The first FFT-based algorithm for numerical homogenization from high-resolution images was proposed by Moulinec and Suquet in 1994 as an alternative to finite elements and twenty years later, it is still widely used in computational micromechanics of materials. The method is based on an iterative solution to an integral equation of the Lippmann-Schwinger type, whose kernel can be explicitly expressed in the Fourier domain. Only recently, it has been recognized that the algorithm has a variational structure arising from a Fourier-Galerkin method. In this talk, I will show how this insight can be used to significantly improve the performance of the original Moulinec-Suquet solver. In particular, I will focus on (i) influence of an iterative solver used to solve the system of linear algebraic equations, (ii) effects of numerical integration of the Galerkin weak form, and (iii) convergence of an a-posteriori bound on the solution during iterations.
