In this paper,we consider the high order method for solving the linear transport equations under diffusive scaling and with random inputs.To tackle the randomness in the problem,the stochastic Galerkin method of the g...In this paper,we consider the high order method for solving the linear transport equations under diffusive scaling and with random inputs.To tackle the randomness in the problem,the stochastic Galerkin method of the generalized polynomial chaos approach has been employed.Besides,the high order implicit-explicit scheme under the micro-macro decomposition framework and the discontinuous Galerkin method have been employed.We provide several numerical experiments to validate the accuracy and the stochastic asymptotic-preserving property.展开更多
In this paper,the authors investigate three aspects of statistical inference for the partially linear regression models where some covariates are measured with errors.Firstly, a bandwidth selection procedure is propos...In this paper,the authors investigate three aspects of statistical inference for the partially linear regression models where some covariates are measured with errors.Firstly, a bandwidth selection procedure is proposed,which is a combination of the differencebased technique and GCV method.Secondly,a goodness-of-fit test procedure is proposed, which is an extension of the generalized likelihood technique.Thirdly,a variable selection procedure for the parametric part is provided based on the nonconcave penalization and corrected profile least squares.Same as＂Variable selection via nonconcave penalized likelihood and its oracle properties＂（J.Amer.Statist.Assoc.,96,2001,1348-1360）,it is shown that the resulting estimator has an oracle property with a proper choice of regularization parameters and penalty function.Simulation studies are conducted to illustrate the finite sample performances of the proposed procedures.展开更多
Many practical problems can be formulated as l0-minimization problems with nonnegativity constraints,which seek the sparsest nonnegative solutions to underdetermined linear systems.Recent study indicates that l1-minim...Many practical problems can be formulated as l0-minimization problems with nonnegativity constraints,which seek the sparsest nonnegative solutions to underdetermined linear systems.Recent study indicates that l1-minimization is efficient for solving l0-minimization problems.From a mathematical point of view,however,the understanding of the relationship between l0-and l1-minimization remains incomplete.In this paper,we further address several theoretical questions associated with these two problems.We prove that the fundamental strict complementarity theorem of linear programming can yield a necessary and sufficient condition for a linear system to admit a unique least l1-norm nonnegative solution.This condition leads naturally to the so-called range space property(RSP)and the “full-column-rank”property,which altogether provide a new and broad understanding of the equivalence and the strong equivalence between l0-and l1-minimization.Motivated by these results,we introduce the concept of “RSP of order K”that turns out to be a full characterization of uniform recovery of all K-sparse nonnegative vectors.This concept also enables us to develop a nonuniform recovery theory for sparse nonnegative vectors via the so-called weak range space property.展开更多
In June 2013, the U.S. National Security Agency proposed two families of lightweight block ciphers, called SIMON and SPECK respectively. These ciphers are designed to perform excellently on both hardware and software ...In June 2013, the U.S. National Security Agency proposed two families of lightweight block ciphers, called SIMON and SPECK respectively. These ciphers are designed to perform excellently on both hardware and software platforms. In this paper, we mainly present zero-correlation linear cryptanalysis on various versions of SIMON. Firstly, by using miss- in-the-middle approach, we construct zero-correlation linear distinguishers of SIMON, and zero-correlation linear attacks are presented based oi1 careful analysis of key recovery phase. Secondly, multidimensional zero-correlation linear attacks are used to reduce the data complexity. Our zero-correlation linear attacks perform better than impossible differential attacks proposed by Abed et al. in ePrint Report 2013/568. Finally, we also use the divide-and-conquer technique to improve the results of linear cryptanalysis proposed by Javad et al. in ePrint Report 2013/663.展开更多
Based on concave function, the problem of finding the sparse solution of absolute value equations is relaxed to a concave programming, and its corresponding algorithm is proposed, whose main part is solving a series o...Based on concave function, the problem of finding the sparse solution of absolute value equations is relaxed to a concave programming, and its corresponding algorithm is proposed, whose main part is solving a series of linear programming. It is proved that a sparse solution can be found under the assumption that the connected matrixes have range space property(RSP). Numerical experiments are also conducted to verify the efficiency of the proposed algorithm.展开更多
基金supported by the Simons Foundation:Collaboration Grantssupported by the AFOSR grant FA9550-18-1-0383.
文摘In this paper,we consider the high order method for solving the linear transport equations under diffusive scaling and with random inputs.To tackle the randomness in the problem,the stochastic Galerkin method of the generalized polynomial chaos approach has been employed.Besides,the high order implicit-explicit scheme under the micro-macro decomposition framework and the discontinuous Galerkin method have been employed.We provide several numerical experiments to validate the accuracy and the stochastic asymptotic-preserving property.
文摘In this paper,the authors investigate three aspects of statistical inference for the partially linear regression models where some covariates are measured with errors.Firstly, a bandwidth selection procedure is proposed,which is a combination of the differencebased technique and GCV method.Secondly,a goodness-of-fit test procedure is proposed, which is an extension of the generalized likelihood technique.Thirdly,a variable selection procedure for the parametric part is provided based on the nonconcave penalization and corrected profile least squares.Same as＂Variable selection via nonconcave penalized likelihood and its oracle properties＂（J.Amer.Statist.Assoc.,96,2001,1348-1360）,it is shown that the resulting estimator has an oracle property with a proper choice of regularization parameters and penalty function.Simulation studies are conducted to illustrate the finite sample performances of the proposed procedures.
基金supported by the Engineering and Physical Sciences Research Council(No.K00946X/1)was partially supported by the National Natural Science Foundation of China(No.11301016).
文摘Many practical problems can be formulated as l0-minimization problems with nonnegativity constraints,which seek the sparsest nonnegative solutions to underdetermined linear systems.Recent study indicates that l1-minimization is efficient for solving l0-minimization problems.From a mathematical point of view,however,the understanding of the relationship between l0-and l1-minimization remains incomplete.In this paper,we further address several theoretical questions associated with these two problems.We prove that the fundamental strict complementarity theorem of linear programming can yield a necessary and sufficient condition for a linear system to admit a unique least l1-norm nonnegative solution.This condition leads naturally to the so-called range space property(RSP)and the “full-column-rank”property,which altogether provide a new and broad understanding of the equivalence and the strong equivalence between l0-and l1-minimization.Motivated by these results,we introduce the concept of “RSP of order K”that turns out to be a full characterization of uniform recovery of all K-sparse nonnegative vectors.This concept also enables us to develop a nonuniform recovery theory for sparse nonnegative vectors via the so-called weak range space property.
基金This work was supported by the National Basic Research 973 Program of China under Grant No. 2013CB338002 and the National Natural Science Foundation of China under Grant Nos. 61272476, 61202420, and 61232009.
文摘In June 2013, the U.S. National Security Agency proposed two families of lightweight block ciphers, called SIMON and SPECK respectively. These ciphers are designed to perform excellently on both hardware and software platforms. In this paper, we mainly present zero-correlation linear cryptanalysis on various versions of SIMON. Firstly, by using miss- in-the-middle approach, we construct zero-correlation linear distinguishers of SIMON, and zero-correlation linear attacks are presented based oi1 careful analysis of key recovery phase. Secondly, multidimensional zero-correlation linear attacks are used to reduce the data complexity. Our zero-correlation linear attacks perform better than impossible differential attacks proposed by Abed et al. in ePrint Report 2013/568. Finally, we also use the divide-and-conquer technique to improve the results of linear cryptanalysis proposed by Javad et al. in ePrint Report 2013/663.
文摘Based on concave function, the problem of finding the sparse solution of absolute value equations is relaxed to a concave programming, and its corresponding algorithm is proposed, whose main part is solving a series of linear programming. It is proved that a sparse solution can be found under the assumption that the connected matrixes have range space property(RSP). Numerical experiments are also conducted to verify the efficiency of the proposed algorithm.
