Sparse Hessian matrices occur often in statistics, and their fast and accurate estimation can improve efficiency of numerical optimization and sampling algorithms. By exploiting the known sparsity pattern of a Hessian, methods in the sparseHessianFD package require many fewer function or gradient evaluations than would be required if the Hessian were treated as dense. The package implements established graph coloring and linear substitution algorithms that were previously unavailable to R users, and is most useful when other numerical, symbolic or algorithmic methods are impractical, inefficient or unavailable
We revisit the role of graph coloring in modeling problems that arise in efficient estimation of la...
spam is an R package for sparse matrix algebra with emphasis on a Cholesky factor-ization of sparse ...
In the context of the Gaussian regression model, the package RKHSMetaMod allows to estimate a meta m...
The sparseHessianFD package is a tool to compute Hessians efficiently when the Hessian is sparse (th...
The solution of a nonlinear optimization problem often requires an estimate of the Hessian matrix f...
The computation of a sparse Hessian matrix H using automatic differentiation (AD) can be made effici...
summary:Necessity of computing large sparse Hessian matrices gave birth to many methods for their ef...
Large-scale optimization algorithms frequently require sparse Hessian matrices that are not readil...
Large scale optimization problems often require an approximation to the Hessian matrix. If the Hess...
Numerical optimization algorithms often require the (symmetric) matrix of second derivatives, $\nab...
Trust region algorithms for nonlinear optimization are commonly believed to be more stable than thei...
We consider the problem of approximating the Hessian matrix of a smooth non-linear function using a ...
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...
In recent years the Hessian matrix and its eigenvalues became important in pattern recognition. Sev...
We revisit the role of graph coloring in modeling problems that arise in efficient estimation of la...
spam is an R package for sparse matrix algebra with emphasis on a Cholesky factor-ization of sparse ...
In the context of the Gaussian regression model, the package RKHSMetaMod allows to estimate a meta m...
The sparseHessianFD package is a tool to compute Hessians efficiently when the Hessian is sparse (th...
The solution of a nonlinear optimization problem often requires an estimate of the Hessian matrix f...
The computation of a sparse Hessian matrix H using automatic differentiation (AD) can be made effici...
summary:Necessity of computing large sparse Hessian matrices gave birth to many methods for their ef...
Large-scale optimization algorithms frequently require sparse Hessian matrices that are not readil...
Large scale optimization problems often require an approximation to the Hessian matrix. If the Hess...
Numerical optimization algorithms often require the (symmetric) matrix of second derivatives, $\nab...
Trust region algorithms for nonlinear optimization are commonly believed to be more stable than thei...
We consider the problem of approximating the Hessian matrix of a smooth non-linear function using a ...
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...
In recent years the Hessian matrix and its eigenvalues became important in pattern recognition. Sev...
We revisit the role of graph coloring in modeling problems that arise in efficient estimation of la...
spam is an R package for sparse matrix algebra with emphasis on a Cholesky factor-ization of sparse ...
In the context of the Gaussian regression model, the package RKHSMetaMod allows to estimate a meta m...