Stefan Maruster

According to our database1, Stefan Maruster authored at least 17 papers between 2001 and 2019.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2019
On the problem of starting points for iterative methods.
Numer. Algorithms, 2019

2018
Predetermining the number of periodic steps in multi-step Newton-like methods for solving equations and systems of equations.
Appl. Math. Comput., 2018

2017
Local convergence of generalized Mann iteration.
Numer. Algorithms, 2017

Thresholds of the Inner Steps in Multi-Step Newton Method.
Algorithms, 2017

Estimating the Local Radius of Convergence for Picard Iteration.
Algorithms, 2017

2015
On the error estimation and T-stability of the Mann iteration.
J. Comput. Appl. Math., 2015

Parallel Variants of Broyden's Method.
Algorithms, 2015

2012
Experimental Study on Parallel Methods for Solving Systems of Equations.
Proceedings of the 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2012

2011
Strong convergence of an iterative algorithm for variational inequalities in Banach spaces.
Math. Comput. Model., 2011

Strong convergence of the Mann iteration for α-demicontractive mappings.
Math. Comput. Model., 2011

Convergence in norm of modified Krasnoselski-Mann iterations for fixed points of demicontractive mappings.
Appl. Math. Comput., 2011

Optimal Inequality Factor for Ehrlich-Aberth's Method.
Proceedings of the 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2011

2010
Iterative methods for the computation of fixed points of demicontractive mappings.
J. Comput. Appl. Math., 2010

2009
On the Convex Feasibility Problem
CoRR, 2009

2007
Strong Convergence of the Projection Method in Convex Feasibility Problem.
Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2007

2005
Quasi-nonexpansivity and the Convex Feasibility Problem.
Sci. Ann. Cuza Univ., 2005

2001
The stability of gradient-like methods.
Appl. Math. Comput., 2001


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