Separators and structure prediction in sparse orthogonal factorization
نویسندگان
چکیده
منابع مشابه
Sparse Gaussian Elimination and Orthogonal Factorization
We consider the solution of a linear system Ax = b on a distributedmemorymachine when the matrixA has full rank and is large, sparse and nonsymmetric. We use our Cartesian nested dissection algorithm to compute a ll-reducingcolumn ordering of the matrix. We develop algorithms that use the associated separator tree to estimate the structure of the factor and to distribute and perform numeric com...
متن کاملPredicting the Structure of Sparse Orthogonal Factors
The problem of correctly predicting the structures of the orthogo nal factorsQ and R from the structure of a matrix A with full column rank is considered in this paper Recently Hare Johnson Olesky and van den Driessche have described a method to predict these structures and they have shown that corresponding to any speci ed nonzero element in the predicted structures of Q or R there exists a ma...
متن کاملMinimal separators in P4-sparse graphs
In this paper, we determine the minimal separators of P4-sparse graphs and establish bounds on their number. Specifically, we show that a P4-sparse graph G on n vertices and m edges has fewer than 2n/3 minimal separators of total description size at most 4m/3. The bound on the number of minimal separators is tight and is also tight for the class of cographs, a well known subclass of the P4-spar...
متن کاملEntropy-Based Sparse Trajectories Prediction Enhanced by Matrix Factorization
Existing moving object’s trajectory prediction algorithms suffer from the data sparsity problem, which affects the accuracy of the trajectory prediction. Aiming to the problem, we present an Entropy-based Sparse Trajectories Prediction method enhanced by Matrix Factorization (ESTP-MF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the traj...
متن کاملParallel Sparse Cholesky Factorization
Sparse matrix factorization plays an important role in many numerical algorithms. In this paper we describe a scalable parallel algorithm based on the Multifrontal Method. Computational experiments on a Parsytec CC system with 32 processors show that large sparse matrices can be factorized in only a few seconds.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Linear Algebra and its Applications
سال: 1997
ISSN: 0024-3795
DOI: 10.1016/s0024-3795(97)80024-9