Symbolic and Exact Structure Prediction for Sparse Gaussian Elimination with Partial Pivoting

نویسندگان

  • Laura Grigori
  • John R. Gilbert
  • Michel Cosnard
چکیده

In this paper we consider two structure prediction problems of interest in Gaussian elimination with partial pivoting of sparse matrices. First, we consider the problem of determining the nonzero structure of the factors L and U during the factorization. We present an exact prediction of the structure, that identifies some numeric cancellations appearing during Gaussian elimination. The numeric cancellations are related to submatrices of the input matrix A that are structurally singular, that is, singular due to the arrangements of their nonzeros, and independently of their numerical values. Second, we consider the problem of estimating upper bounds for the structure of L and U prior to the numerical factorization. We present tight exact bounds for the nonzero structure of L and U of Gaussian elimination with partial pivoting PA = LU under the assumption that the matrix A satisfies a combinatorial property, namely the Hall property, and that the nonzero values in A are algebraically independent from each other. This complements existing work showing that a structure called the row merge graph represents a tight bound for the nonzero structure of L and U under a stronger combinatorial assumption, namely the strong Hall property. We also show that the row merge graph represents a tight symbolic bound for matrices satisfying only the Hall property.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Parallel Sparse Gaussian Elimination with Partial Pivoting and 2-d Data Mapping Parallel Sparse Gaussian Elimination with Partial Pivoting and 2-d Data Mapping Abstract Parallel Sparse Gaussian Elimination with Partial Pivoting and 2-d Data Mapping

Sparse Gaussian elimination with partial pivoting is a fundamental algorithm for many scientiic and engineering applications, but it is still an open problem to develop a time and space eecient algorithm on distributed memory machines. In this thesis, we present an asynchronous algorithm which incorporates static symbolic factorization, nonsymmetric L/U supernode partitioning and supern-ode ama...

متن کامل

A Scalable Sparse Direct Solver Using Static Pivoting

We propose several techniques as alternatives to partial pivoting to stabilize sparse Gaussian elimination. From numerical experiments we demonstrate that for a wide range of problems the new method is as stable as partial pivoting. The main advantage of the new method over partial pivoting is that it permits a priori determination of data structures and communication pattern, which makes it mo...

متن کامل

Improved Symbolic and Numerical Factorization Algorithms for Unsymmetric Sparse Matrices

We present algorithms for the symbolic and numerical factorization phases in the direct solution of sparse unsymmetric systems of linear equations. We have modified a classical symbolic factorization algorithm for unsymmetric matrices to inexpensively compute minimal elimination structures. We give an efficient algorithm to compute a near-minimal data-dependency graph for unsymmetric multifront...

متن کامل

Eecient Sparse Lu Factorization with Partial Pivoting on Distributed Memory Architectures

A sparse LU factorization based on Gaussian elimination with partial pivoting (GEPP) is important to many scientiic applications, but it is still an open problem to develop a high performance GEPP code on distributed memory machines. The main diiculty is that partial pivoting operations dynamically change computation and nonzero ll-in structures during the elimination process. This paper presen...

متن کامل

Efficient Sparse LU Factorization with Partial Pivoting on Distributed Memory Architectures

A sparse LU factorization based on Gaussian elimination with partial pivoting (GEPP) is important to many scientific applications, but it is still an open problem to develop a high performance GEPP code on distributed memory machines. The main difficulty is that partial pivoting operations dynamically change computation and nonzero fill-in structures during the elimination process. This paper p...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • SIAM J. Matrix Analysis Applications

دوره 30  شماره 

صفحات  -

تاریخ انتشار 2008