We consider distributionally robust two-stage stochastic linear optimization problems with higher-order (say (Formula presented.) and even possibly irrational) moment constraints in their ambiguity sets. We suggest to solve the dual form of the problem by a semi-infinite programming approach, which deals with a much simpler reformulation than the conic optimization approach. Some preliminary numerical results are reported
Motivated by problems coming from planning and operational management in power generation companies,...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
We study a two-stage stochastic linear optimization problem where the recourse function is risk-aver...
We consider the two-stage stochastic linear programming model, in which the recourse function is a w...
We consider distributionally robust two-stage stochastic convex programming problems, in which the r...
Two-stage stochastic linear programming is a classical model in operations research. The usual appro...
Stochastic optimization, especially multistage models, is well known to be computationally excruciat...
Stochastic optimization, especially multistage models, is well known to be computationally excru-cia...
Thesis (Ph.D.), Department of Mathematics, Washington State UniversityStochastic semidefinite progra...
Stochastic optimization, especially multistage models, is well known to be computationally excruciat...
In this paper we present a stability analysis of a stochastic optimization problem with stochastic s...
In “Two-Stage Sample Robust Optimization,” Bertsimas, Shtern, and Sturt investigate a simple approx...
Stochastic optimization problems attempt to model uncertainty in the data by assuming that the input...
In this paper we present stability and sensitivity analysis of a stochastic optimization problem wit...
A wide variety of decision problems in engineering, science and economics involve uncertain paramete...
Motivated by problems coming from planning and operational management in power generation companies,...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
We study a two-stage stochastic linear optimization problem where the recourse function is risk-aver...
We consider the two-stage stochastic linear programming model, in which the recourse function is a w...
We consider distributionally robust two-stage stochastic convex programming problems, in which the r...
Two-stage stochastic linear programming is a classical model in operations research. The usual appro...
Stochastic optimization, especially multistage models, is well known to be computationally excruciat...
Stochastic optimization, especially multistage models, is well known to be computationally excru-cia...
Thesis (Ph.D.), Department of Mathematics, Washington State UniversityStochastic semidefinite progra...
Stochastic optimization, especially multistage models, is well known to be computationally excruciat...
In this paper we present a stability analysis of a stochastic optimization problem with stochastic s...
In “Two-Stage Sample Robust Optimization,” Bertsimas, Shtern, and Sturt investigate a simple approx...
Stochastic optimization problems attempt to model uncertainty in the data by assuming that the input...
In this paper we present stability and sensitivity analysis of a stochastic optimization problem wit...
A wide variety of decision problems in engineering, science and economics involve uncertain paramete...
Motivated by problems coming from planning and operational management in power generation companies,...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
We study a two-stage stochastic linear optimization problem where the recourse function is risk-aver...