Iterative Averaging of Entropic Projections for Solving Stochastic Convex Feasibility Problems
Computational Optimization and Applications
Nonsmooth analysis and control theory
Nonsmooth analysis and control theory
Bregman Monotone Optimization Algorithms
SIAM Journal on Control and Optimization
Bregman distances and Klee sets
Journal of Approximation Theory
The Bregman distance, approximate compactness and convexity of Chebyshev sets in Banach spaces
Journal of Approximation Theory
Klee sets and Chebyshev centers for the right Bregman distance
Journal of Approximation Theory
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A closed set of a Euclidean space is said to be Chebyshev if every point in the space has one and only one closest point in the set. Although the situation is not settled in infinite-dimensional Hilbert spaces, in 1932 Bunt showed that in Euclidean spaces a closed set is Chebyshev if and only if the set is convex. In this paper, from the more general perspective of Bregman distances, we show that if every point in the space has a unique nearest point in a closed set, then the set is convex. We provide two approaches: one is by nonsmooth analysis; the other by maximal monotone operator theory. Subdifferentiability properties of Bregman nearest distance functions are also given.