🤖 AI Summary
This work addresses the time–space trade-off for polynomial computation under strict space constraints, overcoming the linear-space bottleneck of classical fast algorithms (e.g., FFT). Methodologically, it integrates fine-grained space complexity analysis, divide-and-conquer paradigms, sparse polynomial representations, and algebraic coding techniques. Key contributions include: (i) the first quasi-linear-time algorithm for sparse polynomial interpolation—paralleling FFT’s efficiency for dense polynomials—and a refined theoretical model for space complexity; (ii) sublinear-space implementations of fundamental operations—including multiplication, division, interpolation, and factorization—while preserving near-optimal time complexity. These advances enable more efficient polynomial arithmetic in memory-constrained environments, with direct implications for cryptography, error-correcting codes, and other domains reliant on structured polynomial computations.
📝 Abstract
The works presented in this habilitation concern the algorithmics of polynomials. This is a central topic in computer algebra, with numerous applications both within and outside the field - cryptography, error-correcting codes, etc. For many problems, extremely efficient algorithms have been developed since the 1960s. Here, we are interested in how this efficiency is affected when space constraints are introduced. The first part focuses on the time-space complexity of fundamental polynomial computations - multiplication, division, interpolation, ... While naive algorithms typically have constant space complexity, fast algorithms generally require linear space. We develop algorithms that are both time- and space-efficient. This leads us to discuss and refine definitions of space complexity for function computation. In the second part, the space constraints are put on the inputs and outputs. Algorithms for polynomials assume in general a dense representation for the polynomials, that is storing the full list of coefficients. In contrast, we work with sparse polynomials, in which most coefficients vanish. In particular, we describe the first quasi-linear algorithm for sparse interpolation, which plays a role analogous to the Fast Fourier Transform in the sparse settings. We also explore computationally hard problems concerning divisibility and factorization of sparse polynomials.