In this paper, let T be a bounded linear operator on a complex Hilbert H. We give and prove that every p-w-hyponormal operator has Bishop's property(β) and spectral properties; Quasi-similar p-w-hyponormal operat...In this paper, let T be a bounded linear operator on a complex Hilbert H. We give and prove that every p-w-hyponormal operator has Bishop's property(β) and spectral properties; Quasi-similar p-w-hyponormal operators have equal spectra and equal essential spectra. Finally, for p-w-hyponormal operators, we give a kind of proof of its normality by use of properties of partial isometry.展开更多
Pantograph-catenary contact force provides the main basis for evaluation of current quality collection; however,the pantograph-catenary contact force is largely affected by the catenary irregularities.To analyze the c...Pantograph-catenary contact force provides the main basis for evaluation of current quality collection; however,the pantograph-catenary contact force is largely affected by the catenary irregularities.To analyze the correlated relationship between catenary irregularities and pantograph-catenary contact force,a method based on nonlinear auto-regressive with exogenous input(NARX) neural networks was developed.First,to collect the test data of catenary irregularities and contact force,the pantograph/catenary dynamics model was established and dynamic simulation was conducted using MATLAB/Simulink.Second,catenary irregularities were used as the input to NARX neural network and the contact force was determined as output of the NARX neural network,in which the neural network was trained by an improved training mechanism based on the regularization algorithm.The simulation results show that the testing error and correlation coefficient are 0.1100 and 0.8029,respectively,and the prediction accuracy is satisfactory.And the comparisons with other algorithms indicate the validity and superiority of the proposed approach.展开更多
Mehrotra's recent suggestion of a predictor corrector variant of primal dual interior point method for linear programming is currently the interior point method of choice for linear programming. In this work t...Mehrotra's recent suggestion of a predictor corrector variant of primal dual interior point method for linear programming is currently the interior point method of choice for linear programming. In this work the authors give a predictor corrector interior point algorithm for monotone variational inequality problems. The algorithm was proved to be equivalent to a level 1 perturbed composite Newton method. Computations in the algorithm do not require the initial iteration to be feasible. Numerical results of experiments are presented.展开更多
The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated...The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm.The deregularization maximized the energy of the solution,which was opposite to the Tikhonov regularization where the energy was minimized.However,combined with box-constraints,the deregularization forced the solution to be close to the binary set.It further exploited the box-constrained dichotomous coordinate descent algorithm and adapted it to the nonstationary iterative Tikhonov regularization to present an efficient detector.As a result,the worst-case and average complexity are reduced down as K2.8 and K2.5 floating point operation per second,respectively.The development improves the "efficient frontier" in multiuser detection,which is illustrated by simulation results.In addition,most operations in the detector are additions and bit-shifts.This makes the proposed technique attractive for fixed-point hardware implementation.展开更多
The lasso of Tibshirani （1996） is a least-squares problem regularized by the l1 norm. Due to the sparseness promoting property of the l1 norm, the lasso has been received much attention in recent years. In this pape...The lasso of Tibshirani （1996） is a least-squares problem regularized by the l1 norm. Due to the sparseness promoting property of the l1 norm, the lasso has been received much attention in recent years. In this paper some basic properties of the lasso and two variants of it are exploited. Moreover, the proximal method and its variants such as the relaxed proximal algorithm and a dual method for solving the lasso by iterative algorithms are presented.展开更多
Abstract Mehrotra-type predictor-corrector algorithm is one of the most effective primal-dual interior- point methods. This paper presents an extension of the recent variant of second order Mehrotra-type predictor-cor...Abstract Mehrotra-type predictor-corrector algorithm is one of the most effective primal-dual interior- point methods. This paper presents an extension of the recent variant of second order Mehrotra-type predictor-corrector algorithm that was proposed by Salahi, et a1.（2006） for linear optimization. Basedon the NT direction as Newton search direction, it is shown that the iteration-complexity bound of thealgorithm for semidefinite optimization is which is similar to that of the correspondingalgorithm for linear optimization.展开更多
This paper addresses the learning algorithm on the unit sphere.The main purpose is to present an error analysis for regression generated by regularized least square algorithms with spherical harmonics kernel.The exces...This paper addresses the learning algorithm on the unit sphere.The main purpose is to present an error analysis for regression generated by regularized least square algorithms with spherical harmonics kernel.The excess error can be estimated by the sum of sample errors and regularization errors.Our study shows that by introducing a suitable spherical harmonics kernel,the regularization parameter can decrease arbitrarily fast with the sample size.展开更多
基金Natural Science and Education Foundation of Henan Province(2007110016)
文摘In this paper, let T be a bounded linear operator on a complex Hilbert H. We give and prove that every p-w-hyponormal operator has Bishop's property(β) and spectral properties; Quasi-similar p-w-hyponormal operators have equal spectra and equal essential spectra. Finally, for p-w-hyponormal operators, we give a kind of proof of its normality by use of properties of partial isometry.
基金Project(20120009110035)supported by Specialized Research Fund for the Doctoral Program of Higher Education of ChinaProject(2011BAG01B05)supported by National Key Technology Research and Development Program of ChinaProject(2011AA110501)supported by National High-tech Research and Development Program of China
文摘Pantograph-catenary contact force provides the main basis for evaluation of current quality collection; however,the pantograph-catenary contact force is largely affected by the catenary irregularities.To analyze the correlated relationship between catenary irregularities and pantograph-catenary contact force,a method based on nonlinear auto-regressive with exogenous input(NARX) neural networks was developed.First,to collect the test data of catenary irregularities and contact force,the pantograph/catenary dynamics model was established and dynamic simulation was conducted using MATLAB/Simulink.Second,catenary irregularities were used as the input to NARX neural network and the contact force was determined as output of the NARX neural network,in which the neural network was trained by an improved training mechanism based on the regularization algorithm.The simulation results show that the testing error and correlation coefficient are 0.1100 and 0.8029,respectively,and the prediction accuracy is satisfactory.And the comparisons with other algorithms indicate the validity and superiority of the proposed approach.
文摘Mehrotra's recent suggestion of a predictor corrector variant of primal dual interior point method for linear programming is currently the interior point method of choice for linear programming. In this work the authors give a predictor corrector interior point algorithm for monotone variational inequality problems. The algorithm was proved to be equivalent to a level 1 perturbed composite Newton method. Computations in the algorithm do not require the initial iteration to be feasible. Numerical results of experiments are presented.
文摘The presented iterative multiuser detection technique was based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm.The deregularization maximized the energy of the solution,which was opposite to the Tikhonov regularization where the energy was minimized.However,combined with box-constraints,the deregularization forced the solution to be close to the binary set.It further exploited the box-constrained dichotomous coordinate descent algorithm and adapted it to the nonstationary iterative Tikhonov regularization to present an efficient detector.As a result,the worst-case and average complexity are reduced down as K2.8 and K2.5 floating point operation per second,respectively.The development improves the "efficient frontier" in multiuser detection,which is illustrated by simulation results.In addition,most operations in the detector are additions and bit-shifts.This makes the proposed technique attractive for fixed-point hardware implementation.
文摘The lasso of Tibshirani （1996） is a least-squares problem regularized by the l1 norm. Due to the sparseness promoting property of the l1 norm, the lasso has been received much attention in recent years. In this paper some basic properties of the lasso and two variants of it are exploited. Moreover, the proximal method and its variants such as the relaxed proximal algorithm and a dual method for solving the lasso by iterative algorithms are presented.
基金supported by Natural Science Foundation of Hubei Province under Grant No.2008CDZ047
文摘Abstract Mehrotra-type predictor-corrector algorithm is one of the most effective primal-dual interior- point methods. This paper presents an extension of the recent variant of second order Mehrotra-type predictor-corrector algorithm that was proposed by Salahi, et a1.（2006） for linear optimization. Basedon the NT direction as Newton search direction, it is shown that the iteration-complexity bound of thealgorithm for semidefinite optimization is which is similar to that of the correspondingalgorithm for linear optimization.
基金supported by National Natural Science Foundation of China (Grant Nos. 61272023 and 61075054)
文摘This paper addresses the learning algorithm on the unit sphere.The main purpose is to present an error analysis for regression generated by regularized least square algorithms with spherical harmonics kernel.The excess error can be estimated by the sum of sample errors and regularization errors.Our study shows that by introducing a suitable spherical harmonics kernel,the regularization parameter can decrease arbitrarily fast with the sample size.
