Joint chance constrained problems give rise to many algorithmic challenges. Even in the convex case, i.e., when an appropriate transformation of the probabilistic constraint is a convex function, its cutting-plane linearization is just an approximation, produced by an oracle providing subgradient and function values that can only be evaluated inexactly. As a result, the cutting-plane model may ...

Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i) its probability of type I error is below a pre-specifie...

This manuscript concerns robust stability analysis of discrete-time LTI systems with varying time delays. The stability problem is treated in the Integral Quadratic Constraint (IQC) framework. The novelty and main contribution of the manuscript is the integral quadratic constraint characterization of the discrete-time time-varying delay operator. The characterization enables the IQC analysis to...

A valued constraint satisfaction problem (VCSP) instance (V,Π, w) is a set of variables V with a set of constraints Π weighted by w. Given a VCSP instance, we are interested in a reweighted subinstance (V,Π′ ⊂ Π, w′) that preserves the value of the given instance (under every assignment to the variables) within factor 1 ± . A well-studied special case is cut sparsification in graphs, which has ...

Uncertainty issues associated with a multi-site, multi-product supply chain planning problem has been analyzed in this paper using the chance constraint programming approach. In literature, such problems have been addressed using the two stage stochastic programming approach. While this approach has merits in terms of decomposition, computational complexity even for small size planning problem ...

We present a stochastic version of Concurrent Constraint Programming (CCP), where we associate a rate to each basic instruction that interacts with the constraint store. We give an operational semantic that can be provided either with a discrete or a continuous model of time. The notion of observables is discussed, both for the discrete and the continuous version, and a connection between the t...

This paper investigates the discrete-time per-sample model of the zero-dispersion optical fiber. It is shown that the capacityachieving input distribution is unique, has (continuous) uniform phase and discrete amplitude with a finite number of mass points. The optimality of this multi-ring input holds when the channel is subject to general input cost constraints that include peak power constrai...

The CSP formalism has shown, for many years, its interest for the representation of numerous kinds of problems, and also often provide effective resolution methods in practice. This formalism has also provided a useful framework for the knowledge representation as well as to implement efficient methods for reasoning about knowledge. The data of a CSP are usually expressed in terms of a constrai...

The Counting Constraint Satisfaction Problem (#CSP) can be expressed as follows: given a set of variables, a set of values that can be taken by the variables, and a set of constraints specifying some restrictions on the values that can be taken simultaneously by some variables, determine the number of assignments of values to variables that satisfy all the constraints. The #CSP provides a gener...

This study covers the output-feedback model predictive control (MPC) of nonlinear systems subjected to stochastic disturbances and state chance constraints. The optimal problem is solved in a dynamic programming fashion, performed with extended Kalman filter. information summarized as Gaussian belief model. Thus, Bellman equation transformed into deterministic using this resulting constrained p...