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Convex Optimization and Entropy

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (30 December 2016)

Special Issue Editor


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Guest Editor
Department of Automation, Kaunas University of Technology, Kaunas, Lithuania
Interests: process analysis and modelling; optimal control and Pontryagin's principle; sensor fusion and vision analysis applications in electromechanical systems; synthesis and research in foundations of inference and machine learning methods; optimal resource allocation using variational programming

Special Issue Information

Dear Colleagues,

Maximization of entropy solves a convex optimization problem with emphasis on the uncertainty of observed data in real world models. We can perturb a system in such a way that a system’s behavior exposes more about its parameters compared to their uncertainties alone. Moreover, uncertainty of observed values allows us to construct an exponential smoothing filter that simultaneously handles information about abrupt changes in the system or even initial and/or boundary conditions. Additional types of information besides uncertainty information of the same convex optimization might result in new scientific endeavors in signal processing, system identification, probabilistic analysis, optimal control, and convex/variational programming applications, including, but not limited to bio-processes, robotics, vision analysis, and electromechanical systems.

This Special Issue aims to contribute to the discussion about links, similarities, strengths and weaknesses of different inference approaches of convex optimization and/or probabilistic methods.

Comparison analysis and/or unit tests of working examples and solutions are encouraged and experimental results and “how-tos” are welcome.

Renaldas Urniezius
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

631 KiB  
Article
Divergence and Sufficiency for Convex Optimization
by Peter Harremoës
Entropy 2017, 19(5), 206; https://doi.org/10.3390/e19050206 - 03 May 2017
Cited by 5 | Viewed by 5297
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Convex Optimization and Entropy)
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