Sparse multivariate function recovery from values with noise and outlier errors

In [Kaltofen and Yang, Proc. ISSAC 2013] we have gen-eralized algebraic error-correcting decoding to multivariate sparse rational function interpolation from evaluations that can be numerically inaccurate and where several evaluations can have severe errors (“outliers”). Here we present a dif-ferent algorithm that can interpolate a sparse multivariate rational function from evaluations where the error rate is 1/q for any q> 2, which our ISSAC 2013 algorithm could not handle. When implemented as a numerical algorithm we can, for instance, reconstruct a fraction of trinomials of degree 15 in 50 variables with non-outlier evaluations of relative noise as large as 10−7 and where as much as 1/4 of the 14717 evaluations are outliers with relative error as small as 0.01 (large outliers are easily located by our method). For the algorithm with exact arithmetic and exact values at non-erroneous points, we provide a proof that for random evaluations one can avoid quadratic oversampling. Our ar-gument already applies to our original 2007 sparse rational function interpolation algorithm [Kaltofen, Yang and Zhi, Proc. SNC 2007], where we have experimentally observed that for T unknown non-zero coefficients in a sparse candi-date ansatz one only needs T +O(1) evaluations rather than the proven O(T 2) (cf. Candès and Tao sparse sensing). Here we finally can give the probabilistic analysis for this fact