Hybrid methods for large sparse nonlinear least squares

Abstract

Hybrid methods are developed for improving the Gauss-Newton method in the case of large residual or ill-conditioned nonlinear least-square problems. These methods are used usually in a form suitable for dense problems. But some standard approaches are unsuitable, and some new possibilities appear in the sparse case. We propose efficient hybrid methods for various representations of the sparse problems. After describing the basic ideas that help deriving new hybrid methods, we are concerned with designing hybrid methods for sparse Jacobian and sparse Hessian representations of the least-square problems. The efficiency of hybrid methods is demonstrated by extensive numerical experiments.