Abstract

The problem of minimizing an entropy functional subject to linear constraints is a useful example of partially finite convex programming. In the 1990s, Borwein and Lewis provided broad and easy-to-verify conditions that guarantee strong duality for such problems. Their approach is to construct a function in the quasi-relative interior of the relevant infinite-dimensional set, which assures the existence of a point in the core of the relevant finite-dimensional set. We revisit this problem, and provide an alternative proof by directly appealing to the definition of the core, rather than by relying on any properties of the quasi-relative interior. Our approach admits a minor relaxation of the linear independence requirements in Borwein and Lewis' framework, which allows us to work with certain piecewise-defined moment functions precluded by their conditions. We provide such a computed example that illustrates how this relaxation may be used to tame observed Gibbs phenomenon when the underlying data is discontinuous. The relaxation illustrates the understanding we may gain by tackling partially-finite problems from both the finite-dimensional and infinite-dimensional sides. The comparison of these two approaches is informative, as both proofs are constructive.

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Keywords

  • Quasi relative interior
  • core
  • strong duality
  • entropy functional optimization
  • partially finite convex programming

Mathematics Subject Classification

46N10

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Suggested citation

S. B. Lindstrom. “A Note on Cores and Quasi Relative Interiors in Partially Finite Convex Programming.” Journal of Convex Analysis 29 (2022), No. 1, 129–142. https://doi.org/10.68381/jca29007

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Published by Heldermann Verlag, 2022. Rights now held by Banach Press LLC.