Sparse Matrix Structures and Higher Derivatives

In this talk we consider solving nonlinear systems of equations and the unconstrained minimization problem using iterative methods from
the Halley class. The methods in this class have in general local and third order rate of convergence. In the unconstrained optimization case
these methods will require the second and third derivative.  We say that the sparsity pattern of the third derivative (or tensor) is induced by
the sparsity pattern of the Hessian matrix.  The sparsity pattern of the  tensor can fairly easy be derived from the indices of the nonzero
elements of the Hessian. However, few datastructures for sparse symmetric matrices will store both the  row and column index of a
nonzero element. We will discuss some datastructures for matrices where the indices  of  nonzero elements of the tensor  can be computed. These will be compressed storage format using array of arrays (jagged) data structures for storing the second and third derivative of a multivariate function. Methods in the Halley class require solution of one or two linear systems of linear equations and we briefly discuss suitable termination criteria for the (inner) iterative method to achieve a cubic rate of convergence as well as direct methods. We also discuss techniques to compute the derivatives using coloring schemes to computesparse Jacobian and Hessian matrices.

Trond Steihaug, University of Bergen, Norway

 

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