Add to ORKGSparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.
The mathematical models of many practical problems lead to systems of linear algebraic equations wh...
International audienceThe efficiency of a sparse linear algebra operation heavily relies on the abil...
Abstract. On many high-speed computers the dense matrix technique is preferable to sparse matrix tec...
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the pack...
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the pack...
A sparse matrix is a matrix with very few nonzero elements. Many applications in diverse fields gi...
spam is an R package for sparse matrix algebra with emphasis on a Cholesky factorization of sparse p...
Today most real life applications require processing large amounts of data (i.e. ”Big Data”). The pa...
spam is an R package for sparse matrix algebra with emphasis on a Cholesky factor-ization of sparse ...
Research is described on sparse matrix techniques for the Computational Structural Mechanics (CSM) T...
Sparse matrix computations arise in many scientific computing problems and for some (e.g.: iterative...
In the last decade, the demand for statistical and computation methods for data analysis that involv...
The multiplication of a sparse matrix by a dense vector is a centerpiece of scientific computing app...
xi, 76 leaves : ill. ; 29 cm.The efficiency of linear algebra operations for sparse matrices on mode...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
The mathematical models of many practical problems lead to systems of linear algebraic equations wh...
International audienceThe efficiency of a sparse linear algebra operation heavily relies on the abil...
Abstract. On many high-speed computers the dense matrix technique is preferable to sparse matrix tec...
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the pack...
SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the pack...
A sparse matrix is a matrix with very few nonzero elements. Many applications in diverse fields gi...
spam is an R package for sparse matrix algebra with emphasis on a Cholesky factorization of sparse p...
Today most real life applications require processing large amounts of data (i.e. ”Big Data”). The pa...
spam is an R package for sparse matrix algebra with emphasis on a Cholesky factor-ization of sparse ...
Research is described on sparse matrix techniques for the Computational Structural Mechanics (CSM) T...
Sparse matrix computations arise in many scientific computing problems and for some (e.g.: iterative...
In the last decade, the demand for statistical and computation methods for data analysis that involv...
The multiplication of a sparse matrix by a dense vector is a centerpiece of scientific computing app...
xi, 76 leaves : ill. ; 29 cm.The efficiency of linear algebra operations for sparse matrices on mode...
Abstract. Sparse matrix-vector multiplication is an important computational kernel that tends to per...
The mathematical models of many practical problems lead to systems of linear algebraic equations wh...
International audienceThe efficiency of a sparse linear algebra operation heavily relies on the abil...
Abstract. On many high-speed computers the dense matrix technique is preferable to sparse matrix tec...