  A class of algorithms for the solution of discrete material optimization
problems in electromagnetic applications is discussed. The idea behind the
algorithm is similar to that of the sequential programming. However, in each
major iteration a model is established on the basis of an appropriately
parametrized material tensor. The resulting nonlinear parametrization is
treated on the level of the sub-problem, for which, globally optimal solutions
can be computed due to the block separability of the model. Although global
optimization of non-convex design problems is generally prohibitive, a well
chosen combination of analytic solutions along with standard global
optimization techniques leads to a very efficient algorithm for most relevant
material parametrizations. A global convergence result for the overall
algorithm is established. The effectiveness of the approach in terms of both
computation time and solution quality is demonstrated by numerical examples,
including the optimal design of cloaking layers for a nano-particle and the
identification of multiple materials with different optical properties in a
matrix.
