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Entropy 2017, 19(5), 206; doi:10.3390/e19050206

Divergence and Sufficiency for Convex Optimization

GSK Department, Copenhagen Business College, Nørre Voldgade 34, 1358 Copenhagen K, Denmark
Academic Editor: Renaldas Urniezius
Received: 30 December 2016 / Revised: 11 April 2017 / Accepted: 2 May 2017 / Published: 3 May 2017
(This article belongs to the Special Issue Convex Optimization and Entropy)
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Logarithmic score and information divergence appear in information theory, statistics, statistical mechanics, and portfolio theory. We demonstrate that all these topics involve some kind of optimization that leads directly to regret functions and such regret functions are often given by Bregman divergences. If a regret function also fulfills a sufficiency condition it must be proportional to information divergence. We will demonstrate that sufficiency is equivalent to the apparently weaker notion of locality and it is also equivalent to the apparently stronger notion of monotonicity. These sufficiency conditions have quite different relevance in the different areas of application, and often they are not fulfilled. Therefore sufficiency conditions can be used to explain when results from one area can be transferred directly to another and when one will experience differences. View Full-Text
Keywords: Bregman divergence; entropy; exergy; Kraft’s inequality; locallity; monotonicity; portfolio; regret; scoring rule; sufficiency Bregman divergence; entropy; exergy; Kraft’s inequality; locallity; monotonicity; portfolio; regret; scoring rule; sufficiency

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Harremoës, P. Divergence and Sufficiency for Convex Optimization. Entropy 2017, 19, 206.

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