWLEDGEMENTS I would like to express my special appreciation and thanks to my advisor, Professor Pavlo Krokhmal, for his guidance and support during my Ph.D. studies. He patiently provided excellent instructions, insightful… (More)

- Maciej Rysz, Foad Mahdavi, Pajouh Pavlo Krokhmal, Eduardo L. Pasiliao
- 2015

In this work, we study the problem of detecting risk-averse low-diameter clusters, modeled as k-clubs, in graphs. It is assumed that the uncertainty of the information associated with vertices is shown by stochastic weights, whose joint distribution is known. The goal is to find a k-club of minimum risk contained in the graph. A stochastic programming… (More)

In this work, we consider a risk-averse maximum weighted k-club problems. It is assumed that vertices of the graph have stochastic weights whose joint distribution is known. The goal is to find the k-club of minimum risk contained in the graph. A stochastic programming framework that is based on the formalism of coherent risk measures is used to find the… (More)

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