  In this work, we address the implementation and performance of inexact
Newton-Krylov and quasi-Newton algorithms, more specifically the BFGS method,
for the solution of the nonlinear elasticity equations, and compare them to a
standard Newton-Krylov method. This is done through a systematic analysis of
the performance of the solvers with respect to the problem size, the magnitude
of the data and the number of processors in both almost incompressible and
incompressible mechanics. We consider three test cases: Cook's membrane
(static, almost incompressible), a twist test (static, incompressible) and a
cardiac model (complex material, time dependent, almost incompressible). Our
results suggest that quasi-Newton methods should be preferred for compressible
mechanics, whereas inexact Newton-Krylov methods should be preferred for
incompressible problems. We show that these claims are also backed up by the
convergence analysis of the methods. In any case, all methods present adequate
performance, and provide a significant speed-up over the standard Newton-Krylov
method, with a CPU time reduction exceeding 50% in the best cases.
