Based on the compressive sensing,a novel algorithm is proposed to solve reconstruction problem under sparsity assumptions.Instead of estimating the reconstructed data through minimizing the objective function,the auth...Based on the compressive sensing,a novel algorithm is proposed to solve reconstruction problem under sparsity assumptions.Instead of estimating the reconstructed data through minimizing the objective function,the authors parameterize the problem as a linear combination of few elementary thresholding functions,which can be solved by calculating the linear weighting coefficients.It is to update the thresholding functions during the process of iteration.The advantage of this method is that the optimization problem only needs to be solved by calculating linear coefficients for each time.With the elementary thresholding functions satisfying certain constraints,a global convergence of the iterative algorithm is guaranteed.The synthetic and the field data results prove the effectiveness of the proposed algorithm.展开更多
This paper shows two approaches to improve the performance of numeral al- gebra software by describing block algorithms in LAPACK. The block algorithms can make up higher level and more effcient BLAS programs. This pa...This paper shows two approaches to improve the performance of numeral al- gebra software by describing block algorithms in LAPACK. The block algorithms can make up higher level and more effcient BLAS programs. This paper further presents the relations between the effciency of the block algorithm and the size of block, and shows the relations relates to not only scale of algorithms and problems but also architectures and Characters of destination machines. Finally The paper gives the test results on Hitachi SR2201& SR8000.展开更多
Motivated by Sasaki＇s work on the extended Hensel construction for solving multivariate algebraic equations, we present a generalized Hensel lifting, which takes advantage of sparsity, for factoring bivariate polynom...Motivated by Sasaki＇s work on the extended Hensel construction for solving multivariate algebraic equations, we present a generalized Hensel lifting, which takes advantage of sparsity, for factoring bivariate polynomial over the rational number field. Another feature of the factorization algorithm presented in this article is a new recombination method, which can solve the extraneous factor problem before lifting based on numerical linear algebra. Both theoretical analysis and experimental data show that the algorithm is etIicient, especially for sparse bivariate polynomials.展开更多
In this study, a collocation technique is presented for approximate solution of the fractional-order logistic population model. Actually, we develop the Bessel collocation method by using the fractional derivative in ...In this study, a collocation technique is presented for approximate solution of the fractional-order logistic population model. Actually, we develop the Bessel collocation method by using the fractional derivative in the Caputo sense to obtain the approximate solutions of this model problem. By means of the fractional derivative in the Caputo sense, the collocation points, the Bessel functions of the first kind, the method transforms the model problem into a system of nonlinear algebraic equations. Numerical applications are given to demonstrate efficiency and accuracy of the method. In applications, the reliability of the scheme is shown by the error function based on the accuracy of the approximate solution.展开更多
文摘Based on the compressive sensing,a novel algorithm is proposed to solve reconstruction problem under sparsity assumptions.Instead of estimating the reconstructed data through minimizing the objective function,the authors parameterize the problem as a linear combination of few elementary thresholding functions,which can be solved by calculating the linear weighting coefficients.It is to update the thresholding functions during the process of iteration.The advantage of this method is that the optimization problem only needs to be solved by calculating linear coefficients for each time.With the elementary thresholding functions satisfying certain constraints,a global convergence of the iterative algorithm is guaranteed.The synthetic and the field data results prove the effectiveness of the proposed algorithm.
文摘This paper shows two approaches to improve the performance of numeral al- gebra software by describing block algorithms in LAPACK. The block algorithms can make up higher level and more effcient BLAS programs. This paper further presents the relations between the effciency of the block algorithm and the size of block, and shows the relations relates to not only scale of algorithms and problems but also architectures and Characters of destination machines. Finally The paper gives the test results on Hitachi SR2201& SR8000.
基金supported by National Natural Science Foundation of China(GrantNos.91118001 and 11170153)National Key Basic Research Project of China(Grant No.2011CB302400)Chongqing Science and Technology Commission Project(Grant No.cstc2013jjys40001)
文摘Motivated by Sasaki＇s work on the extended Hensel construction for solving multivariate algebraic equations, we present a generalized Hensel lifting, which takes advantage of sparsity, for factoring bivariate polynomial over the rational number field. Another feature of the factorization algorithm presented in this article is a new recombination method, which can solve the extraneous factor problem before lifting based on numerical linear algebra. Both theoretical analysis and experimental data show that the algorithm is etIicient, especially for sparse bivariate polynomials.
文摘In this study, a collocation technique is presented for approximate solution of the fractional-order logistic population model. Actually, we develop the Bessel collocation method by using the fractional derivative in the Caputo sense to obtain the approximate solutions of this model problem. By means of the fractional derivative in the Caputo sense, the collocation points, the Bessel functions of the first kind, the method transforms the model problem into a system of nonlinear algebraic equations. Numerical applications are given to demonstrate efficiency and accuracy of the method. In applications, the reliability of the scheme is shown by the error function based on the accuracy of the approximate solution.
