Abstract
This is the first of two papers on multilinear problems, and it is devoted to the worst case setting. A second paper will analyze the average case setting. We show how to reduce the analysis of multilinear problems to linear subproblems. In particular, it is proven that adaption can help by a factor of at most k and k-linear problems. The error of multilinear algorithms is analyzed and optimality properties of spline algorithms for the Hilbert case are established. We illustrate our analysis with an example of a multilinear problem from signal processing.
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