Results 171 to 180 of about 178,756 (227)
Exact collective occupancies of the Moshinsky model in two-dimensional geometry. [PDF]
Sci RepKuroś A +3 more
europepmc +1 more source
Saltations of cis-regulatory modules in Canidae and Hominidae. [PDF]
Sci RepShi J, Wang L, Li LM.
europepmc +1 more source
Prediction of Magnetic Fields in Single-Phase Transformers Under Excitation Inrush Based on Machine Learning. [PDF]
Sensors (Basel)Peng Q, Du H, Zheng Z, Zhu H, Fang Y.
europepmc +1 more source
FastPCA: An R package for fast singular value decomposition. [PDF]
J Open Source SoftwWard KR +5 more
europepmc +1 more source
Characterizing Critical Sources of Carbon Emissions Using Principal Component Analysis. [PDF]
ScientificWorldJournalQureshi M +7 more
europepmc +1 more source
Some of the next articles are maybe not open access.
2017
This chapter begins with the basic theory of eigenvalues and eigenvectors of matrices. Essential concepts such as characteristic polynomials, the Fundamental Theorem of Algebra, the Gerschgorin circle theorem, invariant subspaces, change of basis, spectral radius and the distance between subspaces are developed.
Ravi P. Agarwal, Cristina Flaut
+5 more sources
This chapter begins with the basic theory of eigenvalues and eigenvectors of matrices. Essential concepts such as characteristic polynomials, the Fundamental Theorem of Algebra, the Gerschgorin circle theorem, invariant subspaces, change of basis, spectral radius and the distance between subspaces are developed.
Ravi P. Agarwal, Cristina Flaut
+5 more sources
2010
Given a square matrix \( {\rm A} \in \mathbb{C}^{{n \times n}} \), the eigenvalue problem consists in finding a scalar λ (real or complex) and a nonnull vector x such that $${\rm Ax} = \lambda{\rm x}$$ (6.1) Any such λ is called an eigenvalue of A, while x is the associated eigenvector.
Alfio Quarteroni +2 more
+4 more sources
Given a square matrix \( {\rm A} \in \mathbb{C}^{{n \times n}} \), the eigenvalue problem consists in finding a scalar λ (real or complex) and a nonnull vector x such that $${\rm Ax} = \lambda{\rm x}$$ (6.1) Any such λ is called an eigenvalue of A, while x is the associated eigenvector.
Alfio Quarteroni +2 more
+4 more sources
1997
Gaussian elimination plays a fundamental role in solving a system Ax = b of linear equations. In order to solve a system of linear equations, Gaussian elimination reduces the augmented matrix to a (reduced) row-echelon form by using elementary row operations that preserve row and null spaces.
Jin Ho Kwak, Sungpyo Hong
openaire +1 more source
Gaussian elimination plays a fundamental role in solving a system Ax = b of linear equations. In order to solve a system of linear equations, Gaussian elimination reduces the augmented matrix to a (reduced) row-echelon form by using elementary row operations that preserve row and null spaces.
Jin Ho Kwak, Sungpyo Hong
openaire +1 more source
2014
Recall that an n × n matrix B is similar to an n × n matrix A if there is an invertible n × n matrix P such that B = P −1 AP. Our objective now is to determine under what conditions an n × n matrix is similar to a diagonal matrix. In so doing we shall draw together all of the notions that have been previously developed.
openaire +2 more sources
Recall that an n × n matrix B is similar to an n × n matrix A if there is an invertible n × n matrix P such that B = P −1 AP. Our objective now is to determine under what conditions an n × n matrix is similar to a diagonal matrix. In so doing we shall draw together all of the notions that have been previously developed.
openaire +2 more sources
2007
Eigenvalues and the associated eigenvectors of an endomorphism of a vector space are defined and studied, as is the spectrum of an endomorphism. The characteristic polynomial of a matrix is considered and used to define the characteristic polynomial of the endomorphism of a finitely-generated vector space.
openaire +1 more source
Eigenvalues and the associated eigenvectors of an endomorphism of a vector space are defined and studied, as is the spectrum of an endomorphism. The characteristic polynomial of a matrix is considered and used to define the characteristic polynomial of the endomorphism of a finitely-generated vector space.
openaire +1 more source