Multipoint evaluation and interpolation

Evaluating a polynomial of degree less than $n$ at $n$ points by Horner's rule costs $n^2$ operations, and Lagrange interpolation through $n$ points costs the same. Both can be done in $O(M(n) \log n)$ operations by divide and conquer over a binary tree of products of linear factors. This was found independently by Fiduccia [Fid1972] and by Moenck and Borodin [MB1972; BorMoe1974]; Strassen [Str1973] showed that $\Omega(n \log n)$ nonscalar multiplications are necessary for generic points. Textbook accounts are [GG2003, Ch. 10] and [Ber2008], which also describes the scaled remainder tree; Bostan, Lecerf and Schost [BLS2003] use the transposition principle to reduce the constants.

When the points form a geometric progression $1, q, q^2, \ldots$, evaluation and interpolation are much cheaper: one or two multiplications, by Bluestein's chirp transform [Blu1970] and the algorithm of Bostan and Schost [BS2005].

Throughout, $x_0, \ldots, x_{n-1}$ are the points in $R = \mathbb{Z}/p\mathbb{Z}$ and $M(n)$ is the cost of a multiplication of length $n$, with $M(n)/n$ nondecreasing.

Generic version#

The subproduct tree is implemented once in gr_poly (_gr_poly_tree_build, _gr_poly_evaluate_vec_fast_precomp, _gr_poly_interpolation_weights, _gr_poly_interpolate_fast_precomp) for any commutative ring. Evaluation needs only divisions by the monic polynomials of the tree and therefore works over every ring; interpolation needs the weights $1 / \prod_{j \ne i} (x_i - x_j)$ and therefore points whose differences are invertible. nmod_poly, fmpz_mod_poly, fq_poly, arb_poly and acb_poly call these functions with their contexts, and multimodular interpolation over $\mathbb{Z}$ (fmpz_poly_interpolate_multi_mod) uses them modulo each prime. Over balls, the tree algorithms give correct enclosures, but the radii can grow much more than with evaluation point by point. The generic gr_poly_interpolate currently uses Newton interpolation with $O(n^2)$ operations, which also works over integral domains when the interpolating polynomial has coefficients in the ring; the fast functions must be called explicitly. The algorithms for geometric progressions are specific to nmod_poly.

The subproduct tree#

Assume first $n = 2^k$. The subproduct tree has the linear polynomials $x - x_i$ as leaves, and each internal node is the product of its two children. Level $i$ consists of the $n/2^i$ monic polynomials

of degree $2^i$, and the root is $P = P_{k,0} = \prod_l (x - x_l)$.

For general $n$ the same layout is used with $k = \lceil \log_2 n \rceil$; the last node of each level is the product of the remaining $n \bmod 2^i$ factors if that number is nonzero.

Evaluation by the remainder tree#

Correctness is Lemma 2. Each level costs $n/2^i$ divisions of a polynomial of degree less than $2^{i+1}$ by one of degree $2^i$, which with Newton division (Division with remainder) costs $O(M(n))$ per level. Hence evaluation costs $O(M(n) \log n)$, in addition to the tree. The constant can be lowered by precomputing the inverses of the reversed tree nodes, or by the transposed and scaled remainder trees of [BLS2003] and [Ber2008], which replace the divisions by middle products; FLINT uses the plain remainder tree.

Interpolation#

Let $y_0, \ldots, y_{n-1}$ be values and the $x_l$ pairwise distinct with all $x_l - x_m$ invertible. The interpolating polynomial of degree less than $n$ is given by Lagrange's formula

So the weights are obtained by one multipoint evaluation of $P'$ and $n$ inversions (or one batch inversion). The sum can then be computed going up the tree:

The cost is $O(M(n) \log n)$: one evaluation and, for the combination, two products per node, $O(M(n))$ per level.

For few points, quadratic methods are faster:

• Newton interpolation: divided differences $O(n^2)$, then conversion from the Newton basis $\prod_{m < l} (x - x_m)$ to the monomial basis in $O(n^2)$.
• Barycentric (Lagrange) interpolation: compute $P$, the weights $w_l$ directly as products, and add up $y_l w_l P/(x - x_l)$, each quotient obtained by synthetic division (_nmod_poly_div_root).

Geometric progressions#

Let the points be $x_j = q^j$, $0 \le j < n$, and write $q = r^2$ (FLINT is given $r$, so that no square root is needed).

The sum is a correlation of the vector $(f_i r^{-i^2})$ with the "chirp" sequence $c_k = r^{k^2}$, $0 \le k < \ell + n - 1$: with $a$ the reversal of $(f_i r^{-i^2})$, $f(q^j)$ is $r^{-j^2}$ times coefficient $\ell - 1 + j$ of $a \cdot c$, a middle product (Middle products and the transposition principle). So evaluation at $n$ points of a geometric progression costs one middle product and $O(n)$ multiplications. This is the chirp z-transform [RSR1969], also used to compute DFTs of arbitrary length; see [ASU1975] for its use in polynomial evaluation.

Interpolation at a geometric progression uses a $q$-analogue of Newton interpolation [BS2005, §5]. With $u_i = \prod_{k=1}^{i} (q^k - 1)$ and $Q_i = q^{i(i-1)/2}$, the Newton coefficients of the interpolant are the low coefficients of a single product

up to scaling by $Q_i$, and the change from the Newton basis $\prod_{m < l} (x - q^m)$ to the monomial basis is again one (transposed) product. Hence interpolation costs two products of length $n$ and $O(n)$ multiplications. It requires $q^k - 1$ to be invertible for $1 \le k < n$: the points must be distinct, and for composite modulus their differences must be invertible.

Extrapolation, computing $f(q^k)$ for $k$ outside $[0, \ell)$ from the values $f(q^0), \ldots, f(q^{\ell - 1})$ of a polynomial of length $\ell$, is a single middle product as well:

Use in FLINT#

Arbitrary points.

• _nmod_poly_tree_alloc, _nmod_poly_tree_build and _nmod_poly_tree_free manage the subproduct tree. tree[i] holds the nodes of level $i$ consecutively (each of length $2^i + 1$, the last possibly shorter). Level 1 is computed directly from the points and the root is not built, since evaluation reduces modulo the highest level below the length of $f$ and interpolation forms the top product only once, to obtain $P'$.
• nmod_poly_evaluate_nmod_vec uses Horner's rule at each point for polynomials of length less than 32 (nmod_poly_evaluate_nmod_vec_iter) and Algorithm 1 otherwise (nmod_poly_evaluate_nmod_vec_fast), which builds the tree for each call. _nmod_poly_evaluate_nmod_vec_fast_precomp takes a prebuilt tree, and accepts polynomials longer or shorter than the number of points: it first reduces $f$ modulo all nodes of the highest built level whose nodes have degree at most $\deg f$. The divisions use _gr_poly_divrem with the nmod context, so they switch to Newton division at the cutoffs of Division with remainder.
• nmod_poly_interpolate_nmod_vec uses Newton interpolation for $n < 6$ (nmod_poly_interpolate_nmod_vec_newton), the barycentric form for $n < 16$ (nmod_poly_interpolate_nmod_vec_barycentric) and Algorithm 2 otherwise (nmod_poly_interpolate_nmod_vec_fast). _nmod_poly_interpolation_weights and _nmod_poly_interpolate_nmod_vec_fast_precomp split the fast method into its two steps, so that the weights can be reused for many sets of values at the same points.
• nmod_poly_product_roots_nmod_vec computes $P$ alone, by multiplying in the linear factors one at a time for $n < 20$ and by splitting the points into two halves recursively otherwise.

Geometric progressions. nmod_geometric_progression_init prepares the evaluation, interpolation and extrapolation data for the points $1, r^2, r^4, \ldots, r^{2(\ell - 1)}$; _nmod_geometric_progression_init_function selects which of the three to prepare and can also store fft_small transforms of the fixed factors (the chirp sequence, the interpolation series and the extrapolation series), so that each later product needs two transforms instead of three. The products of evaluation and extrapolation are middle products in which the fixed factor $v$ is the long operand, and these are computed as transposed products (Transposed FFT): the precomputation stores the images of $v$ through the transposed inverse transform (_mul_precomp_init_tr), one transform of length $v_n$ rounded up to a multiple of 256 per prime, with $v_n = 2\ell - 1$ for the chirp sequence and $v_n = \ell - 1$ for each of the two extrapolation series, instead of a power of two at least $v_n$; each call then transforms the reversed short operand and applies the transposed forward transform (_nmod_poly_mul_tr_precomp). The interpolation series is stored as an ordinary transform (_mul_precomp_init). A call uses the stored images of $v$ only if the product would use fft_small anyway, if it fits, and if this is estimated to be cheaper than what _nmod_poly_mulmid_fft_small would do without the precomputation. For $p < 2^{\mathtt{FLINT\_BITS} - 1}$ the evaluation and interpolation data are computed with Shoup multiplication for the many products by powers of $r$. Then

• nmod_poly_evaluate_geometric_nmod_vec_fast evaluates by Lemma 5 with one middle product (nmod_poly_evaluate_geometric_nmod_vec_iter uses Horner's rule);
• nmod_poly_interpolate_geometric_nmod_vec_fast interpolates by the method of Bostan and Schost;
• nmod_poly_extrapolate_geometric extrapolates by Lemma 6 with one middle product.

These are the methods of choice whenever the evaluation points can be chosen freely, for example in evaluation–interpolation schemes for products of multivariate or matrix polynomials.

The functions are described, with examples, in Multipoint evaluation and interpolation.

@misc{flint-doc-algorithms-polynomials-subproduct-tree,
  author       = {{The FLINT team}},
  title        = {{Multipoint evaluation and interpolation}},
  howpublished = {FLINT: Fast Library for Number Theory, documentation, version 3.7.0-dev},
  year         = {2026},
  url          = {https://flintlib.org/doc/algorithms/polynomials/subproduct-tree.html},
  note         = {Generated 2026-11-02}
}