Sentence examples for methods with error from inspiring English sources
We propose self-adaptive finite element methods with error control for solving elliptic and electromagnetic problems with discontinuous coefficients.
This bound also cuts through the upper bound from other analytical methods for the ATR problem, such as recursive decomposition algorithms (RDA)—a desirable feature when exact or approximate methods with error guarantees fail to scale.
In this paper, we introduce and study a new class of over-relaxed ( A, η, m ) -proximal point iterative methods with errors for solving general nonlinear operator equations in Hilbert spaces.
In this paper, motivated by the above results, we consider the problem of finding a common solution to the zero point problems and fixed point problems based on hybrid iterative methods with errors.
In this paper, motivated by the above results, we consider the problem of finding a common solution to the zero point problem and the fixed point problem based on Mann-type iterative methods with errors.
(1.4) Unlike iteration methods with errors of [13], our iteration process (1.4) is always well defined, that is, ({x_{n}}) is always in K if K is convex subset of E. If ({beta_{n}}={0}) for all (nge1), (1.4) becomes the explicit form as follows: x_{n+1}= 1-alpha_{n})x_{n+1}= 1-alpha_{n}n}x_{n}.
These include the robust concept exploration method with error margin indices (RCEM-EMI) and the inductive design exploration method (IDEM).
The Analytically Reduced Chemistries (ARCs) are obtained using Directed Relation Graph method with Error Propagation (DRGEP) and Quasi-Steady-State (QSS) approximation.
The method with error estimates are contained in Section three.
In this paper, we study the shrinking projection method with error introduced by Kimura [10] (see also [12, 14]).
On the other hand, Kimura [10] introduced the following iterative scheme for finding a fixed point of nonexpansive mappings by the shrinking projection method with error in a Hilbert space: (Kimura [10]).
