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3.4 Solving Matrix Equations with Inverses
... variables when they are written in the same order in each equation. The matrix X is called the variable matrix and contains the two variables in the problem. The matrix B is called the constant matrix and contains the constants from the right hand side of the matrix equation. To match the different ...
... variables when they are written in the same order in each equation. The matrix X is called the variable matrix and contains the two variables in the problem. The matrix B is called the constant matrix and contains the constants from the right hand side of the matrix equation. To match the different ...
Characterization of majorization monotone
... [17] H.Yuan, N.Khaneja, Phys. Rev. A 72, 040301 (2005). [18] H.Yuan, S. Glaser, N.Khaneja, Phys. Rev. A 76, 012316 (2007). [19] H.Yuan, R. Zeier, N.Khaneja, Phys. Rev. A 77, 032340 (2008). [20] T. Hornung, S. Gordienko, R. de Vivie-Riedle, and B. Verhaar, Phys. Rev. A 66, 043607 (2002). [21] S. E. S ...
... [17] H.Yuan, N.Khaneja, Phys. Rev. A 72, 040301 (2005). [18] H.Yuan, S. Glaser, N.Khaneja, Phys. Rev. A 76, 012316 (2007). [19] H.Yuan, R. Zeier, N.Khaneja, Phys. Rev. A 77, 032340 (2008). [20] T. Hornung, S. Gordienko, R. de Vivie-Riedle, and B. Verhaar, Phys. Rev. A 66, 043607 (2002). [21] S. E. S ...
RESEARCH STATEMENT
... My research interests fall into the general category of numerical analysis, but are more specifically contained in structured matrices. The main objects that I am interested in studying are Leja ordering for Cauchy-Vandermonde matrices and a fast inversion algorithm for a Laurent-Vandermonde system. ...
... My research interests fall into the general category of numerical analysis, but are more specifically contained in structured matrices. The main objects that I am interested in studying are Leja ordering for Cauchy-Vandermonde matrices and a fast inversion algorithm for a Laurent-Vandermonde system. ...
ROW REDUCTION AND ITS MANY USES
... no free variables, and every variable is completely determined, hence if a solution exists it is unique. Finally, invertibility of A is equivalent to Ax = b having a unique solution for every b, hence equivalent to Ae having pivots in every row and column. The only way this is possible is if A is a ...
... no free variables, and every variable is completely determined, hence if a solution exists it is unique. Finally, invertibility of A is equivalent to Ax = b having a unique solution for every b, hence equivalent to Ae having pivots in every row and column. The only way this is possible is if A is a ...
Jordan normal form
In linear algebra, a Jordan normal form (often called Jordan canonical form)of a linear operator on a finite-dimensional vector space is an upper triangular matrix of a particular form called a Jordan matrix, representing the operator with respect to some basis. Such matrix has each non-zero off-diagonal entry equal to 1, immediately above the main diagonal (on the superdiagonal), and with identical diagonal entries to the left and below them. If the vector space is over a field K, then a basis with respect to which the matrix has the required form exists if and only if all eigenvalues of the matrix lie in K, or equivalently if the characteristic polynomial of the operator splits into linear factors over K. This condition is always satisfied if K is the field of complex numbers. The diagonal entries of the normal form are the eigenvalues of the operator, with the number of times each one occurs being given by its algebraic multiplicity.If the operator is originally given by a square matrix M, then its Jordan normal form is also called the Jordan normal form of M. Any square matrix has a Jordan normal form if the field of coefficients is extended to one containing all the eigenvalues of the matrix. In spite of its name, the normal form for a given M is not entirely unique, as it is a block diagonal matrix formed of Jordan blocks, the order of which is not fixed; it is conventional to group blocks for the same eigenvalue together, but no ordering is imposed among the eigenvalues, nor among the blocks for a given eigenvalue, although the latter could for instance be ordered by weakly decreasing size.The Jordan–Chevalley decomposition is particularly simple with respect to a basis for which the operator takes its Jordan normal form. The diagonal form for diagonalizable matrices, for instance normal matrices, is a special case of the Jordan normal form.The Jordan normal form is named after Camille Jordan.