We introduce a divide-and-conquer method for the generalized eigenvalue problem Ax = lambda Bx, where A and B are real symmetric tridiagonal matrices and B is positive-definite. It is a generalization of Cuppen's ...
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We introduce a divide-and-conquer method for the generalized eigenvalue problem Ax = lambda Bx, where A and B are real symmetric tridiagonal matrices and B is positive-definite. It is a generalization of Cuppen's method for the standard eigenvalue problem, B = I, which is based on rank-one modifications. Our method is an alternative to a method developed by Borges and Gragg using restrictions and extensions.
The divide-and-conquer (DC) method, which is one of the linear-scaling methods avoiding explicit diagonalization of the Fock matrix. has been applied mainly to pure density functional theory (DFT) or semiempirical mol...
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The divide-and-conquer (DC) method, which is one of the linear-scaling methods avoiding explicit diagonalization of the Fock matrix. has been applied mainly to pure density functional theory (DFT) or semiempirical molecular orbital calculations so far. The present study applies the DC method to such calculations including the Hartree-Fock (HF) exchange terms as the HF and hybrid HF/DFT. Reliability of the DC-HF and DC-hybrid HF/DFT is found to be strongly dependent oil the cut-off radius, which defines the localization region in the DC formalism. This dependence on the cut-off radius is assessed from various points of view: that is, total energy, energy components, local energies, and density of states. Additionally, to accelerate the self-consistent field convergence in DC calculations, a new convergence technique is proposed. (c) 2007 Wiley, Periodicals, Inc.
This paper presents a divide-and-conquer method for computing the symmetric singular value decomposition, or Takagi factorization, of a complex symmetric and tridiagonal matrix. An analysis of accuracy shows that our ...
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This paper presents a divide-and-conquer method for computing the symmetric singular value decomposition, or Takagi factorization, of a complex symmetric and tridiagonal matrix. An analysis of accuracy shows that our method produces accurate Takagi values and orthogonal Takagi vectors. Our preliminary numerical experiments have confirmed our analysis and demonstrated that our divide-and-conquer method is much more efficient than the implicit QR method even for moderately large matrices.
A two-level hierarchical parallelization scheme including the second-order Moller-Plesset perturbation (MP2) theory in the divide-and-conquer method is presented. The scheme is a combination of coarse-grain paralleliz...
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A two-level hierarchical parallelization scheme including the second-order Moller-Plesset perturbation (MP2) theory in the divide-and-conquer method is presented. The scheme is a combination of coarse-grain parallelization assigning each subsystem to a group of processors, with fine-grain parallelization, where the computational tasks for evaluating MP2 correlation energy of the assigned subsystem are distributed among processors in the group. Test calculations demonstrate that the present scheme shows high parallel efficiency and makes MP2 calculations practical for very large molecules. (C) 2011 Wiley Periodicals, Inc. J Comput Chem 32: 2756-2764, 2011
A divide-and-conquer method for computing eigenvalues and eigenvectors of a block-tridiagonal matrix with rank-one off-diagonal blocks is presented. The implications of unbalanced merging operations due to unequal blo...
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A divide-and-conquer method for computing eigenvalues and eigenvectors of a block-tridiagonal matrix with rank-one off-diagonal blocks is presented. The implications of unbalanced merging operations due to unequal block sizes are analyzed and illustrated with numerical examples. It is shown that an unfavorable order for merging blocks in the synthesis phase of the algorithm may lead to a significant increase of the arithmetic complexity. A strategy to determine a good merging order that is at least close to optimal in all cases is given. The method has been implemented and applied to test problems from a quantum chemistry application.
We study the accuracy of the divide-and-conquer method for electronic structure calculations. The analysis is conducted for a prototypical subdomain problem in the method. We prove that the pointwise difference betwee...
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We study the accuracy of the divide-and-conquer method for electronic structure calculations. The analysis is conducted for a prototypical subdomain problem in the method. We prove that the pointwise difference between electron densities of the global system and the subsystem decays exponentially as a function of the distance away from the boundary of the subsystem, under the gap assumption of both the global system and the subsystem. We show that the gap assumption is crucial for the accuracy of the divide-and-conquer method by numerical examples. In particular, we show examples with the loss of accuracy when the gap assumption of the subsystem is invalid.
A framework for an efficient low-complexity divide-and-conquer algorithm for computing eigenvalues and eigenvectors of an n x n symmetric band matrix with semibandwidth b << n is proposed and its arithmetic comp...
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A framework for an efficient low-complexity divide-and-conquer algorithm for computing eigenvalues and eigenvectors of an n x n symmetric band matrix with semibandwidth b << n is proposed and its arithmetic complexity analyzed. The distinctive feature of the algorithm-after subdivision of the original problem into p subproblems and their solution-is a separation of the eigenvalue and eigenvector computations in the central synthesis problem. The eigenvalues are computed recursively by representing the corresponding symmetric rank b (p-1) modi cation of a diagonal matrix as a series of rank-one modi cations. Each rank-one modi cation problem can be solved using techniques developed for the tridiagonal divide-and-conquer algorithm. Once the eigenvalues are known, the corresponding eigenvectors can be computed efficiently using modified QR factorizations with restricted column pivoting. It is shown that the complexity of the resulting divide-and-conquer algorithm is O(n(2)b(2)) (in exact arithmetic).
Large-scale two-component (2c) relativistic quantum-chemical (RQC) theory is reviewed. We briefly discuss the theories, advantages, and extensibilities of an overall linear-scaling scheme in 2c relativistic theory. Th...
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Large-scale two-component (2c) relativistic quantum-chemical (RQC) theory is reviewed. We briefly discuss the theories, advantages, and extensibilities of an overall linear-scaling scheme in 2c relativistic theory. The theory is based on the infinite-order Douglas-Kroll-Hess method, with the local unitary transformation scheme to produce the 2c relativistic Hamiltonian, and the divide-and-conquer method to achieve linear-scaling of Hartree-Fock and electron correlation methods. Furthermore, perspectives for large-scale RQC are explained to bring the practical usage and treatment of light and heavy elements in 2c relativistic calculations close to those in non-relativistic methods. (c) 2014 Wiley Periodicals, Inc.
This study proposes two efficient algorithms of the linear-scaling divide-and-conquer self-consistent field (DC-SCF) method for parallel computations. These algorithms minimize the amount of network communication requ...
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This study proposes two efficient algorithms of the linear-scaling divide-and-conquer self-consistent field (DC-SCF) method for parallel computations. These algorithms minimize the amount of network communication required for determining the common Fermi level by adopting an approximate Fermi level. One algorithm adopts the quasi-converged Fermi level during DC-SCF iterations, while the other uses the quasi-converged electron numbers of individual subsystems. A numerical assessment demonstrates the high parallel efficiency for both methods without loss in accuracy.
This paper proposes a novel ensemble regression model to predict time series data of water quality. The proposed model consists of multiple regressors and a classifier. The model transforms the original time series da...
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ISBN:
(数字)9783642412998
ISBN:
(纸本)9783642412998;9783642412981
This paper proposes a novel ensemble regression model to predict time series data of water quality. The proposed model consists of multiple regressors and a classifier. The model transforms the original time series data into subsequences by sliding window and divides it into several parts according to the fitness of regressor so that each regressor has advantages in a specific part. The classifier decides which part the new data should belong to so that the model could divide the whole prediction problem into small parts and conquer it after computing on only one part. The ensemble regression model, with a combination of Support Vector Machine, RBF Neural Network and Grey Model, is tested using 450-week observations of CODMn data provided by Ministry of Environmental Protection of the People's Republic of China during 2004 and 2012. The results show that the model could approximately convert the problem of prediction into a problem of classification and provide better accuracy over each single model it has combined.
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