Optimal error bounds for non-expansive fixed-point iterations in normed spaces

Mathematical Programming - This paper investigates optimal error bounds and convergence rates for general Mann iterations for computing fixed-points of non-expansive maps. We look for iterations...     

Round-off error analysis of iterations for large linear systems

Summary We deal with the rounding error analysis of successive approximation iterations for the solution of large linear systemsA _x =b. We prove that Jacobi, Richardson, Gauss-Seidel and SOR iterations arenumerically stable wheneverA=A ^*>0 andA has PropertyA. This means that the computed resultx _k approximates the exact solution with relative error of order A·A ^–1 where is the relative computer precision. However with the exception of Gauss-Seidel iteration the residual vector Ax _k –b is of order A² A ^–1 and hence the remaining three iterations arenot well-behaved.This work was partly done during the author's visit at Carnegie-Mellon University and it was supported in part by the Office of Naval Research under Contract N00014-76-C-0370; NR 044-422 and by the National Science Foundation under Grant MCS75-222-55     

B-Nekrasov matrices and error bounds for linear complementarity problems

The class of B-Nekrasov matrices is a subclass of P-matrices that contains Nekrasov Z-matrices with positive diagonal entries as well as B-matrices. Error bounds for the linear complementarity problem when the involved matrix is a B-Nekrasov matrix are presented. Numerical examples show the sharpness and applicability of the bounds.     

New improved error bounds for the linear complementarity problem

New local and global error bounds are given for both nonmonotone and monotone linear complementarity problems. Comparisons of various residuals used in these error bounds are given. A possible candidate for a best error bound emerges from our comparisons as the sum of two natural residuals.This material is based on research supported by Air Force Office of Scientific Research Grant AFOSR-89-0410 and National Science Foundation Grant CCR-9101801.     

Component-wise perturbation analysis and error bounds for linear least squares solutions

Perturbation bounds for the linear least squares problem min _x Ax –b₂ corresponding tocomponent-wise perturbations in the data are derived. These bounds can be computed using a method of Hager and are often much better than the bounds derived from the standard perturbation analysis. In particular this is true for problems where the rows ofA are of widely different magnitudes. Generalizing a result by Oettli and Prager, we can use the bounds to compute a posteriori error bounds for computed least squares solutions.     

A posteriors error bounds for inhomogeneous linear dirichlet and neumann problems

Second-order inhomogeneous linear Dirichlet and Neumann problems in divergent form on a simply-connected to-dimensional domain with Lipschitz-continious boundary of finite length are considered. Conjugate problems, that is, a pair of one Dirichlet and one Neumann problem the minima of energies of which add to a known constant, are introduced. From the conceppt of conjugate problems, two-sided bounds for the energy of the exact solution of any given Dirichlet or Neumann problem are constructed. These two-sided bounds for the energy at the exact solution are in turn used to obtain easily calculable a posteriori error bounds for the norm of the difference of the approximate and exact solutions of the given problem     

Sufficient conditions for error bounds and linear regularity in Banach spaces

In this paper, we study error bounds for lower semicontinuous functions defined on Banach space and linear regularity for finitely many closed subset in Banach spaces. By using Clarke＇s subd- ifferentials and Ekeland variational principle, we establish several sufficient conditions ensuring error bounds and linear regularity in Banach spaces.     

Some new error bounds for linear complementarity problems of H-matrices

Some new error bounds for linear complementarity problems of H-matrices are presented based on the preconditioned technique. Numerical examples show that these bounds are better than some existing ones.     

An error bound for fixed-point iterations

We derive an error bound for fixed-point iterationsx _n+1=f(x _n ) by using monotonicity in the sense of [2]. The new bound is preferable to the classical one which bounds the error in terms of the Lipschitz constant off.     

New error bounds for the linear complementarity problem with an SB-matrix

Error bounds for SB-matrices linear complementarity problems are given in the paper (Dai et al., Numer Algorithms 61:121–139, 2012). In this paper, new error bounds for the linear complementarity problem when the matrix involved is an SB-matrix are presented and some sufficient conditions that new bounds are sharper than those of the previous paper under certain assumptions are provided. New perturbation bounds of SB-matrices linear complementarity problems are also considered.     

Perturbation analysis of global error bounds for systems of linear inequalities

This paper studies the existence of a uniform global error bound when a system of linear inequalities is under local arbitrary perturbations. Specifically, given a possibly infinite system of linear inequalities satisfying the Slater’s condition and a certain compactness condition, it is shown that for sufficiently small arbitrary perturbations the perturbed system is solvable and there exists a uniform global error bound if and only if the original system is bounded or its homogeneous system has a strict solution. Received: April 12, 1998 / Accepted: February 11, 2000?Published online July 20, 2000     

Note on error bounds for linear complementarity problems of Nekrasov matrices

Numerical Algorithms - García-Esnaola and Peña (Numer. Algor. 67, 655–667, 2014) presented an error bound involving a parameter for linear complementarity problems of Nekrasov...     

Improved componentwise verified error bounds for least squares problems and underdetermined linear systems

Recently Miyajima presented algorithms to compute componentwise verified error bounds for the solution of full-rank least squares problems and underdetermined linear systems. In this paper we derive simpler and improved componentwise error bounds which are based on equalities for the error of a given approximate solution. Equalities are not improvable, and the expressions are formulated in a way that direct evaluation yields componentwise and rigorous estimates of good quality. The computed bounds are correct in a mathematical sense covering all sources of errors, in particular rounding errors. Numerical results show a gain in accuracy compared to previous results.     

On the asymptotic optimality of error bounds for some linear complementarity problems

Numerical Algorithms - We introduce strong B-matrices and strong B-Nekrasov matrices, for which some error bounds for linear complementarity problems are analyzed. In particular, it is proved that...     

An improvement of the error bounds for linear complementarity problems of Nekrasov matrices

A new error bound for the linear complementarity problems, which involves a parameter, is given when the involved matrices are Nekrasov matrices. It is shown that there exists an optimal value of the parameter such that the new bound is sharper than that provided by Li et al. (Numer Algor. 2017;74:997–1009). Numerical examples are given to illustrate the corresponding results.     

A comparison of error bounds for linear complementarity problems of H-matrices

We give new error bounds for the linear complementarity problem when the involved matrix is an H-matrix with positive diagonals. We find classes of H-matrices for which the new bounds improve considerably other previous bounds. We also show advantages of these new bounds with respect the computational cost. A new perturbation bound of H-matrices linear complementarity problems is also presented.     

Complexity bounds for approximately solving discounted MDPs by value iterations

For an infinite-horizon discounted Markov decision process with a finite number of states and actions, this note provides upper bounds on the number of operations required to compute an approximately optimal policy by value iterations in terms of the discount factor, spread of the reward function, and desired closeness to optimality. One of the provided upper bounds on the number of iterations has the property that it is a non-decreasing function of the value of the discount factor.