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Equistable DistanceHereditary Graphs
"... A graph is called equistable when there is a nonnegative weight function on its vertices such that a set S of vertices has total weight 1 if and only if S is maximal stable. We show that a necessary condition for a graph to be equistable is su#cient when the graph in question is distanceheredit ..."
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A graph is called equistable when there is a nonnegative weight function on its vertices such that a set S of vertices has total weight 1 if and only if S is maximal stable. We show that a necessary condition for a graph to be equistable is su#cient when the graph in question is distance
EQUISTABLE CHORDAL GRAPHS
"... Abstract. A graph is called equistable when there is a nonnegative weight function on its vertices such that a set S of vertices has total weight 1 if and only if S is maximal stable. We show that a chordal graphs is equistable if and only if every two adjacent nonsimplicial vertices have a common ..."
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Abstract. A graph is called equistable when there is a nonnegative weight function on its vertices such that a set S of vertices has total weight 1 if and only if S is maximal stable. We show that a chordal graphs is equistable if and only if every two adjacent nonsimplicial vertices have a
Research Interests: Summary
, 2007
"... My current research interests are in structural and algorithmic graph theory – a field connecting pure mathematics with theoretical computer science. I am particularly interested in studying ways to cope with (hard) optimization problems on graphs. In these problems, it is often desirable to find a ..."
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My current research interests are in structural and algorithmic graph theory – a field connecting pure mathematics with theoretical computer science. I am particularly interested in studying ways to cope with (hard) optimization problems on graphs. In these problems, it is often desirable to find a
Keynote Talk 1 Stream: Keynote Speakers Invited session
"... When optimizing under stochastic uncertainty, the entity of primary importance is a chance constraint Prob qsi>P f(x;qsi) in Q> = 1 epsilon, for all P in PP where x is the decision vector, qsi is a random perturbation with distribution P known to belong to a given family PP, Q is a given tar ..."
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When optimizing under stochastic uncertainty, the entity of primary importance is a chance constraint Prob qsi>P f(x;qsi) in Q> = 1 epsilon, for all P in PP where x is the decision vector, qsi is a random perturbation with distribution P known to belong to a given family PP, Q is a given target set, and epsilon « 1 is a given tolerance. Aside of a handful of special cases, chance constrains are computationally intractable: first, it is difficult to check efficiently whether the constraint is satisfied at a given x, and second, the feasible set of a chance constraint typically is nonconvex, which makes it problematic to optimize under the constraint. Given these difficulties, a natural way to process a chance constraint is to replace it with its safe tractable approximation a tractable convex constraint with the feasible set contained in the one of the chance constraint. In the talk, we overview some recent results in this direction, with emphasis on chance versions of wellstructured convex constraints (primarily, affinely perturbed scalar linear and linear matrix inequalities) and establish links between this topic and Robust Optimization. � MA02
gramming in the Enterprisewide Optimization of Process Industries
"... Enterprisewide optimization (EWO) is a new emerging area that lies at the interface of chemical engineering and operations research, and has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the ..."
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Enterprisewide optimization (EWO) is a new emerging area that lies at the interface of chemical engineering and operations research, and has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the operations of supply, production and distribution activities of a company to reduce costs and inventories. A major focus in EWO is the optimization of manufacturing plants as part of the overall optimization of the supply chain. Major operational items include production planning, scheduling, and control. This talk provides an overview of major modeling and computational challenges in the development of deterministic and stochastic linear/nonlinear mixedinteger optimization models for planning and scheduling for the optimization of plants and entire supply chains that are involved in EWO problems. We illustrate the application of these ideas in four major problems: a) integration of planning and scheduling in batch processes