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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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A novel iterative method for computing generalized inverse.

Youshen Xia1, Tianping Chen, Jinjun Shan

  • 1College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350002, China ysxia2001@yahoo.com.

Neural Computation
|November 12, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new iterative method for calculating the generalized inverse, featuring low computational complexity and proven global convergence. An acceleration scheme is also presented for faster computations.

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Area of Science:

  • Numerical Analysis
  • Linear Algebra

Background:

  • Generalized inverse computation is crucial in various scientific and engineering fields.
  • Existing methods may suffer from high computational complexity or convergence issues.

Purpose of the Study:

  • To propose a novel iterative method for computing the generalized inverse.
  • To develop an acceleration scheme for faster computation of the generalized inverse.
  • To demonstrate the global convergence and effectiveness of the proposed methods.

Main Methods:

  • A novel iterative algorithm based on a Karush-Kuhn-Tucker (KKT) formulation.
  • The algorithm involves four matrix and vector multiplications per iteration.
  • An acceleration scheme is introduced to enhance computational speed.

Main Results:

  • The proposed iterative method exhibits low computational complexity.
  • Global convergence is proven for the iterative method without any conditions.
  • The acceleration scheme also demonstrates global convergence.
  • Numerical evaluations confirm the effectiveness of the proposed algorithms.

Conclusions:

  • The novel iterative method provides an efficient and robust approach for generalized inverse computation.
  • The acceleration scheme significantly enhances the speed of generalized inverse calculation.
  • The findings offer a valuable contribution to numerical linear algebra and its applications.