Regularity of set-valued maps and their selections through set differences. Part 1: Lipschitz continuity

We introduce Lipschitz continuity of set-valued maps with respect to a given set difference. The existence of Lipschitz selections that pass through any point of the graph of the map and inherit its Lipschitz constant is studied. We show that the Lipschitz property of the set-valued map with respect to the Demyanov difference with a given constant is characterized by the same property of its generalized Steiner selections. For a univariate multifunction with only compact values in R^n, we characterize its Lipschitz continuity in the Hausdorff metric (with respect to the metric difference) by the same property of its metric selections with the same constant. 2010 Mathematics Subject Classification: 54C65, 54C60, 26E25

Generalized Steiner selections applied to standard problems of set-valued numerical analysis

Generalized Steiner points and the corresponding selections for set-valued maps share interesting commutation properties with set operations which make them suitable for the set-valued numerical problems presented here. This short overview will present first applications of these selections to standard problems in this area, namely representation of convex, compact sets in R n and set operations, set-valued integration and interpolation as well as the calculation of attainable sets of linear differential inclusions. Hereby, the convergence results are given uniformly for a dense countable representation of generalized Steiner points/selections. To achieve this aim, stronger conditions on the set-valued map F have to be taken into account, e.g. the Lipschitz condition on F has to be satisfied for the Demyanov distance instead of the Hausdorff distance. To establish an overview on several applications, not the strongest available results are formulated in this article

Random extremal solutions of differential inclusions

Given a Lipschitz continuous multifunction $F$ on ${\mathbb{R}}^{n}$, we construct a probability measure on the set of all solutions to the Cauchy problem $\dot x\in F(x)$ with $x(0)=0$. With probability one, the derivatives of these random solutions take values within the set $ext F(x)$ of extreme points for a.e.~time $t$. This provides an alternative approach in the analysis of solutions to differential inclusions with non-convex right hand side

Regularity of set-valued maps and their selections through set differences. Part 2: One-sided Lipschitz properties

We introduce one-sided Lipschitz (OSL) conditions of setvalued maps with respect to given set differences. The existence of selections of such maps that pass through any point of their graphs and inherit uniformly their OSL constants is studied. We show that the OSL property of a convex-valued set-valued map with respect to the Demyanov difference with a given constant is characterized by the same property of the generalized Steiner selections. We prove that an univariate OSL map with compact images in R^1 has OSL selections with the same OSL constant. For such a multifunction which is OSL with respect to the metric difference, one-sided Lipschitz metric selections exist through every point of its graph with the same OSL constant. 2010 Mathematics Subject Classification: 47H06, 54C65, 47H04, 54C60, 26E25

Stabilita a aproximace pro úlohy stochastického programování

Matematicko-fyzikální fakultaFaculty of Mathematics and Physic

Stability and Sample-Based Approximations of Composite Stochastic Optimization Problems

Optimization under uncertainty and risk is indispensable in many practical situations. Our paper addresses stability of optimization problems using composite risk functionals that are subjected to multiple measure perturbations. Our main focus is the asymptotic behavior of data-driven formulations with empirical or smoothing estimators such as kernels or wavelets applied to some or to all functions of the compositions. We analyze the properties of the new estimators and we establish strong law of large numbers, consistency, and bias reduction potential under fairly general assumptions. Our results are germane to risk-averse optimization and to data science in general

The minimum time function for the controlled Moreau's sweeping process

Let C(t), t 65 0 be a Lipschitz set-valued map with closed and (mildly non-)convex values and f (t, x, u) be a map, Lipschitz continuous w.r.t. x. We consider the problem of reaching a target S within the graph of C subject to the differential inclusion x 08 12N_{C(t)} (x) + G(t, x) starting \u307from x_0 08 C(t_0 ) in the minimum time T (t_0 , x_0 ). The dynamics is called a perturbed sweeping (or Moreau) process. We give sufficient conditions for T to be finite and continuous and characterize T through Hamilton\u2013Jacobi inequalities. Crucial tools for our approach are characterizations of weak and strong flow invariance of a set S subject to the inclusion. Due to the presence of the normal cone N_{C(t)} (x), the right-hand side of the inclusion contains implicitly the state constraint x(t) 08 C(t) and is not Lipschitz continuous with respect to x