The study and the determination of polyhedral regions of local stability for linear systems subject to control saturation is addressed. The analysis of the nonlinear behavior of the closed-loop saturated system is made by dividing the state space in regions of saturation. Inside each of these regions, the system evolution can be represented by a linear system with an additive disturbance. From ...

The scatterometer wind retrieval process produces several possible wind vector choices or ambiguities at each resolution cell. Ambiguity selection routines are generally ad hoc and often result in ambiguity selection errors. It is important to locate areas of ambiguity selection error to assess the quality of scatterometer wind data. A quality assurance algorithm is presented based on comparing...

In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based model for the posterior probability function. We propose an alternating optimization algorithm, na...

We show that the presentation of affine T-varieties of complexity one in terms of polyhedral divisors holds over an arbitrary field. We also describe a class of multigraded algebras over Dedekind domains. We study how the algebra associated to a polyhedral divisor changes when we extend the scalars. As another application, we provide a combinatorial description of affine G-varieties of complexi...

We propose a Bayesian framework for assessing the relative strengths of data-driven ambiguity sets in distributionally robust optimization (DRO) when the underlying distribution is defined by a finite-dimensional parameter. The key idea is to measure the relative size between a candidate ambiguity set and a specific asymptotically optimal set. As the amount of data grows large, this asymptotica...

We propose a novel, Bayesian framework for assessing the relative strengths of data-driven ambiguity sets in distributionally robust optimization (DRO). The key idea is to measure the relative size between a candidate ambiguity set and an asymptotically optimal set as the amount of data grows large. This asymptotically optimal set is provably the smallest convex ambiguity set that satisfies a s...

We explore connections between polyhedral projection and inference in propositional logic We formulate the problem of drawing all inferences that contain a restricted set of atoms i e all inferences that pertain to a given question as a logical projection problem We show that polyhedral projection partially solves this problem and in particular derives precisely those inferences that can be obt...

We prove that every separable polyhedral Banach space X is isomorphic to a polyhedral Banach space Y such that, the set ext BY ∗ cannot be covered by a sequence of balls B(yi, 2i) with 0 < 2i < 1 and 2i → 0. In particular ext BY ∗ cannot be covered by a sequence of norm compact sets. This generalizes a result from [7] where an equivalent polyhedral norm ||| · ||| on c0 was constructed such that...