In this paper, we investigate and analyze the nonconvex variational inequalities introduced by Noor in (Optim. Lett. 3: 411-418, 2009) and (Comput. Math. Model. 21: 97-108, 2010) and prove that the algorithms and resu...
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In this paper, we investigate and analyze the nonconvex variational inequalities introduced by Noor in (Optim. Lett. 3: 411-418, 2009) and (Comput. Math. Model. 21: 97-108, 2010) and prove that the algorithms and results in the above mentioned papers are not valid. To overcome the problems in the above cited papers, we introduce and consider a new class of variational inequalities, named regularized nonconvex variational inequalities, instead of the class of nonconvex variational inequalities introduced in the above mentioned papers. We also consider a class of nonconvex Wiener-Hopf equations and establish the equivalence between the regularized nonconvex variational inequalities and the fixed point problems as well as the nonconvex Wiener-Hopf equations. By using the obtained equivalence formulations, we prove the existence of a unique solution for the regularized nonconvex variational inequalities and propose some projection iterative schemes for solving the regularized nonconvex variational inequalities. We also study the convergence analysis of the suggested iterative schemes under some certain conditions.
The precession of a pendulum with a Cardan suspension point oscillating under an external periodic force is investigated. The parameters of precession versus the pendulum geometric characteristics and the external for...
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The precession of a pendulum with a Cardan suspension point oscillating under an external periodic force is investigated. The parameters of precession versus the pendulum geometric characteristics and the external force frequency are studied both theoretically and numerically.
A novel, fast and easy single sample measurement has been developed based upon temperature dependence of equilibrium constant in order to determine the enthalpy and entropy changes of a complexation reaction using spe...
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A novel, fast and easy single sample measurement has been developed based upon temperature dependence of equilibrium constant in order to determine the enthalpy and entropy changes of a complexation reaction using spectrophotometric temperature titration. The method can be used in determination of the formation constant and thermodynamic parameters of the solutions that there are difficulties in their titration where volatile compounds are studying. Knowledge of component spectra is not required for the analysis. The formation constants of the interactions of -di and tri-brominated meso-tetraphenylporphyrins, and meso-tetrakis(4-methylphenyl) and (4-methoxyphenyl) porphyrins with Me2SnCl2 and Bu2SnCl2, have been determined in range of 0-25 A degrees C utilizing van't Hoff relation, mass balance and equilibrium constant equations by an iterative least squares method with Delta H (0) as adjustable parameter. The outputs of analysis are the equilibrium constants, ligand and adduct spectral profiles, their concentrations as a function of temperature, the adjusted values of the standard enthalpy Delta H (0), and entropy Delta S (0) changes. The order of formation constants of the resulting 1:1 complexes decreased with increasing number of bromide substituents and increased with adding methyl and methoxy groups, and vary as H2T(4-CH3O)PP > H2T(4-CH3)PP > H2TPP > H2TPPBr2 > H2TPPBr3 and Me2SnCl2 > Bu2SnCl2.
We consider convolution sampling and reconstruction of signals in certain reproducing kernel subspaces of L-p, 1 <= p <= infinity. We show that signals in those subspaces could be stably reconstructed from their...
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We consider convolution sampling and reconstruction of signals in certain reproducing kernel subspaces of L-p, 1 <= p <= infinity. We show that signals in those subspaces could be stably reconstructed from their convolution samples taken on a relatively separated set with small gap. Exponential convergence and error estimates are established for the iterative approximation-projection reconstruction algorithm.
The range-based localization problem often arises in TOA or RSSI based position estimation schemes. It is well-known that such a localization problem can be formulated as a nonlinear least-squares (NLS) estimation pro...
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ISBN:
(纸本)9781424423538
The range-based localization problem often arises in TOA or RSSI based position estimation schemes. It is well-known that such a localization problem can be formulated as a nonlinear least-squares (NLS) estimation problem. In this paper, we formulate the problem as a constrained optimization problem, which is equivalent to the general NLS problem. By using a greedy optimization strategy, we derive a simple iterative algorithm with closed-form expressions for the NLS localization, which can be implemented in a distributed way. Simulation results show that the localization performance of the proposed localization algorithm is very close to the Cramer-Rao lower bound.
Newton's method applied to a quadratic polynomial converges rapidly to a root for almost all starting points and almost all coefficients. This can be understood in terms of an associative binary operation arising ...
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Newton's method applied to a quadratic polynomial converges rapidly to a root for almost all starting points and almost all coefficients. This can be understood in terms of an associative binary operation arising from 2 x 2 matrices. Here we develop an analogous theory based on 3 x 3 matrices which yields a two-variable generally convergent algorithm for cubics.
Conversely to the fully connected case, it has been proved in theory that interference alignment (IA) can be achievable in a partially connected multi- cell multiple input and multiple output (MIMO) interfering broadc...
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Conversely to the fully connected case, it has been proved in theory that interference alignment (IA) can be achievable in a partially connected multi- cell multiple input and multiple output (MIMO) interfering broadcast channels (IBC) network of arbitrary size efficiently. For this applicable significance, based on the L-interfering MIMO IBC model, we present three iterative IA algorithms to solve the alignment problem for this type of model in this paper. Then we discuss the feasibility conditions and the computational complexity of the algorithms. Simulations show that, with a finite antenna number per transmitter and receiver pair between base station (BS) and user, the proposed algorithms can achieve the optimal degrees of freedom (DoF) and can be applied to a partially connected MIMO IBC network with arbitrary number of cells and users per cell.
Compressive sensing (CS), is a framework which points us a promising way of not measuring N-dimensional signals directly, but rather a set of related measurements, which a linear combination of the original underlying...
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Compressive sensing (CS), is a framework which points us a promising way of not measuring N-dimensional signals directly, but rather a set of related measurements, which a linear combination of the original underlying N-dimensional signal. However, the traditional CS reconstruction methods use l1-norm optimization which usually gives poor performance on 2D signal or only suit for specific natural image. In this paper, an iterative weighing algorithm for image reconstruction in CS is proposed. According to the sparsity of last iteration, the algorithmiteratively refines the weighting coefficients to enhance the sparsity of the reconstruction results until the convergence is reached. The experiments for natural image and remote sensing image demonstrate that the proposed method can outperforms the traditional CS framework in image reconstruction in the sense that the PSNR of reconstruction image improve over 2dB in the average.
iterative Multilateration is widely used in multi-hop wireless sensor networks, where few anchor nodes are deployed in a large area. To compensate for the lackness of robustness in iterative localization when there ar...
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iterative Multilateration is widely used in multi-hop wireless sensor networks, where few anchor nodes are deployed in a large area. To compensate for the lackness of robustness in iterative localization when there are sufficient numbers of reference nodes around some blind node, a new estimation algorithm called TTSL is proposed in this paper. Firstly, we obtain all samples using trilateration from every combination of three reference nodes. Then the location of the blind node is estimated. Simulation experiments show that the proposed TTSL algorithm can reduce the position error sufficiently.
This study explores the convergence property of the interference leakage minimization algorithm (ILMA) proposed by S. W. Peters and R. W. Heath Jr.. Among the cooperative interference alignment (IA) schemes, we specia...
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ISBN:
(纸本)9781467331227
This study explores the convergence property of the interference leakage minimization algorithm (ILMA) proposed by S. W. Peters and R. W. Heath Jr.. Among the cooperative interference alignment (IA) schemes, we specialize the ILMA for its better feasibility in real applications than others. However, ILMA cannot converge to a global minimum because of its unitary constraints. As an iterative algorithm, no guarantee of global convergence means the tendency towards system instability. Up to the present, most efforts on cooperative IA are focused on pursuing high sum rate or low error floor. None of existing works looks into the convergence issue. In this paper, we explore the global convergence of ILMA. Specifically, we devise a constraint relaxation version of ILMA, named rILMA. Except that rILMA inherits the benefits from ILMA, mathematical proofs given in this paper also show that rILMA converges to a global minimum. This exploration directly results in the improvement of performance. Numerical simulations show that rILMA outperforms Max-SINR and ILMA in sum rate performance.
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