matrix theory


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matrix theory

[′mā·triks ‚thē·ə·rē]
(mathematics)
The algebraic study of matrices and their use in evaluating linear processes.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
His emphasis is on the phenomenal legal career of his father Sir Alladi Krishnaswami Iyer and his role in drafting the Constitution of India; his own efforts to introduce modern physics into Madras by conducting a theoretical physics seminar at his house; his visits to about 200 academic centers of research worldwide to lecture on his research in theoretical physics; and his fundamental work in stochastic processes, elementary particle physics, matrix theory, and Einstein's special theory of relativity.
In his extraordinary lifetime Gauss contributed to a number of fields including number theory, algebra, statistics, analysis, differential geometry, geodesy, geophysics, mechanics, electrostatics, magnetic fields, astronomy, matrix theory and optics.
The study of matrix equations and systems of matrix equations is an active research topic in matrix theory and its applications.
Random matrix theory (see the classical text [1]) first appeared in physics in Wigner's work on the level spacing in large nuclei.
Macedo, "Brownian-motion ensembles of random matrix theory: a classification scheme and an integral transform method," Nuclear Physics, vol.
Reference [4] explains the application of linear transformation in matrix theory. It uses 2DPCA to find out the feature vectors and uses the classical one-dimensional PCA technology to make further compression, so that the dimension is reduced.
This paper has a mathematical methodological character utilizing only basic matrix theory. It shows two ways for how to prove the well-known statement regarding the set of eigenvalues of Aw = [lambda]w with a consistent matrix A of type n x n (n [greater than or equal to] 2), and it demonstrates how to find the corresponding eigenvector components presented as some probabilities of events for maximal eigenvalue [lambda] = [[lambda].sub.max] = n.
Classical matrix theory sometimes fails to solve the problems involving uncertainties.
In recent years, the method of random matrix theory has been applied to the spectrum sensing [5, 6], which uses the eigenvalue of the signal covariance matrix as the statistic and then derives the corresponding threshold to judge.
Equally pertinent is the discussion of the cortical body matrix theory as a connection between cognitive and homeostatic representations of the body.
Based on the random matrix theory (RMT), the algorithm is used to process the signal data received by multiple antennas.