Rrr 7 - 2013 Time Consistency versus Law Invariance in Multistage Stochastic Optimization with Coherent Risk Measures : Multilevel Optimization Modeling and Computational
نویسندگان
چکیده
A circulant is a Cayley graph over a cyclic group. A well-covered graph is a graph in which all maximal stable sets are of the same size α = α(G), or in other words, they are all maximum. A CIS graph is a graph in which every maximal stable set and every maximal clique intersect. It is not difficult to show that a circulant G is a CIS graph if and only if G and its complement G are both well-covered and the product α(G)α(G) is equal to the number of vertices. It is also easy to demonstrate that both families, the circulants and the CIS graphs, are closed with respect to the operations of taking the complement and lexicographic product. We study the structure of the CIS circulants. It is well-known that all P4-free graphs are CIS. In this paper, in addition to the simple family of the P4-free circulants, we construct a non-trivial sparse but infinite family of CIS circulants. We are not aware of any CIS circulant that could not be obtained from graphs in this family by the operations of taking the complement and lexicographic product.
منابع مشابه
Time Consistency Versus Law Invariance in Multistage Stochastic Optimization with Coherent Risk Measures: Multilevel Optimization Modeling and Computational Complexity
Coherent risk measures have become a popular tool for incorporating risk aversion into stochastic optimization models. For dynamic models in which uncertainly is resolved at more than one stage, however, use of coherent risk measures within a standard single-level optimization framework presents the modeler with an uncomfortable choice between two desirable model properties, time consistency an...
متن کاملMultilevel Optimization Modeling for Risk-Averse Stochastic Programming
Coherent risk measures have become a popular tool for incorporating risk aversion into stochastic optimization models. For dynamic models in which uncertainty is resolved at more than one stage, however, using coherent risk measures within a standard single-level optimization framework becomes problematic. To avoid severe time-consistency difficulties, the current state of the art is to employ ...
متن کاملTime-consistent approximations of risk-averse multistage stochastic optimization problems
OF THE DISSERTATION TIME-CONSISTENT APPROXIMATIONS OF RISK-AVERSE MULTISTAGE STOCHASTIC OPTIMIZATION PROBLEMS by Tsvetan Asamov Dissertation Director: Andrzej Ruszczyński In this work we study the concept of time consistency as it relates to multistage risk-averse stochastic optimization problems on finite scenario trees. We use dynamic time-consistent formulations to approximate problems havin...
متن کاملTime consistency of dynamic risk measures
In this paper we discuss time consistency of risk averse multistage stochastic programming problems. We show, in a framework of finite scenario trees, that composition of law invariant coherent risk measures can be law invariant only for the expectation or max-risk measures.
متن کاملStochastic programming approach to optimization under uncertainty
In this paper we discuss computational complexity and risk averse approaches to two and multistage stochastic programming problems. We argue that two stage (say linear) stochastic programming problems can be solved with a reasonable accuracy by Monte Carlo sampling techniques while there are indications that complexity of multistage programs grows fast with increase of the number of stages. We ...
متن کامل