On Risk-Averse Weighted k-Club Problems
نویسندگان
چکیده
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 corresponding subgraphs. The selected representation of risk of a subgraph ensures that the optimal solutions are maximal k-clubs. A combinatorial branch-and-bound solution algorithm is proposed and solution performances are compared with an equivalent mathematical programming counterpart problem for instances with k = 2.
منابع مشابه
On risk-averse maximum weighted subgraph problems
In this work, we consider a class of risk-averse maximum weighted subgraph problems (RMWSP). 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 that is based on the formalism of modern...
متن کاملComparison of p300 in risk-seeker and risk-averse people during simple gambling task
Risk preference, the degree of tendency to take risk, has a fundamental role at individual and social health and is divided to risk seeker and risk averse. Therefore, the study of neural corelates of risk preferences is essential at the field of psychology and psychiatry. The current study aimed to examine and compare an ERP component named P300 between subjects with different risk preferences....
متن کاملIdentifying risk-averse low-diameter clusters in graphs with stochastic vertex weights
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 framework that is based on the formalism of...
متن کاملAlgorithms for Probabilistically-Constrained Models of Risk-Averse Stochastic Optimization with Black-Box Distributions
We consider various stochastic models that incorporate the notion of risk-averseness into the standard 2-stage recourse model, and develop novel techniques for solving the algorithmic problems arising in these models. A key notable feature of our work that distinguishes it from work in some other related models, such as the (standard) budget model and the (demand-) robust model, is that we obta...
متن کاملRisk premiums and certainty equivalents of loss-averse newsvendors of bounded utility
Loss-averse behavior makes the newsvendors avoid the losses more than seeking the probable gains as the losses have more psychological impact on the newsvendor than the gains. In economics and decision theory, the classical newsvendor models treat losses and gains equally likely, by disregarding the expected utility when the newsvendor is loss-averse. Moreover, the use of unbounded utility to m...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014