In this article we study the problem of finite-time constrained optimal control unknown stochastic linear time-invariant (LTI) systems, which is key ingredient a predictive algorithm—albeit typically having access to model. We propose novel distributionally robust data-enabled (DeePC) algorithm uses noise-corrupted input/output data predict future trajectories and compute inputs while satisfyin...

Time management under uncertainty is essential to large scale projects. From space exploration industrial production, there a need schedule and perform activities. given complex specifications on timing. In order generate schedules that are robust in the duration of activities, prior work has focused problem framing uses an interval-bounded representation. However, such approaches unable take a...

We propose a chance-constrained formulation for the problem of dimensioning frequency restoration reserves on power transmission network. cast our as two-stage stochastic mixed integer linear program, and heuristic algorithm solving problem. Our model accounts simultaneous sizing both upward downward reserves, uncertainty driven by imbalances, contingencies available capacity. core methodology ...

A natural way to handle optimization problem with data affected by stochastic uncertainty is to pass to a chance constrained version of the problem, where candidate solutions should satisfy the randomly perturbed constraints with probability at least 1− . While being attractive from modeling viewpoint, chance constrained problems “as they are” are, in general, computationally intractable. In th...

We consider a chance constrained problem, where one seeks to minimize a convex objective over solutions satisfying, with a given close to one probability, a system of randomly perturbed convex constraints. Our goal is to build a computationally tractable approximation of this (typically intractable) problem, i.e., an explicitly given deterministic optimization program with the feasible set cont...

Ambiguous Chance Constrained Programs: Algorithms and Applications Emre Erdoğan Chance constrained problems are optimization problems where one or more constraints ensure that the probability of one or more events occurring is less than a prescribed threshold. Although it is typically assumed that the distribution defining the chance constraints are known perfectly; in practice this assumption ...

The aim of this paper is to provide new efficient methods for solving general chance-constrained integer linear programs to optimality. Valid linear inequalities are given for these problems. They are proved to characterize properly the set of solutions. They are based on a specific scenario, whose definition impacts strongly on the quality of the linear relaxation built. A branch-and-cut algor...

In this paper, we consider the link prediction problem, where we are given a partial snapshot of a network at some time and the goal is to predict additional links at a later time. The accuracy of the current prediction methods is quite low due to the extreme class skew and the large number of potential links. In this paper, we describe learning algorithms based on chance constrained programs a...

This paper aims at proposing tractable algorithms to find effectively good solutions to large size chance-constrained combinatorial problems. A new robust model is introduced to deal with uncertainty in mixed-integer linear problems. It is shown to be strongly related to chance-constrained programming when considering pure 0– 1 problems. Furthermore, its tractability is highlighted. Then, an op...