We introduce Kirk-multistep-SP and Kirk-S iterative algorithms and we prove some convergence and stability results for these iterative algorithms. Since these iterative algorithms are more general than some other iter...
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We introduce Kirk-multistep-SP and Kirk-S iterative algorithms and we prove some convergence and stability results for these iterative algorithms. Since these iterative algorithms are more general than some other iterative algorithms in the existing literature, our results generalize and unify some other results in the literature.
Let E be a Banach space, C a nonempty closed convex subset of E, f : C -> C a contraction, and Ti : C -> C a nonexpansive mapping with nonempty F := boolean AND(N)(i=1) Fix(Ti), where N >= 1 is an integer and...
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Let E be a Banach space, C a nonempty closed convex subset of E, f : C -> C a contraction, and Ti : C -> C a nonexpansive mapping with nonempty F := boolean AND(N)(i=1) Fix(Ti), where N >= 1 is an integer and is the set of fixed points of Ti. Let {x(n)} be the sequence defined by x(t)(n) = tf (x(t)(n)) + (1 0- t)Tn+NTn+N-1 center dot center dot center dot T(n+1)x(t)(n) (0 < t < 1). First, it is shown that as t -> 0, the sequence {x(t)(n)} converges strongly to a solution in F of certain variational inequality provided E is reflexive and has a weakly sequentially continuous duality mapping. Then it is proved that the iterative algorithm x(n+1) = lambda(n+1)f (x(n)) + (1 - lambda(n+1))T(n+1)x(n) (n >= 0) converges strongly to a solution in F of certain variational inequality in the same Banach space provided the sequence f An I satisfies certain conditions and the sequence {x(n)} is weakly asymptotically regular. Applications to the convex feasibility problem are included.
In this article, we apply Fourier transform to convert a nonlinear problem to a suitable equation and then we introduce a modified homotopy perturbation to divide the above equation into some smaller and easier equati...
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In this article, we apply Fourier transform to convert a nonlinear problem to a suitable equation and then we introduce a modified homotopy perturbation to divide the above equation into some smaller and easier equations. These equations can be solved by some iterative algorithms which are constructed by modified homotopy perturbation and Adomian polynomials. As an example, we use the iterative algorithms to find the exact solution of nonlinear ordinary and partial differential equations (in abbreviated form, ODE and PDE). To show ability and validity of the presented algorithms, we solve Korteweg-de Vries (KdV) equation to approximate the exact solution with a high accuracy. Furthermore, a discussion is presented herein about the convergence of the proposed algorithms in Banach space
Based on the theory of adjoint equations, iterative algorithms for solving one class of data assimilation problems for the reconstruction of the initial condition are developed and substantiated. The iterative process...
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Based on the theory of adjoint equations, iterative algorithms for solving one class of data assimilation problems for the reconstruction of the initial condition are developed and substantiated. The iterative processes are optimized with the use of the spectral properties of control operators. The results are illustrated by the example of a quasi-local model of turbulent oceanic heat transfer.
The paper deals with numerical analysis of an inverse boundary transport problem. A class of iterative algorithms for solving this problem is considered, the algorithms convergence conditions are studied, and the conv...
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The paper deals with numerical analysis of an inverse boundary transport problem. A class of iterative algorithms for solving this problem is considered, the algorithms convergence conditions are studied, and the convergence rate estimates are derived. Numerical examples are presented.
We present two new error bounds for optimization problems over a convex set whose objective function f is either semianalytic or gamma-strictly convex, with gamma greater than or equal to 1. We then apply these error ...
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We present two new error bounds for optimization problems over a convex set whose objective function f is either semianalytic or gamma-strictly convex, with gamma greater than or equal to 1. We then apply these error bounds to analyze the rate of convergence of a wide class of iterative descent algorithms for the aforementioned optimization problem. Our analysis shows that the function sequence {f(x(k))} converges at least at the sublinear rate of k(-epsilon) for some positive constant epsilon, where k is the iteration index. Moreover, the distances from the iterate sequence {x(k)} to the set of stationary points of the optimization problem converge to zero at least sublinearly.
Regular mesh-connected arrays are shown to be isomorphic to a class of so-called regular iterative algorithms. For a wide variety of problems it is shown how to obtain appropriate iterative algorithms and then how to ...
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Regular mesh-connected arrays are shown to be isomorphic to a class of so-called regular iterative algorithms. For a wide variety of problems it is shown how to obtain appropriate iterative algorithms and then how to translate these algorithms into arrays in a systematic fashion. Several "systolic" arrays presented in the literature are shown to be specific cases of the variety of architectures that can be derived by the techniques presented here. These include arrays for Fourier Transform, Matrix Multiplication, and Sorting.
Inverse kinematics is now widely used to predict poses of human-like characters (Wang 1999). As the human skeleton is highly redundant, infinity of postures verify the imposed kinematic constraints. However, only a fe...
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Inverse kinematics is now widely used to predict poses of human-like characters (Wang 1999). As the human skeleton is highly redundant, infinity of postures verify the imposed kinematic constraints. However, only a few of those postures are natural. Energy and Jerk minimization can help choosing a pose during bipedal walking (Nicolas et al. 2007). Additional secondary tasks can be used to drive the solver to a posture that verifies a wide set of constraints. Inverse kinetics solvers (Boulic et al. 1996) are based on such a technique in order to control the position of the center of mass (denoted COM).
Carrier-blind and non-data-aided (NDA) feedforward solutions for symbol timing recovery are particularly important for initial acquisition in burst modems;the Oerder and Meyr (O&M) algorithm is perhaps the most pr...
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ISBN:
(纸本)9781424443840
Carrier-blind and non-data-aided (NDA) feedforward solutions for symbol timing recovery are particularly important for initial acquisition in burst modems;the Oerder and Meyr (O&M) algorithm is perhaps the most prominent example in this respect. Since usually operated with an oversampling rate of four, alternatives using only two samples per symbol have been suggested in the open literature. On the other hand, Moeneclaey and Batsele introduced an error detector for blind NDA recovery in feedback loops, based on only one sample per symbol. In the current paper, it is shown that this approach can be successfully applied to a feedforward solution. In this context, two estimator variants are developed exhibiting no self-noise effect for M-ary PSK schemes, whereas it turns out that this does not hold true for nonconstant modulus constellations.
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