On the efficient determination of Hessian matrix sparsity pattern : algorithms and data structures

Evaluation of the Hessian matrix of a scalar function is a subproblem in many numerical optimization algorithms. For large-scale problems often the Hessian matrix is sparse and structured, and it is preferable to exploit such information when available. Using symmetry in the second derivative values of the components it is possible to detect the sparsity pattern of the Hessian via products of the Hessian matrix with specially chosen direction vectors. We use graph coloring methods and employ efficient sparse data structures to implement the sparsity pattern detection algorithms.

Authors

• Sultana, Marzia
• University of Lethbridge. Faculty of Arts and Science

• algorithmic differentiation tools
• black-box gradient
• direction vectors
• graph coloring
• greedy CPR algorithm
• sparsity patterns

Identifier

hdl:10133/4601

OAI identifier oai:identifier

oai:opus.uleth.ca:10133/4601