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Non-orthogonal joint diagonalization algorithm based on hybrid trust region method and its application to blind source separation

机译:基于混合信任域方法的非正交联合对角化算法及其在盲源分离中的应用

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We proposed an algorithm for the efficient non-orthogonal joint diagonalization of a given set of matrices. The algorithm is based on the hybrid trust region method (HTRM) and its optimization approach, on which the efficiency of the method depends. Unlike traditional trust region methods that resolve sub-problems, HTRM efficiently searches a region via a quasi-Newton approach, by which it identifies new iteration points when a trial step is rejected. Thus, the proposed algorithm improves computational efficiency. Under mild conditions, we prove that the HTRM-based algorithm has global convergence properties together with local superlinear and quadratic convergence rates. Finally, we apply the combinative algorithm to blind source separation (BSS). Numerical results show that this method is highly robust, and computer simulations indicate that the algorithms excellently performs BSS.
机译:我们提出了一种用于给定矩阵集的有效非正交联合对角化算法。该算法基于混合信任区域方法(HTRM)及其优化方法,该方法的效率取决于该方法。与解决子问题的传统信任区域方法不同,HTRM通过准牛顿方法有效地搜索区域,该方法可在拒绝试验步骤时识别新的迭代点。因此,提出的算法提高了计算效率。在温和条件下,我们证明了基于HTRM的算法具有全局收敛性以及局部超线性和二次收敛率。最后,我们将组合算法应用于盲源分离(BSS)。数值结果表明,该方法具有很高的鲁棒性,计算机仿真表明该算法具有出色的BSS性能。

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