Library

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 22 (1982), S. 288-303 
    ISSN: 1436-4646
    Keywords: TheLDL T Factorization ; Symmetric Indefinite Matrices ; Symmetric Additions of Rows and Columns ; Partial Pivoting Strategies
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In an earlier paper [6] it is shown how the use of symmetric additions of rows and columns enables a stableLDL T factorization of symmetric indefinite matrices. In this paper we describe partial pivoting strategies. These strategies are faster than the complete pivoting strategies that were introduced in the first paper. Numerical experiments are included. The results show that some of the new strategies share the stable behaviour of complete pivoting while running almost as fast as the well-known Cholesky decomposition.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Annals of operations research 46-47 (1993), S. 9-60 
    ISSN: 1572-9338
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics , Economics
    Notes: Abstract Recently, it has been observed that several nondifferentiable minimization problems share the property that the question of whether a given point is optimal can be answered by solving a certain bounded least squares problem. If the resulting residual vector,r, vanishes then the current point is optimal. Otherwise,r is a descent direction. In fact, as we shall see,r points at the steepest descent direction. On the other hand, it is customary to characterize the optimality conditions (and the steepest descent vector) of a convex nondifferentiable function via its subdifferential. Also, it is well known that optimality conditions are usually related to theorems of the alternative. One aim of our survey is to clarify the relations between these subjects. Another aim is to introduce a new type of theorems of the alternative. The new theorems characterize the optimality conditions of discretel 1 approximation problems and multifacility location problems, and provide a simple way to obtain the subdifferential and the steepest descent direction in such problems. A further objective of our review is to demonstrate that the ability to compute the steepest descent direction at degenerate dead points opens a new way for handling degeneracy in active set methods.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    Electronic Resource
    Electronic Resource
    Springer
    BIT 33 (1993), S. 262-276 
    ISSN: 1572-9125
    Keywords: 49A40 ; 65F35 ; 65K05 ; Large sparse linear systems ; Regularized minimax problems ; Row relaxation methods
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This paper presents a row relaxation method for solving the doubly regularized minimax problem $$minimize \tfrac{1}{2}\varepsilon (\parallel x\parallel _2^2 + \parallel Ax - b\parallel _\infty ^2 ) + \parallel Ax - b\parallel _\infty $$ whereε is a given positive constant. It is shown that the dual of this problem can be brought to the form $$minimize \tfrac{1}{2}\parallel A^T y\parallel _2^2 - \varepsilon b^T y + \tfrac{1}{2}(max\{ 0,\parallel y\parallel _1 - 1\} )^2 $$ and ify solves the dual thenA T y/ε solves the primal. The dual problem is solved via a row relaxation method that resembles Kaczmarz's method. This feature makes the new method suitable for problems in whichA is large, sparse, and unstructured. Another advantage is that the method is easily adapted to handle linear constraints. Numerical results are included.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 47 (1990), S. 297-299 
    ISSN: 1436-4646
    Keywords: Alternative theorems ; ℓ p -norms ; linear least-squares problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Although the theorems of the alternative have been well known for a long time, their relationship to linear least squares problems and steepest descent directions was revealed only recently. This relationship was used by the author to derive a new theorem of the alternative. The present research extends this theorem to theℓ p norm,p 〉 1.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    Electronic Resource
    Electronic Resource
    New York, NY [u.a.] : Wiley-Blackwell
    Numerical Linear Algebra with Applications 1 (1994), S. 247-263 
    ISSN: 1070-5325
    Keywords: Engineering ; Engineering General
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Mathematics
    Notes: This paper presents a row relaxation method for solving the regularized ℓp least norm problem \documentclass{article}\pagestyle{empty}\begin{document}$$ {\rm minimize}P({\rm x}) = \frac{1}{2}\varepsilon \parallel {\rm x}\parallel _{\rm 2}^{\rm 2} + \parallel A{\rm x} - {\rm b}\parallel _p^p /p $$\end{document} where e and p are positive constants, 1〈p〈∞ The interest that we have in this problem lies in the observation that for small values of E the minimizer of P (X ) is a good substitute for a minimizer of the unregularized problem \documentclass{article}\pagestyle{empty}\begin{document}$$ {\rm minimize }U({\rm x}) = \parallel A{\rm x} - {\rm b}\parallel _p^p /p $$\end{document} It is shown that the dual of the regularized problem has the form \documentclass{article}\pagestyle{empty}\begin{document}$$ {\rm minimize }D({\rm y}) = {\rm b}^T {\rm y} - \frac{1}{2}\varepsilon \parallel A^T {\rm y/}\varepsilon \parallel _2^2 - \parallel {\rm y}\parallel _q^q /q $$\end{document} where q = p /(p - 1). Moreover, if y solves the dual problem then X = ATy/∊ solves the primal problem and P(ATy/∊) = D(y). Maximizing the dual objective function by changing one variable at a time results in a row relaxation method that resembles Kaczmarz's method. This feature makes the new method suitable for solving large sparse C, problems that arise in computerized tomography, geophysics, and groundwater hydrology. Numerical experiments illustrate the feasibility of our ideas.
    Additional Material: 5 Tab.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    Electronic Resource
    Electronic Resource
    New York, NY [u.a.] : Wiley-Blackwell
    Numerical Linear Algebra with Applications 5 (1998), S. 79-99 
    ISSN: 1070-5325
    Keywords: bidiagonalization ; least squares ; minimum norm solution ; rank-deficient ; regularization ; Riley-Golub iteration ; singular value decomposition ; Engineering ; Numerical Methods and Modeling
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Mathematics
    Notes: In this paper we consider the solution of linear least squares problems          minx∥Ax - b∥22 where the matrix A ∊ Rm × n is rank deficient. Put p = min{m, n}, let σi, i = 1, 2,…, p, denote the singular values of A, and let ui and vi denote the corresponding left and right singular vectors. Then the minimum norm solution of the least squares problem has the form x* = ∫ri = 1(uTib/σi)vi, where r ≤ p is the rank of A.The Riley-Golub iteration,          xk + 1 = arg minx{∥Ax - b∥22 + λ∥x - xk∥22} converges to the minimum norm solution if x0 is chosen equal to zero. The iteration is implemented so that it takes advantage of a bidiagonal decomposition of A. Thus modified, the iteration requires only O(p) flops (floating point operations). A further gain of using the bidiagonalization of A is that both the singular values σi and the scalar products uTib can be computed at marginal extra cost. Moreover, we determine the regularization parameter, λ, and the number of iterations, k, in a way that minimizes the difference x* - xk with respect to a certain norm. Explicit rules are derived for calculating these parameters.One advantage of our approach is that the numerical rank can be easily determined by using the singular values. Furthermore, by the iterative procedure, x* is approximated without computing the singular vectors of A. This gives a fast and reliable method for approximating minimum norm solutions of well-conditioned rank-deficient least squares problems. Numerical experiments illustrate the viability of our ideas, and demonstrate that the new method gives more accurate approximations than an approach based on a QR decomposition with column pivoting. © 1998 John Wiley & Sons, Ltd.
    Additional Material: 2 Ill.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...