An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are mad...An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.展开更多
Aiming at the problem that a large number of array elements are needed for uniform arrays to meet the requirements of direction map,a sparse array pattern synthesis method is proposed in this paper based on the sparse...Aiming at the problem that a large number of array elements are needed for uniform arrays to meet the requirements of direction map,a sparse array pattern synthesis method is proposed in this paper based on the sparse sensing theory.First,the Orthogonal Matching Pursuit(OMP)algorithm and the Exact Augmented Lagrange Multiplier(EALM)algorithm were improved in the sparse sensing theory to obtain a more efficient Orthogonal Multi⁃Matching Pursuit(OMMP)algorithm and the Semi⁃Exact Augmented Lagrange Multiplier(SEALM)algorithm.Then,the two improved algorithms were applied to linear array and planar array pattern syntheses respectively.Results showed that the improved algorithms could achieve the required pattern with very few elements.Numerical simulations verified the effectiveness and superiority of the two synthetic methods.In addition,compared with the existing sparse array synthesis method,the proposed method was more robust and accurate,and could maintain the advantage of easy implementation.展开更多
基金Project (2002CB312200) supported by the National Key Basic Research and Development Program of China Project(03JJY3109) supported by the Natural Science Foundation of Hunan Province
文摘An active set truncated-Newton algorithm (ASTNA) is proposed to solve the large-scale bound constrained sub-problems. The global convergence of the algorithm is obtained and two groups of numerical experiments are made for the various large-scale problems of varying size. The comparison results between ASTNA and the subspace limited memory quasi-Newton algorithm and between the modified augmented Lagrange multiplier methods combined with ASTNA and the modified barrier function method show the stability and effectiveness of ASTNA for simultaneous optimization of distillation column.
基金Sponsored by the National Natural Science Foundation of China(Grant No.U1813222)the Tianjin Natural Science Foundation(Grant No.18JCYBJC16500)+1 种基金the Hebei Province Natural Science Foundation(Grant No.E2016202341)the Research Project on Graduate Training in Hebei University of Technology(Grant No.201801Y006).
文摘Aiming at the problem that a large number of array elements are needed for uniform arrays to meet the requirements of direction map,a sparse array pattern synthesis method is proposed in this paper based on the sparse sensing theory.First,the Orthogonal Matching Pursuit(OMP)algorithm and the Exact Augmented Lagrange Multiplier(EALM)algorithm were improved in the sparse sensing theory to obtain a more efficient Orthogonal Multi⁃Matching Pursuit(OMMP)algorithm and the Semi⁃Exact Augmented Lagrange Multiplier(SEALM)algorithm.Then,the two improved algorithms were applied to linear array and planar array pattern syntheses respectively.Results showed that the improved algorithms could achieve the required pattern with very few elements.Numerical simulations verified the effectiveness and superiority of the two synthetic methods.In addition,compared with the existing sparse array synthesis method,the proposed method was more robust and accurate,and could maintain the advantage of easy implementation.
