In this paper we explain how to characterize the best approximation to any x in a Hilbert space X from the set C ∩ {x ∈ X : gi(x) ≤ 0, i = 1, 2, · · · ,m} in the face of data uncertainty in the convex constraints, gi(x) ≤ 0, i = 1, 2, · · · ,m, where C is a closed convex subset of X. Following the robust optimization approach, we establish Lagrange multiplier characterizations of the robust con...

This paper tackles the problem of simultaneously designing a partition of an uncertain set and its corresponding set of multiple controllers that optimize the worst-case performance of a linear time invariant system under parametric uncertainty. The parametric uncertainty region is assumed to be convex polytopic, which is also partitioned into a set of convex polytopic local regions. It is desi...

We introduce the notion centre of a convex set and study space continuous affine functions on compact with centre. show that these spaces are precisely dual base normed in which underlying has (unique) also characterize corresponding norm space. obtain condition compact, balanced, subset locally space, so is an absolute order unit Similarly, we latter becomes absolutely

We address the problem of verifying Probabilistic Computation Tree Logic (PCTL) properties of Markov Decision Processes (MDPs) whose state transition probabilities are only known to lie within uncertainty sets. We first introduce the model of Convex-MDPs (CMDPs), i.e., MDPs with convex uncertainty sets. CMDPs generalize Interval-MDPs (IMDPs) by allowing also more expressive (convex) description...

This paper studies robust solutions and semidefinite linear programming (SDP) relaxations of a class of convex polynomial optimization problems in the face of data uncertainty. The class of convex optimization problems, called robust SOS-convex polynomial optimization problems, includes robust quadratically constrained convex optimization problems and robust separable convex polynomial optimiza...

We present a new approach to measure uncertainty/information applicable to theories based on convex sets of probability distributions, also called credal sets. A definition of a total disaggregated uncertainty measure on credal sets is proposed in this paper motivated by recent outcomes. This definition is based on the upper and lower values of Shannon’s entropy for a credal set. We justify the...

Non-probabilistic convex models need to be provided only the changing boundary of parameters rather than their exact probability distributions; thus, such models can be applied to uncertainty analysis of complex structures when experimental information is lacking. The interval and the ellipsoidal models are the two most commonly used modeling methods in the field of non-probabilistic convex mod...