In this paper, the σ_duals of two classes important sequence spaces l 1(X) and l ∞(X) are investigated, and shows that some topology properties of locally convex space (X,τ) can be characterized by the σ _dua...In this paper, the σ_duals of two classes important sequence spaces l 1(X) and l ∞(X) are investigated, and shows that some topology properties of locally convex space (X,τ) can be characterized by the σ _duals. The criterions of bounded sets in l 1(X) and l ∞(X ) with respect to the weak topologies generated by the σ _duals are obtained. Furthermore, a Schur type result and an automatic continuity theorem of matrix transformation are established.展开更多
The authors obtain new characterizations of unconditional Cauchy series in terms of separation properties of subfamilies of p(N), and a generalization of the Orlicz-Pettis Theorem is also obtained. New results on the ...The authors obtain new characterizations of unconditional Cauchy series in terms of separation properties of subfamilies of p(N), and a generalization of the Orlicz-Pettis Theorem is also obtained. New results on the uniform convergence on matrices and a new version of the Hahn-Schur summation theorem are proved. For matrices whose rows define unconditional Cauchy series, a better sufficient condition for the basic Matrix Theorem of Antosik and Swartz, new necessary conditions and a new proof of that theorem are given.展开更多
The matrix rank minimization problem arises in many engineering applications. As this problem is NP-hard, a nonconvex relaxation of matrix rank minimization, called the Schatten-p quasi-norm minimization(0 < p <...The matrix rank minimization problem arises in many engineering applications. As this problem is NP-hard, a nonconvex relaxation of matrix rank minimization, called the Schatten-p quasi-norm minimization(0 < p < 1), has been developed to approximate the rank function closely. We study the performance of projected gradient descent algorithm for solving the Schatten-p quasi-norm minimization(0 < p < 1) problem.Based on the matrix restricted isometry property(M-RIP), we give the convergence guarantee and error bound for this algorithm and show that the algorithm is robust to noise with an exponential convergence rate.展开更多
Estimate bounds for the Perron root of a nonnegative matrix are important in theory of nonnegative matrices.It is more practical when the bounds are expressed as an easily calcu-lated function in elements of matrices....Estimate bounds for the Perron root of a nonnegative matrix are important in theory of nonnegative matrices.It is more practical when the bounds are expressed as an easily calcu-lated function in elements of matrices.For the Perron root of nonnegative irreducible matrices,three sequences of lower bounds are presented by means of constructing shifted matrices,whose convergence is studied.The comparisons of the sequences with known ones are supplemented with a numerical example.展开更多
A class ofparallel nonlinear multisplitting AOR methods is set upby directly ltisplittingthe nonlinear mapping F:D C Rn、R”for solving the nonlinear system of equationsF(x)= 0.The different choices of the relaxati...A class ofparallel nonlinear multisplitting AOR methods is set upby directly ltisplittingthe nonlinear mapping F:D C Rn、R”for solving the nonlinear system of equationsF(x)= 0.The different choices of the relaxation par。ters c。 yield all the kn。n and a lotof new rel8Xatlon methods as well as a M of new relaxatlon parallel nonlinear multisplittingmethods.Thetwrvsided approximation properties and th IMuences on convergence Mmthe relaxatlon parameters about the new methods are shown,and the sufficient conditionsguaranteeing the methods to converge globally are discussed.FlnallL aht ofnumericalresultsshow that the methods are feasible and efficient.展开更多
The semidefinite matrix completion(SMC) problem is to recover a low-rank positive semidefinite matrix from a small subset of its entries. It is well known but NP-hard in general. We first show that under some cases, S...The semidefinite matrix completion(SMC) problem is to recover a low-rank positive semidefinite matrix from a small subset of its entries. It is well known but NP-hard in general. We first show that under some cases, SMC problem and S1/2relaxation model share a unique solution. Then we prove that the global optimal solutions of S1/2regularization model are fixed points of a symmetric matrix half thresholding operator. We give an iterative scheme for solving S1/2regularization model and state convergence analysis of the iterative sequence.Through the optimal regularization parameter setting together with truncation techniques, we develop an HTE algorithm for S1/2regularization model, and numerical experiments confirm the efficiency and robustness of the proposed algorithm.展开更多
This paper studies a family of the local convergence of the improved secant methods for solving the nonlinear equality constrained optimization subject to bounds on variables. The Hessian of the Lagrangian is approxim...This paper studies a family of the local convergence of the improved secant methods for solving the nonlinear equality constrained optimization subject to bounds on variables. The Hessian of the Lagrangian is approximated using the DFP or the BFGS secant updates. The improved secant methods are used to generate a search direction. Combining with a suitable step size, each iterate switches to trial step of strict interior feasibility. When the Hessian is only positive definite in an affine null subspace, one shows that the algorithms generate the sequences converging q-linearly and two-step q-superlinearly. Yhrthermore, under some suitable assumptions, some sequences generated by the algorithms converge locally one-step q-superlinearly. Finally, some numerical results are presented to illustrate the effectiveness of the proposed algorithms.展开更多
文摘In this paper, the σ_duals of two classes important sequence spaces l 1(X) and l ∞(X) are investigated, and shows that some topology properties of locally convex space (X,τ) can be characterized by the σ _duals. The criterions of bounded sets in l 1(X) and l ∞(X ) with respect to the weak topologies generated by the σ _duals are obtained. Furthermore, a Schur type result and an automatic continuity theorem of matrix transformation are established.
文摘The authors obtain new characterizations of unconditional Cauchy series in terms of separation properties of subfamilies of p(N), and a generalization of the Orlicz-Pettis Theorem is also obtained. New results on the uniform convergence on matrices and a new version of the Hahn-Schur summation theorem are proved. For matrices whose rows define unconditional Cauchy series, a better sufficient condition for the basic Matrix Theorem of Antosik and Swartz, new necessary conditions and a new proof of that theorem are given.
基金supported by National Natural Science Foundation of China(Grant No.11171299)
文摘The matrix rank minimization problem arises in many engineering applications. As this problem is NP-hard, a nonconvex relaxation of matrix rank minimization, called the Schatten-p quasi-norm minimization(0 < p < 1), has been developed to approximate the rank function closely. We study the performance of projected gradient descent algorithm for solving the Schatten-p quasi-norm minimization(0 < p < 1) problem.Based on the matrix restricted isometry property(M-RIP), we give the convergence guarantee and error bound for this algorithm and show that the algorithm is robust to noise with an exponential convergence rate.
基金the National Natural Science Foundation of China (No.10771030)Project for Academic Leader and Group of UESTC (No.L08011001JX0776)
文摘Estimate bounds for the Perron root of a nonnegative matrix are important in theory of nonnegative matrices.It is more practical when the bounds are expressed as an easily calcu-lated function in elements of matrices.For the Perron root of nonnegative irreducible matrices,three sequences of lower bounds are presented by means of constructing shifted matrices,whose convergence is studied.The comparisons of the sequences with known ones are supplemented with a numerical example.
文摘A class ofparallel nonlinear multisplitting AOR methods is set upby directly ltisplittingthe nonlinear mapping F:D C Rn、R”for solving the nonlinear system of equationsF(x)= 0.The different choices of the relaxation par。ters c。 yield all the kn。n and a lotof new rel8Xatlon methods as well as a M of new relaxatlon parallel nonlinear multisplittingmethods.Thetwrvsided approximation properties and th IMuences on convergence Mmthe relaxatlon parameters about the new methods are shown,and the sufficient conditionsguaranteeing the methods to converge globally are discussed.FlnallL aht ofnumericalresultsshow that the methods are feasible and efficient.
基金supported by National Natural Science Foundation of China(Grant Nos.11431002,71271021 and 11301022)the Fundamental Research Funds for the Central Universities of China(Grant No.2012YJS118)
文摘The semidefinite matrix completion(SMC) problem is to recover a low-rank positive semidefinite matrix from a small subset of its entries. It is well known but NP-hard in general. We first show that under some cases, SMC problem and S1/2relaxation model share a unique solution. Then we prove that the global optimal solutions of S1/2regularization model are fixed points of a symmetric matrix half thresholding operator. We give an iterative scheme for solving S1/2regularization model and state convergence analysis of the iterative sequence.Through the optimal regularization parameter setting together with truncation techniques, we develop an HTE algorithm for S1/2regularization model, and numerical experiments confirm the efficiency and robustness of the proposed algorithm.
基金supported by the partial supports of the National Science Foundation under Grant No.10871130the Ph.D. Foundation under Grant No.20093127110005 of Chinese Education Ministry
文摘This paper studies a family of the local convergence of the improved secant methods for solving the nonlinear equality constrained optimization subject to bounds on variables. The Hessian of the Lagrangian is approximated using the DFP or the BFGS secant updates. The improved secant methods are used to generate a search direction. Combining with a suitable step size, each iterate switches to trial step of strict interior feasibility. When the Hessian is only positive definite in an affine null subspace, one shows that the algorithms generate the sequences converging q-linearly and two-step q-superlinearly. Yhrthermore, under some suitable assumptions, some sequences generated by the algorithms converge locally one-step q-superlinearly. Finally, some numerical results are presented to illustrate the effectiveness of the proposed algorithms.
