J. A. Scott

J. D. Hogg, J. A. Scott

The open-source nonlinear solver Ipopt (https://projects.coin-or.org/Ipopt) is a widely-used software package for the solution of large-scale non-linear optimization problems. At its heart, it employs a third-party linear solver to solve a series of sparse symmetric indefinite systems. The speed, accuracy and robustness of the chosen linear solver is critical to the overall performance of Ipopt. In some instances, it can be beneficial to scale the linear system before it is solved. In this paper, different scaling algorithms are employed within Ipopt with a new linear solver HSL_MA97 from the HSL mathematical software library (http://www.hsl.rl.ac.uk). An extensive collection of problems from the CUTEr test set (http://www.cuter.rl.ac.uk) is used to illustrate the effects of scaling.

I. Daužickaitė, M. A. Freitag, S. Gürol et al.

Variational data assimilation is a technique for combining measured data with dynamical models. It is a key component of Earth system state estimation and is commonly used in weather and ocean forecasting. The approach involves a large-scale generalized nonlinear least-squares problem. Solving the resulting sequence of sparse linear subproblems requires the use of sophisticated numerical linear algebra methods. In practical applications, the computational demands severely limit the number of iterations of a Krylov subspace solver that can be performed and so high-quality preconditioners are vital. In this paper, we present a numerical linear algebra perspective on variational data assimilation and discuss contemporary solution methods for the challenges posed by large-scale geophysical applications. The principal contribution is a focused treatment of the underlying linear algebraic subproblems, accompanied by a concise and clear introduction to the essential concepts of variational data assimilation and an extensive bibliography.