Convex Optimization and Entropy
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (30 December 2016)
Special Issue Editor
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
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