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Extreme eigenpairs computation is of considerable interest in signal processing and estimation. Thus problem of simultaneous computation of the smallest and largest eigenvalues and the corresponding eigenvectors of a symmetric matrix is considered. The proposed methods are derived from optimizing cost functions which are chosen to have optimal values at vectors that are linear combinations of extreme...
In this paper, we deal with the Henig efficiency for set-valued optimization problems in sense of subdifferential. By the epiderivative for a set-valued mapping, the concepts of the generalized gradient and subdifferential for efficiency are introduced. Based upon the separation theorem, the existence for Henig subdifferential is established, and the optimality condition for Henig efficient solutions...
Algorithms solving inverse problems for simple (open) kinematic chains are presented in this paper. Due to very high kinematics chains construction complexity, those algorithms should be as universal as possible. That is why optimization methods are used. They allow fast and accurate calculations for arbitrary kinematics chains constructions, permitting them to be used with success in robotics and...
This paper presents a low-complexity reduced-rank approach to adaptive linearly constrained minimum variance (LCMV) beamforming. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of adaptive filters according to the minimum variance criterion. The constrained joint iterative optimization procedure adjusts the parameters of a bank of full-rank adaptive filters...
In this paper, we present a recursive method for the optimization of humanoid robot motions. The method is based on an efficient dynamics algorithm, which allows the calculation of the gradient function with respect to the control parameters analytically. The algorithm makes use of the theory of Lie groups and Lie algebra. The main objective of this method is to smooth the pre-calculated humanoid...
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