On Design of Polyhedral Estimates in Linear Inverse Problems
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Polyhedral estimate is a generic efficiently computable nonlinear in observations routine for recovering unknown signal belonging to a given convex compact set from noisy observation of signal's linear image. Risk analysis and optimal design of polyhedral estimates may be addressed through efficient bounding of optimal values of optimization problems. Such problems are typically hard; yet, it was shown in Juditsky, Nemirovski 2019 that nearly minimax optimal ("up to logarithmic factors") estimates can be efficiently constructed when the signal set is an ellitope - a member of a wide family of convex and compact sets of special geometry (see, e.g., Juditsky, Nemirovski 2018). The subject of this paper is a new risk analysis for polyhedral estimate in the situation where the signal set is an intersection of an ellitope and an arbitrary polytope allowing for improved polyhedral estimate design in this situation.
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