On the efficient determination of Hessian matrix sparsity pattern : algorithms and data structures
Date
2016
Authors
Sultana, Marzia
University of Lethbridge. Faculty of Arts and Science
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Publisher
Lethbridge, Alta : University of Lethbridge, Dept. of Mathematics and Computer Science
Abstract
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.
Description
Keywords
algorithmic differentiation tools , black-box gradient , direction vectors , graph coloring , greedy CPR algorithm , sparsity patterns