• 제목/요약/키워드: generalized inverses

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COMPUTING DETERMINANTAL REPRESENTATION OF GENERALIZED INVERSES

  • Stanimirovic, Predrag-S.;Tasic, Milan-B.
    • Journal of applied mathematics & informatics
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    • 제9권2호
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    • pp.519-529
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    • 2002
  • We investigate implementation of the determinantal representation of generalized inverses for complex and rational matrices in the symbolic package MATHEMATICA. We also introduce an implementation which is applicable to sparse matrices.

NONNEGATIVE INTEGRAL MATRICES HAVING GENERALIZED INVERSES

  • Kang, Kyung-Tae;Beasley, LeRoy B.;Encinas, Luis Hernandez;Song, Seok-Zun
    • 대한수학회논문집
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    • 제29권2호
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    • pp.227-237
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    • 2014
  • For an $m{\times}n$ nonnegative integral matrix A, a generalized inverse of A is an $n{\times}m$ nonnegative integral matrix G satisfying AGA = A. In this paper, we characterize nonnegative integral matrices having generalized inverses using the structure of nonnegative integral idempotent matrices. We also define a space decomposition of a nonnegative integral matrix, and prove that a nonnegative integral matrix has a generalized inverse if and only if it has a space decomposition. Using this decomposition, we characterize nonnegative integral matrices having reflexive generalized inverses. And we obtain conditions to have various types of generalized inverses.

A MATRIX INEQUALITY ON SCHUR COMPLEMENTS

  • YANG ZHONG-PENG;CAO CHONG-GUANG;ZHANG XIAN
    • Journal of applied mathematics & informatics
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    • 제18권1_2호
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    • pp.321-328
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    • 2005
  • We investigate a matrix inequality on Schur complements defined by {1}-generalized inverses, and obtain simultaneously a necessary and sufficient condition under which the inequality turns into an equality. This extends two existing matrix inequalities on Schur complements defined respectively by inverses and Moore-Penrose generalized inverses (see Wang et al. [Lin. Alg. Appl., 302-303(1999)163-172] and Liu and Wang [Lin. Alg. Appl., 293(1999)233-241]). Moreover, the non-uniqueness of $\{1\}$-generalized inverses yields the complicatedness of the extension.

FORWARD ORDER LAW FOR THE GENERALIZED INVERSES OF MULTIPLE MATRIX PRODUCT

  • Xiong, Zhipin;Zheng, Bing
    • Journal of applied mathematics & informatics
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    • 제25권1_2호
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    • pp.415-424
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    • 2007
  • The generalized inverses have many important applications in the aspects of theoretic research and numerical computations and therefore they were studied by many authors. In this paper we get some necessary and sufficient conditions of the forward order law for {1}-inverse of multiple matrices products $A\;=\;A_1A_2{\cdots}A_n$ by using the maximal rank of generalized Schur complement.

GENERALIZED INVERSES IN NUMERICAL SOLUTIONS OF CAUCHY SINGULAR INTEGRAL EQUATIONS

  • Kim, S.
    • 대한수학회논문집
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    • 제13권4호
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    • pp.875-888
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    • 1998
  • The use of the zeros of Chebyshev polynomial of the first kind $T_{4n+4(x}$ ) and second kind $U_{2n+1}$ (x) for Gauss-Chebyshev quad-rature and collocation of singular integral equations of Cauchy type yields computationally accurate solutions over other combinations of $T_{n}$ /(x) and $U_{m}$(x) as in [8]. We show that the coefficient matrix of the overdetermined system has the generalized inverse. We estimate the residual error using the norm of the generalized inverse.e.

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THE APPLICATIONS OF ADDITIVE MAP PRESERVING IDEMPOTENCE IN GENERALIZED INVERSE

  • Yao, Hongmei;Fan, Zhaobin;Tang, Jiapei
    • Journal of applied mathematics & informatics
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    • 제26권3_4호
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    • pp.541-547
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    • 2008
  • Suppose R is an idempotence-diagonalizable ring. Let n and m be two arbitrary positive integers with $n\;{\geq}\;3$. We denote by $M_n(R)$ the ring of all $n{\times}n$ matrices over R. Let ($J_n(R)$) be the additive subgroup of $M_n(R)$ generated additively by all idempotent matrices. Let ($D=J_n(R)$) or $M_n(R)$. In this paper, by using an additive idem potence-preserving result obtained by Coo (see [4]), I characterize (i) the additive preservers of tripotence from D to $M_m(R)$ when 2 and 3 are units of R; (ii) the additive preservers of inverses (respectively, Drazin inverses, group inverses, {1}-inverses, {2}-inverses, {1, 2}-inverses) from $M_n(R)$ to $M_n(R)$ when 2 and 3 are units of R.

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EXPLICIT SOLUTIONS OF INFINITE QUADRATIC PROGRAMS

  • Sivakumar, K.C.;Swarna, J.Mercy
    • Journal of applied mathematics & informatics
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    • 제12권1_2호
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    • pp.211-218
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    • 2003
  • Let H be a Hilbert space, X be a real Banach space, A : H \longrightarrow X be an operator with D(A) dense in H, G: H \longrightarrow H be positive definite, $\chi$ $\in$ D(A) and b $\in$ H. Consider the quadratic programming problem: QP: Minimize $\frac{1}{2}$〈p, $\chi$〉 + 〈$\chi$, G$\chi$〉 subject to A$\chi$= b In this paper, we obtain an explicit solution to the above problem using generalized inverses.

How to Characterize Equalities for the Generalized Inverse $A^{(2)}_{T,S}$ of a Matrix

  • LIU, YONGHUI
    • Kyungpook Mathematical Journal
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    • 제43권4호
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    • pp.605-616
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    • 2003
  • In this paper, some rank equalities related to generalized inverses $A^{(2)}_{T,S}$ of a matrix are presented. As applications, a variety of rank equalities related to the M-P inverse, the Drazin inverse, the group inverse, the weighted M-P inverse, the Bott-Duffin inverse and the generalized Bott-Duffin inverse are established.

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AN ITERATIVE METHOD FOR ORTHOGONAL PROJECTIONS OF GENERALIZED INVERSES

  • Srivastava, Shwetabh;Gupta, D.K.
    • Journal of applied mathematics & informatics
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    • 제32권1_2호
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    • pp.61-74
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    • 2014
  • This paper describes an iterative method for orthogonal projections $AA^+$ and $A^+A$ of an arbitrary matrix A, where $A^+$ represents the Moore-Penrose inverse. Convergence analysis along with the first and second order error estimates of the method are investigated. Three numerical examples are worked out to show the efficacy of our work. The first example is on a full rank matrix, whereas the other two are on full rank and rank deficient randomly generated matrices. The results obtained by the method are compared with those obtained by another iterative method. The performance measures in terms of mean CPU time (MCT) and the error bounds for computing orthogonal projections are listed in tables. If $Z_k$, k = 0,1,2,... represents the k-th iterate obtained by our method then the sequence of the traces {trace($Z_k$)} is a monotonically increasing sequence converging to the rank of (A). Also, the sequence of traces {trace($I-Z_k$)} is a monotonically decreasing sequence converging to the nullity of $A^*$.