Sparsity issues in the computation of Jacobian matrices

Authors:
Shahadat Hossain;Trond Steihaug
Affiliations:
University of Lethbridge, Lethbridge, Alberta, Canada;University of Bergen, Bergen, Norway
Venue:
Proceedings of the 2002 international symposium on Symbolic and algebraic computation
Year:
2002

Citing 6
Cited 1

Software for estimating sparse Jacobian matrices

ACM Transactions on Mathematical Software (TOMS)
The cyclic coloring problem and estimation of spare hessian matrices

SIAM Journal on Algebraic and Discrete Methods
The Efficient Computation of Sparse Jacobian Matrices Using Automatic Differentiation

SIAM Journal on Scientific Computing
Evaluating derivatives: principles and techniques of algorithmic differentiation

Evaluating derivatives: principles and techniques of algorithmic differentiation
Automatic differentiation of algorithms: from simulation to optimization

Automatic differentiation of algorithms: from simulation to optimization
Reducing the number of AD passes for computing a sparse Jacobian matrix

Automatic differentiation of algorithms

Approximating Hessians in unconstrained optimization arising from discretized problems

Computational Optimization and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

The knowledge of sparsity information plays an important role in efficient determination of sparse Jacobian matrices. In a recent work, we have proposed sparsity-exploiting substitution techniques to determine Jacobian matrices. In this paper, we take a closer look at the underlying combinatorial problem. We propose a column ordering heuristic to augment the "usable sparsity" in the Jacobian matrix. Furthermore, we present a new elimination technique based on merging of successive columns.