Add to ORKGAbstract. We consider the Scenario Convex Program (SCP) for two classes of optimization problems that are not tractable in general: Robust Convex Programs (RCPs) and Chance-Constrained Programs (CCPs). We establish a probabilistic bridge from the optimal value of SCP to the optimal values of RCP and CCP in which the uncertainty takes values in a general, possibly infinite dimensional, metric space. We then extend our results to a certain class of non-convex problems that includes, for example, binary decision variables. In the process, we also settle a measurability issue for a general class of scenario programs, which to date has been addressed by an assumption. Finally, we demonstrate the applicability of our results on a benchmark proble...
In the companion paper we introduced a theory for random convex programs (RCPs), deriving tight uppe...
This paper proposes a new probabilistic solution framework for robust control analysis and synthesis...
Discarding constraints in scenario optimization, a technique known as the sampling-and-discarding sc...
Abstract. A central issue arising in financial, engineering and, more generally, in many applicative...
We consider the scenario approach theory to deal with convex optimization programs affected by uncer...
We study the objective function value performance of the scenario approach for robust convex optimiz...
The scenario optimization method developed by Calafiore and Campi (2006) is a theoretically sound an...
\u3cp\u3eRandomized optimization is an established tool for control design with modulated robustness...
The scenario approach is a general methodology for data-based optimization that has attracted a grea...
Randomized optimization is an established tool for control design with modulated robustness. While f...
The "scenario approach" provides an intuitive method to address chance constrained problems arising ...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
Randomized optimization is an established tool for control design with modulated robustness. While f...
Scenarios are indispensable ingredients for the numerical solution of stochastic programs. Earlier a...
In the companion paper we introduced a theory for random convex programs (RCPs), deriving tight uppe...
This paper proposes a new probabilistic solution framework for robust control analysis and synthesis...
Discarding constraints in scenario optimization, a technique known as the sampling-and-discarding sc...
Abstract. A central issue arising in financial, engineering and, more generally, in many applicative...
We consider the scenario approach theory to deal with convex optimization programs affected by uncer...
We study the objective function value performance of the scenario approach for robust convex optimiz...
The scenario optimization method developed by Calafiore and Campi (2006) is a theoretically sound an...
\u3cp\u3eRandomized optimization is an established tool for control design with modulated robustness...
The scenario approach is a general methodology for data-based optimization that has attracted a grea...
Randomized optimization is an established tool for control design with modulated robustness. While f...
The "scenario approach" provides an intuitive method to address chance constrained problems arising ...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
Randomized optimization is an established tool for control design with modulated robustness. While f...
Scenarios are indispensable ingredients for the numerical solution of stochastic programs. Earlier a...
In the companion paper we introduced a theory for random convex programs (RCPs), deriving tight uppe...
This paper proposes a new probabilistic solution framework for robust control analysis and synthesis...
Discarding constraints in scenario optimization, a technique known as the sampling-and-discarding sc...