Information complexity of functional optimization problems and their approximation schemes

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Abstract

Functional optimization is investigated using tools from information-based complexity. In such optimization problems, a functional has to be minimized with respect to admissible solutions belonging to an infinite-dimensional space of functions. This context models tasks arising in optimal control, systems identification, machine learning, time-series analysis, etc. The solution via variable-basis approximation schemes, which provide a sequence of nonlinear programming problems approximating the original functional one, is considered. Also for such problems, the information complexity is estimated.

Published

08/25/2010

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Articles