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For help on the process, see Wikipedia:How to draw a diagram with Inkscape. This tutorial aims to instruct a beginner on the basic principles of vector graphics using Microsoft Word (Office 97 or later). The basic principles are the same in other drawing programs such as CorelDraw or the free and open source OpenOffice.org.
Let A be a square n × n matrix with n linearly independent eigenvectors q i (where i = 1, ..., n).Then A can be factored as = where Q is the square n × n matrix whose i th column is the eigenvector q i of A, and Λ is the diagonal matrix whose diagonal elements are the corresponding eigenvalues, Λ ii = λ i.
The eigenspaces of T always form a direct sum. As a consequence, eigenvectors of different eigenvalues are always linearly independent. Therefore, the sum of the dimensions of the eigenspaces cannot exceed the dimension n of the vector space on which T operates, and there cannot be more than n distinct eigenvalues. [d]
Two Hermitian matrices commute if their eigenspaces coincide. In particular, two Hermitian matrices without multiple eigenvalues commute if they share the same set of eigenvectors. This follows by considering the eigenvalue decompositions of both matrices. Let and be two Hermitian matrices.
Draw the structure in your molecule editor, and save it as a Windows Metafile (.wmf), Enhanced Metafile (.emf), or Encapsulated Postscript (.eps). Open the saved file in Inkscape. Resize the picture about 400%, then click to File→Document Properties→Fit page to selection. Select the molecule and click Path→Object to path.
An n × n matrix A is diagonalizable if and only if the sum of the dimensions of the eigenspaces is n. Or, equivalently, if and only if A has n linearly independent eigenvectors. Not all matrices are diagonalizable; matrices that are not diagonalizable are called defective matrices. Consider the following matrix:
In mathematics, spectral graph theory is the study of the properties of a graph in relationship to the characteristic polynomial, eigenvalues, and eigenvectors of matrices associated with the graph, such as its adjacency matrix or Laplacian matrix.
In mathematics, a self-adjoint operator on a complex vector space V with inner product , is a linear map A (from V to itself) that is its own adjoint.That is, , = , for all , ∊ V.