singular matrix

(noun)

A matrix that has no inverse. 

Related Terms

  • identity matrix
  • inverse matrix
  • matrix

Examples of singular matrix in the following topics:

  • The Inverse of a Matrix

    • The matrix $B$ is the inverse of the matrix $A$ if when multiplied together, $A\cdot B$ or $B\cdot A$ gives the identity matrix.
    • The definition of an inverse matrix is based on the identity matrix $[I]$, and it has already been established that only square matrices have an associated identity matrix.
    • When multiplying this mystery matrix by our original matrix, the result is $[I]$.
    • If an inverse has been found, then a quick check to be sure it is correct is to multiply it by the original matrix and see if the identify matrix results:
    • This is called a singular matrix.
  • Orthogonal decomposition of rectangular matrices

    • It is called the Singular Value Decomposition and works for any matrix whatsoever.
    • Any matrix $A \in \mathbf{R}^{n \times m}$ can be factored as
    • $\Lambda \in \mathbf{R}^{{n \times m}}$ is a rectangular matrix with the singular values on its main diagonal and zero elsewhere.
    • A direct approach to the SVD, attributed to the physicist Lanczos, is to make a symmetric matrix out of the rectangular matrix $A$ as follows: Let
    • This is the singular value decomposition.
  • Cell Types in Bones

    • Osteoblasts, which do not divide, synthesize and secrete the collagen matrix and calcium salts.
    • As the secreted matrix surrounding the osteoblast calcifies, the osteoblast becomes trapped within it.
    • Osteocytes maintain the mineral concentration of the matrix via the secretion of enzymes.
    • They are able to communicate with each other and receive nutrients via long cytoplasmic processes that extend through canaliculi (singular = canaliculus), channels within the bone matrix.
    • When osteoblasts get trapped within the calcified matrix, their structure and function changes; they become osteocytes.
  • Two-mode SVD analysis

    • Singular value decomposition (SVD) is one method of identifying the factors underlying two-mode (valued) data.
    • To illustrate SVD, we have input a matrix of 23 major donors (those who gave a combined total of more than $1,000,000 to five or more campaigns) by 44 California ballot initiatives.
    • Figure 17.6 shows the "singular values" extracted from the rectangular donor-by-initiative matrix using Tools>2-Mode Scaling>SVD.
    • The "singular values" are analogous to "eigenvalues" in the more common factor and components scaling techniques.
    • Two-mode scaling of California donors and initiatives by Single Value Decomposition: Singular values
  • Number

    • The Latin has two Numbers,—the Singular and Plural.
    • The Singular denotes one object, the Plural, more than one.
  • Eigenvectors and Orthogonal Projections

    • Let $\mathbf{v}_i$ denote the ith column of the matrix $V$ .
    • (The same argument applies to $U$ of course. ) The outer product $\mathbf{v}_i \mathbf{v}_i ^T$ is an $m \times m$ matrix.
    • It is easy to see that the action of this matrix on a vector is to project that vector onto the one-dimensional subspace spanned by $\mathbf{v}_i$ :
    • On the other hand, if we only include the terms in the sum associated with the $r$ nonzero singular values, then we get a projection operator onto the non-null space (which is the row space).
  • What is a Matrix?

    • The matrix has a long history of application in solving linear equations.
    • A matrix with m rows and n columns is called an m × n matrix or $m$-by-$n$ matrix, while m and n are called its dimensions.
    • A matrix which has the same number of rows and columns is called a square matrix.
    • In some contexts, such as computer algebra programs, it is useful to consider a matrix with no rows or no columns, called an empty matrix.
    • Each element of a matrix is often denoted by a variable with two subscripts.
  • Subject-Verb Agreement

    • If only one person is in the subject, it's singular.
    • Collective nouns (which refer to a group of beings or things as a single unit) are singular, and so take singular verbs.
    • They take a singular verb form.
    • Amounts take singular verbs because they are treated as units, which are singular nouns.
    • Sums and products take singular verbs in mathematical equations.
  • Intercellular Junctions

    • The extracellular matrix allows cellular communication within tissues through conformational changes that induce chemical signals, which ultimately transform activities within the cell.
    • This transport primarily uses the vascular tissues (xylem and phloem); however, there are also structural modifications called plasmodesmata (singular: plasmodesma) that facilitate direct communication in plant cells.
  • Matrix Structure

    • The matrix structure organizes employees by function and output to capitalize on strengths and improve efficiency.
    • The matrix structure groups employees by both function and product .
    • Balanced or functional matrix: A project manager is assigned to oversee the project.
    • Strong or project matrix: A project manager is primarily responsible for the project.
    • Representing matrix organizations visually has challenged managers ever since the matrix management structure was invented.
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