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• W2073162506 startingPage "75" @default.
• W2073162506 abstract "One of the key problems of motor control is the redundancy problem, in particular how the central nervous system (CNS) chooses an action out of infinitely many possible. A promising way to address this question is to assume that the choice is made based on optimization of a certain cost function. A number of cost functions have been proposed in the literature to explain performance in different motor tasks: from force sharing in grasping to path planning in walking. However, the problem of uniqueness of the cost function(s) was not addressed until recently. In this article, we analyze two methods of finding additive cost functions in inverse optimization problems with linear constraints, so-called linear-additive inverse optimization problems. These methods are based on the Uniqueness Theorem for inverse optimization problems that we proved recently (Terekhov et al., J Math Biol 61(3):423–453, 2010). Using synthetic data, we show that both methods allow for determining the cost function. We analyze the influence of noise on the both methods. Finally, we show how a violation of the conditions of the Uniqueness Theorem may lead to incorrect solutions of the inverse optimization problem." @default.
• W2073162506 created "2016-06-24" @default.
• W2073162506 creator A5018090717 @default.
• W2073162506 creator A5052023425 @default.
• W2073162506 date "2011-02-01" @default.
• W2073162506 modified "2023-09-25" @default.
• W2073162506 title "Analytical and numerical analysis of inverse optimization problems: conditions of uniqueness and computational methods" @default.
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• W2073162506 doi "https://doi.org/10.1007/s00422-011-0421-2" @default.
• W2073162506 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3098747" @default.
• W2073162506 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21311907" @default.
• W2073162506 hasPublicationYear "2011" @default.
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