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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)

- Maciej Rysz, Alexander Vinel, Pavlo A Krokhmal, Eduardo L Pasiliao
- 2015

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)

- 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)

- Maciej Rysz, Pavlo Krokhmal, Eduardo L Pasiliao
- 2015

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)

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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