The interpolation step in the Guruswami-Sudan algorithm is a bivariate interpolation problem with multiplicities commonly solved in the literature using either structured linear algebra or basis reduction of polynomial lattices. This problem has been extended to three or more variables; for this generalization, all fast algorithms proposed so far rely on the lattice approach. In this paper, we reduce this multivariate interpolation problem to a problem of simultaneous polynomial approximations, which we solve using fast structured linear algebra. This improves the best known complexity bounds for the interpolation step of the list-decoding of Reed-Solomon codes, Parvaresh-Vardy codes, and folded Reed-Solomon codes. In particular, for Reed-Solomon list-decoding with re-encoding, our approach has complexity O˜( ω−1 m 2 (n − k)), where , m, n, and k are the list size, the multiplicity, the number of sample points, and the dimension of the code, and ω is the exponent of linear algebra; this accelerates the previously fastest known algorithm by a factor of /m.

International audienceWe study the cost of multiplication modulo triangular families of polynomials. Following previous work by Li et al. (2007), we propose an algorithm that relies on homotopy and fast evaluation-interpolation techniques. We obtain a quasi-linear time complexity for substantial families of examples, for which no such result was known before. Applications are given notably to additions of algebraic numbers in small characteristic

We provide algorithms computing power series solutions of a large class of differential or q-differential equations or systems. Their number of arithmetic operations grows linearly with the precision, up to logarithmic terms.

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