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This paper proposes a tractable iterative scheme for computing parameter-dependent matrix sign function.
We compute the spectral projector by constructing the matrix sign function through a simple polynomial recursion.
We describe a hybrid Lyapunov solver based on the matrix sign function, where the intensive parts of the computation are accelerated using a graphics processor (GPU) while executing the remaining operations on a general-purpose multi-core processor (CPU).
The other methodology comes from the iterative method, for example, it is referred to as Newton's method [6], matrix sign function method [19], disk function method [20], and structured doubling algorithms [21, 22] and references therein.
These integral formulas reveal that any property or iterative method involving the matrix sign function can be transformed into one for the polar decomposition by replacing (A^{2}) via (A^A), and vice versa.
Drawing the attraction basins [15] of (10) for finding the solution of the polynomial equation (u^{2}-1=0) in the complex plane reveals that the application of (9) for finding matrix sign function and consequently the unitary polar factor has global convergence.
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Following this algorithm, the matrix A is quantized by a sign function because A T in the block Newton method corresponds to the input signal in the conventional LMS algorithm.
The sign function S is defined as: And a matrix of leaping factors β is defined as a matrix form: Because β is only meaningful in two consecutive stages, the β matrix is much like a diagonal matrix.
For small dense problems one could employ the sign function method, [29, 30] but this would require storing full dense inverses of matrices of the given class, which is certainly not possible in the described acoustic field problems.
sgn Sign function.
sgn denotes sign function.
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