A Greedy Subspace Method for Computing the ℒ∞‐Norm

We consider the computation of the L∞-norm for a general class of L∞-functions and focus on the case where the function is represented in terms of large-scale matrix-valued factors. We propose a subspace projection method to obtain reduced approximations of this function by interpolation techniques. The L∞-norms are computed for the resulting reduced functions, then the subspaces are refined by means of the optimizer of the L∞-norm of the reduced function. In this way we obtain much better performance compared to existing methods.