In this note we derive an exact expression for the expected probability V of constraint violation in a sampled convex program (see [1], [2] for definitions and an introduction to this topic): V = expected number of support constraints/1 + number of constraints. This result (Theorem 1) is obtained using a simple technique based on cardinality count. In the note, we also use a Chernoff bounding technique on the upper tail violation probability expression derived in to obtain one of the tightest available explicit bounds on the sample complexity of sampled convex programs (Proposition 3)
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
Discarding constraints in scenario optimization, a technique known as the sampling-and-discarding sc...
Abstract. We consider the Scenario Convex Program (SCP) for two classes of optimization problems tha...
This paper deals with the sampled scenarios approach to robust convex programming. It has been shown...
In the companion paper we introduced a theory for random convex programs (RCPs), deriving tight uppe...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
We revisit the so-called sampling and discarding approach used to quantify the probability of constr...
The "scenario approach" provides an intuitive method to address chance constrained problems arising ...
In this paper we aim at output analysis with respect to changes of the probability distribution for ...
1 Abstract: This thesis deals with chance constrained stochastic programming problems. We consider s...
We consider the scenario approach theory to deal with convex optimization programs affected by uncer...
We consider a class of mixed-integer optimization problems subject to N randomly drawn convex constr...
We consider the fundamental problem of deriving quantitative bounds on the probability that a given ...
In this paper we consider stochastic programming problems where the objec-tive function is given as ...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
Discarding constraints in scenario optimization, a technique known as the sampling-and-discarding sc...
Abstract. We consider the Scenario Convex Program (SCP) for two classes of optimization problems tha...
This paper deals with the sampled scenarios approach to robust convex programming. It has been shown...
In the companion paper we introduced a theory for random convex programs (RCPs), deriving tight uppe...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
We revisit the so-called sampling and discarding approach used to quantify the probability of constr...
The "scenario approach" provides an intuitive method to address chance constrained problems arising ...
In this paper we aim at output analysis with respect to changes of the probability distribution for ...
1 Abstract: This thesis deals with chance constrained stochastic programming problems. We consider s...
We consider the scenario approach theory to deal with convex optimization programs affected by uncer...
We consider a class of mixed-integer optimization problems subject to N randomly drawn convex constr...
We consider the fundamental problem of deriving quantitative bounds on the probability that a given ...
In this paper we consider stochastic programming problems where the objec-tive function is given as ...
In many relevant situations, chance constrained linear programs can be explicitly converted into eff...
Discarding constraints in scenario optimization, a technique known as the sampling-and-discarding sc...
Abstract. We consider the Scenario Convex Program (SCP) for two classes of optimization problems tha...