The paper deals with two methods of solving optimization programs where uncertainties occur: stochastic (in particular chance-constrained) programming and robust programming. We review briefly how these two methods deal with uncertainty and what approximations are commonly used. Furthermore, we are concentrated on approximations based on sample sets where some type of weak dependence occurs. We demonstrate that such kind of dependence does not imply any important malfunction of optimization methods used there. Numerical illustration on simple optimization program is given.stochastic programming, robust programming, weak dependence
An optimization problem often has some uncertain data, and the optimum of a linear program can be ve...
This thesis concentrates on stochastic programming problems based on empirical and theoretical distr...
We consider stability of solutions to optimization problems with probabilistic constraints under per...
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We s...
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We sp...
Chance constrained problems are optimization problems where one or more constraints ensure that the ...
In this paper, we introduce an approach for constructing uncertainty sets for robust optimization us...
Abstract Uncertainty is often present in environmental and energy economics. Tra-ditional approaches...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
Many engineering problems can be cast as optimization problems subject to convex constraints that ar...
Part 3: Stochastic Optimization and ControlInternational audienceDue to their frequently observed la...
Many engineering problems can be cast as optimization problems subject to convex constraints that ar...
Some developments in structure and stability of stochastic programs during the last decade together ...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
Convergence analysis for optimization problems with chance constraints concerns impact of variation ...
An optimization problem often has some uncertain data, and the optimum of a linear program can be ve...
This thesis concentrates on stochastic programming problems based on empirical and theoretical distr...
We consider stability of solutions to optimization problems with probabilistic constraints under per...
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We s...
The paper deals with two wide areas of optimization theory: stochastic and robust programming. We sp...
Chance constrained problems are optimization problems where one or more constraints ensure that the ...
In this paper, we introduce an approach for constructing uncertainty sets for robust optimization us...
Abstract Uncertainty is often present in environmental and energy economics. Tra-ditional approaches...
This paper briefly describes three well-established frameworks for handling uncertainty in optimizat...
Many engineering problems can be cast as optimization problems subject to convex constraints that ar...
Part 3: Stochastic Optimization and ControlInternational audienceDue to their frequently observed la...
Many engineering problems can be cast as optimization problems subject to convex constraints that ar...
Some developments in structure and stability of stochastic programs during the last decade together ...
Robust optimization is a valuable alternative to stochastic programming, where all underlying probab...
Convergence analysis for optimization problems with chance constraints concerns impact of variation ...
An optimization problem often has some uncertain data, and the optimum of a linear program can be ve...
This thesis concentrates on stochastic programming problems based on empirical and theoretical distr...
We consider stability of solutions to optimization problems with probabilistic constraints under per...