We consider the problem of approximating the Hessian matrix of a smooth non-linear function using a minimum number of gradient evaluations, particularly in the case that the Hessian has a known, fixed sparsity pattern. We study the class of Direct Methods for this problem, and propose two new ways of classifying Direct Methods. Examples are given that show the relationships among optimal methods from each class. The problem of finding a non-overlapping direct cover is shown to be equivalent o a generalized graph coloring problem--the distance-2 graph coloring problem. A theorem is proved showing that the general distance-k graph coloring problem is NP-Complete for all fixed k>- 2, and hence that the optimal non-overlapping direct cover p...
This research concerns the algorithmic study of Hessian approximation in the context of multilevel n...
There are several benefits of taking the Hessian of the objective function into account when designi...
Unconstrained optimization, Discretized problems, Sparsity, Partial separability, Numerical experien...
summary:Necessity of computing large sparse Hessian matrices gave birth to many methods for their ef...
Large scale optimization problems often require an approximation to the Hessian matrix. If the Hess...
The computation of a sparse Hessian matrix H using automatic differentiation (AD) can be made effici...
Numerical optimization algorithms often require the (symmetric) matrix of second derivatives, $\nab...
We revisit the role of graph coloring in modeling problems that arise in efficient estimation of la...
The solution of a nonlinear optimization problem often requires an estimate of the Hessian matrix f...
Given a mapping with a sparse Jacobian matrix, the problem of minimizing the number of function eval...
Sparse Hessian matrices occur often in statistics, and their fast and accurate estimation can improv...
Large-scale optimization algorithms frequently require sparse Hessian matrices that are not readil...
Abstract Matrix partitioning problems that arise in the efficient estimation ofsparse Jacobians and ...
For an unconstrained minimization problem with a sparse Hessian, a symmetric version of Schubert's ...
The sparseHessianFD package is a tool to compute Hessians efficiently when the Hessian is sparse (th...
This research concerns the algorithmic study of Hessian approximation in the context of multilevel n...
There are several benefits of taking the Hessian of the objective function into account when designi...
Unconstrained optimization, Discretized problems, Sparsity, Partial separability, Numerical experien...
summary:Necessity of computing large sparse Hessian matrices gave birth to many methods for their ef...
Large scale optimization problems often require an approximation to the Hessian matrix. If the Hess...
The computation of a sparse Hessian matrix H using automatic differentiation (AD) can be made effici...
Numerical optimization algorithms often require the (symmetric) matrix of second derivatives, $\nab...
We revisit the role of graph coloring in modeling problems that arise in efficient estimation of la...
The solution of a nonlinear optimization problem often requires an estimate of the Hessian matrix f...
Given a mapping with a sparse Jacobian matrix, the problem of minimizing the number of function eval...
Sparse Hessian matrices occur often in statistics, and their fast and accurate estimation can improv...
Large-scale optimization algorithms frequently require sparse Hessian matrices that are not readil...
Abstract Matrix partitioning problems that arise in the efficient estimation ofsparse Jacobians and ...
For an unconstrained minimization problem with a sparse Hessian, a symmetric version of Schubert's ...
The sparseHessianFD package is a tool to compute Hessians efficiently when the Hessian is sparse (th...
This research concerns the algorithmic study of Hessian approximation in the context of multilevel n...
There are several benefits of taking the Hessian of the objective function into account when designi...
Unconstrained optimization, Discretized problems, Sparsity, Partial separability, Numerical experien...