The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

Tho Le-ngoc - One of the best experts on this subject based on the ideXlab platform.

  • Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion
    2008 International Wireless Communications and Mobile Computing Conference, 2008
    Co-Authors: Hsiao-chun Wu, Shih Yu Chang, Tho Le-ngoc
    Abstract:

    Least-square estimation (LSE) and multiple- parameter linear regression (MLR) are the important estimation techniques for engineering and science, especially in the communications and signal processing areas. The majority of computational complexity incurred in LSE and MLR arises from a Hermitian Matrix inversion. In practice, the Yule-Walker equations are not valid and hence the Levinson-Durbin algorithm cannot be employed for general LSE and MLR problems. Therefore, the most efficient Hermitian Matrix inversion method is based on the Cholesky factorization. In this paper, we derive a new dyadic recursion algorithm for sequential rank-adaptive Hermitian Matrix inversions. In addition, we provide the theoretical computational complexity analyses to compare our new dyadic recursion scheme and the conventional Cholesky factorization. We can design a variable model-order LSE (MLR) using this proposed dyadic recursion approach thereupon. Through our complexity analyses and the Monte Carlo simulations, we show that our new dyadic recursion algorithm is more efficient than the conventional Cholesky factorization for the sequential rank-adaptive LSE (MLR) and the associated variable model-order LSE (MLR) can seek the trade-off between the targeted estimation performance and the required computational complexity.

Hsiao-chun Wu - One of the best experts on this subject based on the ideXlab platform.

  • efficient rank adaptive least square estimation and multiple parameter linear regression using novel dyadically recursive Hermitian Matrix inversion
    International Journal of Antennas and Propagation, 2012
    Co-Authors: Hsiao-chun Wu, Shih Yu Chang, Tho Lengoc, Yiyan Wu
    Abstract:

    Least-square estimation (LSE) and multiple-parameter linear regression (MLR) are the important estimation techniques for engineering and science, especially in the mobile communications and signal processing applications. The majority of computational complexity incurred in LSE and MLR arises from a Hermitian Matrix inversion. In practice, the Yule-Walker equations are not valid, and hence the Levinson-Durbin algorithm cannot be employed for general LSE and MLR problems. Therefore, the most efficient Hermitian Matrix inversion method is based on the Cholesky factorization. In this paper, we derive a new dyadic recursion algorithm for sequential rank-adaptive Hermitian Matrix inversions. In addition, we provide the theoretical computational complexity analyses to compare our new dyadic recursion scheme and the conventional Cholesky factorization. We can design a variable model-order LSE (MLR) using this proposed dyadic recursion approach thereupon. Through our complexity analyses and the Monte Carlo simulations, we show that our new dyadic recursion algorithm is more efficient than the conventional Cholesky factorization for the sequential rank-adaptive LSE (MLR) and the associated variable model-order LSE (MLR) can seek the trade-off between the targeted estimation performance and the required computational complexity. Our proposed new scheme can benefit future portable and mobile signal processing or communications devices.

  • Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion
    2008 International Wireless Communications and Mobile Computing Conference, 2008
    Co-Authors: Hsiao-chun Wu, Shih Yu Chang, Tho Le-ngoc
    Abstract:

    Least-square estimation (LSE) and multiple- parameter linear regression (MLR) are the important estimation techniques for engineering and science, especially in the communications and signal processing areas. The majority of computational complexity incurred in LSE and MLR arises from a Hermitian Matrix inversion. In practice, the Yule-Walker equations are not valid and hence the Levinson-Durbin algorithm cannot be employed for general LSE and MLR problems. Therefore, the most efficient Hermitian Matrix inversion method is based on the Cholesky factorization. In this paper, we derive a new dyadic recursion algorithm for sequential rank-adaptive Hermitian Matrix inversions. In addition, we provide the theoretical computational complexity analyses to compare our new dyadic recursion scheme and the conventional Cholesky factorization. We can design a variable model-order LSE (MLR) using this proposed dyadic recursion approach thereupon. Through our complexity analyses and the Monte Carlo simulations, we show that our new dyadic recursion algorithm is more efficient than the conventional Cholesky factorization for the sequential rank-adaptive LSE (MLR) and the associated variable model-order LSE (MLR) can seek the trade-off between the targeted estimation performance and the required computational complexity.

E M Godfrin - One of the best experts on this subject based on the ideXlab platform.

  • a method to compute the inverse of an n block tridiagonal quasi Hermitian Matrix
    Journal of Physics: Condensed Matter, 1991
    Co-Authors: E M Godfrin
    Abstract:

    This paper presents a method for computing the inverse of a complex n-block tridiagonal quasi-Hermitian Matrix using an adequate number of partitions of the complete Matrix. This type of Matrix is very usual in quantum mechanics and, more specifically, in solid state physics (e.g. interfaces and super-lattices), when the tight-binding approximation is used. The efficiency of the method is analysed by comparing the required CPU time and work-area with other techniques.

Shih Yu Chang - One of the best experts on this subject based on the ideXlab platform.

  • efficient rank adaptive least square estimation and multiple parameter linear regression using novel dyadically recursive Hermitian Matrix inversion
    International Journal of Antennas and Propagation, 2012
    Co-Authors: Hsiao-chun Wu, Shih Yu Chang, Tho Lengoc, Yiyan Wu
    Abstract:

    Least-square estimation (LSE) and multiple-parameter linear regression (MLR) are the important estimation techniques for engineering and science, especially in the mobile communications and signal processing applications. The majority of computational complexity incurred in LSE and MLR arises from a Hermitian Matrix inversion. In practice, the Yule-Walker equations are not valid, and hence the Levinson-Durbin algorithm cannot be employed for general LSE and MLR problems. Therefore, the most efficient Hermitian Matrix inversion method is based on the Cholesky factorization. In this paper, we derive a new dyadic recursion algorithm for sequential rank-adaptive Hermitian Matrix inversions. In addition, we provide the theoretical computational complexity analyses to compare our new dyadic recursion scheme and the conventional Cholesky factorization. We can design a variable model-order LSE (MLR) using this proposed dyadic recursion approach thereupon. Through our complexity analyses and the Monte Carlo simulations, we show that our new dyadic recursion algorithm is more efficient than the conventional Cholesky factorization for the sequential rank-adaptive LSE (MLR) and the associated variable model-order LSE (MLR) can seek the trade-off between the targeted estimation performance and the required computational complexity. Our proposed new scheme can benefit future portable and mobile signal processing or communications devices.

  • Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion
    2008 International Wireless Communications and Mobile Computing Conference, 2008
    Co-Authors: Hsiao-chun Wu, Shih Yu Chang, Tho Le-ngoc
    Abstract:

    Least-square estimation (LSE) and multiple- parameter linear regression (MLR) are the important estimation techniques for engineering and science, especially in the communications and signal processing areas. The majority of computational complexity incurred in LSE and MLR arises from a Hermitian Matrix inversion. In practice, the Yule-Walker equations are not valid and hence the Levinson-Durbin algorithm cannot be employed for general LSE and MLR problems. Therefore, the most efficient Hermitian Matrix inversion method is based on the Cholesky factorization. In this paper, we derive a new dyadic recursion algorithm for sequential rank-adaptive Hermitian Matrix inversions. In addition, we provide the theoretical computational complexity analyses to compare our new dyadic recursion scheme and the conventional Cholesky factorization. We can design a variable model-order LSE (MLR) using this proposed dyadic recursion approach thereupon. Through our complexity analyses and the Monte Carlo simulations, we show that our new dyadic recursion algorithm is more efficient than the conventional Cholesky factorization for the sequential rank-adaptive LSE (MLR) and the associated variable model-order LSE (MLR) can seek the trade-off between the targeted estimation performance and the required computational complexity.

John E Pask - One of the best experts on this subject based on the ideXlab platform.

  • a projected preconditioned conjugate gradient algorithm for computing many extreme eigenpairs of a Hermitian Matrix
    Journal of Computational Physics, 2015
    Co-Authors: Eugene Vecharynski, Chao Yang, John E Pask
    Abstract:

    We present an iterative algorithm for computing an invariant subspace associated with the algebraically smallest eigenvalues of a large sparse or structured Hermitian Matrix A. We are interested in the case in which the dimension of the invariant subspace is large (e.g., over several hundreds or thousands) even though it may still be small relative to the dimension of A. These problems arise from, for example, density functional theory (DFT) based electronic structure calculations for complex materials. The key feature of our algorithm is that it performs fewer Rayleigh-Ritz calculations compared to existing algorithms such as the locally optimal block preconditioned conjugate gradient or the Davidson algorithm. It is a block algorithm, and hence can take advantage of efficient BLAS3 operations and be implemented with multiple levels of concurrency. We discuss a number of practical issues that must be addressed in order to implement the algorithm efficiently on a high performance computer.