Add to ORKGAbstract. In this paper we deal with linear chance-constrained opti-mization problems, a class of problems which naturally arise in practical applications in finance, engineering, transportation and scheduling, where decisions are made in presence of uncertainty. After giving the determin-istic equivalent formulation of a linear chance-constrained optimization problem we construct a conjugate dual problem to it. Then we provide for this primal-dual pair weak sufficient conditions which ensure strong duality. In this way we generalize some results recently given in the litera-ture. We also apply the general duality scheme to a portfolio optimization problem, a fact that allows us to derive necessary and sufficient optimality conditions for i...
In this dissertation, we investigate chance-constrained linear matrix inequality (LMI) optimization ...
A fundamental problem in risk management is the robust aggregation of different sources of risk in a...
Recently, coherent risk measure minimization was formulated as robust optimization and the correspon...
The aim of this thesis is to present new results concerning duality in scalar optimization. We show ...
We apply conjugate duality to establish existence of optimal portfolios in an asset-allocation probl...
Abstract. A central issue arising in financial, engineering and, more generally, in many applicative...
Duality theory is important in finding solutions to optimization problems. For example, in linear pr...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
Choice among risky investments has been described using a chance constrained programming model with ...
In this paper we extend to a multi-objective optimization with a interval-valued function and real v...
Abstract This paper investigates the computational aspects of distributionally ro-bust chance constr...
We solve a linear chance constrained portfolio optimization problem using Robust Optimization (RO) m...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
This article studies convex duality in stochastic optimization over fi-nite discrete-time. The first...
Robust and distributionally robust optimization are modeling paradigms for decision-making under unc...
In this dissertation, we investigate chance-constrained linear matrix inequality (LMI) optimization ...
A fundamental problem in risk management is the robust aggregation of different sources of risk in a...
Recently, coherent risk measure minimization was formulated as robust optimization and the correspon...
The aim of this thesis is to present new results concerning duality in scalar optimization. We show ...
We apply conjugate duality to establish existence of optimal portfolios in an asset-allocation probl...
Abstract. A central issue arising in financial, engineering and, more generally, in many applicative...
Duality theory is important in finding solutions to optimization problems. For example, in linear pr...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
Choice among risky investments has been described using a chance constrained programming model with ...
In this paper we extend to a multi-objective optimization with a interval-valued function and real v...
Abstract This paper investigates the computational aspects of distributionally ro-bust chance constr...
We solve a linear chance constrained portfolio optimization problem using Robust Optimization (RO) m...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
This article studies convex duality in stochastic optimization over fi-nite discrete-time. The first...
Robust and distributionally robust optimization are modeling paradigms for decision-making under unc...
In this dissertation, we investigate chance-constrained linear matrix inequality (LMI) optimization ...
A fundamental problem in risk management is the robust aggregation of different sources of risk in a...
Recently, coherent risk measure minimization was formulated as robust optimization and the correspon...