Evaluation Basis
$$ \gdef\vec#1{\bm{#1}} \gdef\delim#1#2#3{\mathopen{}\mathclose{\left#1 #2 \right#3}} \gdef\p#1{\delim({#1})} \gdef\Mod#1{\delim[{#1}]} \gdef\F{\mathbb{F}} \gdef\G{\mathbb{G}} \gdef\g{\mathrm{g}} \gdef\NTT{\mathsf{NTT}} \gdef\MSM{\mathsf{MSM}} \gdef\Mul{\mathsf{Mul}} \gdef\Mulc{\mathsf{Mul}^{\mathsf{C}}} \gdef\Add{\mathsf{Add}} \gdef\D#1{\mathrm{\partial}_{#1}} \gdef\Z{\mathrm{Z}} \gdef\T{\mathsf{T}} \gdef\I{\mathrm{I}} \gdef\J{\mathrm{J}} \gdef\ω{\mathrm{ω}} \gdef\diag{\operatorname{diag}} \gdef\set#1{\delim\{{#1}\}} $$
Monomial basis
Given a field $\F$ and a polynomial ring $\F[X]$ over it. A $n$-term polynomials $P∈\F[X]$ can be written in monomial form
$$ P(X) = c_0 + c_1 ⋅ X + c_2 ⋅ X^2 + ⋯ + c_{n-1} ⋅ X^{n-1} $$
where $\vec c ∈ \F^n$ is the vector of coefficients. This is a basis in the linear algebra sense as we can construct a vector $\vec m ∈ \F[X]^n$ such that $P$ is the inner product $\vec c ⋅ \vec m$. Such a $\vec m$ is called a standard basis.
$$ P(X) = \begin{bmatrix} c_0 \\ c_1 \\ c_2 \\ ⋮ \\ c_{n-1} \end{bmatrix} ⋅ \begin{bmatrix} 1 \\ X \\ X^2 \\ ⋮ \\ X^{n-1} \end{bmatrix} $$
Zero polynomial
Polynomials that can be factored into a constant linear factors are called splitting.
$$ P(X) = a⋅(X - x_0)⋅(X - x_1)⋅⋯⋅(X - x_{n-1}) $$
the set $\mathcal S \subset \F$ are called roots as $P(x) = 0$ iff $x ∈ \mathcal S$. Define the zero polynomial over a set $\mathcal S$ as
$$ \Z_{\mathcal S}(X) = \prod_{x∈\mathcal X} \p{X - x} $$
this is the minimal polynomial such that
$$ \forall_{x ∈ \mathcal S}\quad \Z_{\mathcal S}(x) = 0 $$
the roots allow for some set theoretic operations (todo. generalize to multisets)
$$ \begin{aligned} \Z_{\mathcal A \cup \mathcal B}(X) &= \frac{\Z_{\mathcal A}(X)⋅\Z_{\mathcal B}(X)}{\Z_{\mathcal A ∩ \mathcal B}(X)} & \Z_{\mathcal A \setminus \mathcal B}(X) &= \frac{\Z_{\mathcal A}(X)}{\Z_{\mathcal A ∩ \mathcal B}(X)} \\ \end{aligned} $$
in particular for $x ∈ \mathcal S$:
$$ \Z_{\mathcal S \setminus \set{x}}(X) = \frac{\Z_{\mathcal S}(X)}{X - x} $$
Derivative
The derivative of $\Z_{\mathcal S}$ can be found using the product rule or the logarithmic derivative
$$ \begin{aligned} \Z_{\mathcal S}'(X) &= \sum_{x∈\mathcal S} \Z_{\mathcal S \setminus \set{x}}(X) = \sum_{x∈\mathcal S} \frac{\Z_{\mathcal S}(X)}{X - x} = \Z_{\mathcal S}(X) ⋅ \sum_{x∈\mathcal S} \frac{1}{X - x} \end{aligned} $$
The first form is suitable to evaluate on the domain $\mathcal S$, which has an elegant result:
$$ \begin{aligned} \forall_{x ∈ \mathcal S}\quad \Z_{\mathcal S}'(x) = \Z_{\mathcal S \setminus \set{x}}(x) \end{aligned} $$
Specific values
If $\mathcal S$ are the $n$-th roots of unity $\mathcal Ω_n = \set{1, ω_n, ω_n^2, …, ω_n^{n-1}}$ the zero polynomial has a sparse monomial form:
$$ \begin{aligned} \Z_{\mathcal Ω_n}(X) &= X^n - 1 &\quad \Z_{\mathcal Ω_n}'(X) &= n⋅X^{n-1} \end{aligned} $$
If $\mathcal S$ is the sequence $[0,n) = \set{0,1,2,…,n-1}$ the zero polynomial is the falling factorial. For $(-n, 0]$ it is the rising factorial:
$$ \begin{aligned} \Z_{[0,n)}(X) &= X^{\underline{n}} &\quad \Z_{(-n,0]}(X) &= X^{\overline{n}} & \end{aligned} $$
These are related to the factorial and gamma function
$$ \begin{aligned} \Z_{[0,n)}(X) &= \frac{\Gamma\p{X + 1}}{\Gamma\p{X - n + 1}} &\quad \Z_{(-n,0]}(X) &= \frac{\Gamma\p{X + n}}{\Gamma\p{X}} \end{aligned} $$
In general they have lots of uses in combinatorics and umbral calculus (todo explore)
Evaluation basis
Given an evaluation domain $\vec x ∈ \F^n$ containing $n$ distinct elements, we can also represent $P$ uniquely by its evaluations $\vec y ∈ \F^n$ on this domain $y_i = P(x_i)$.
We can again construct a standard basis $\vec L ∈ \F[X]^n$, in this case called a Lagrange basis
$$ L_i(X) = \frac{\Z_{\vec x \setminus \set{x_i}}(X)}{\Z_{\vec x \setminus \set{x_i}}(x_i)} = \frac{\Z_{\vec x \setminus \set{x_i}}(X)}{\Z_{\vec x}'(x_i)} $$
these satisfy $L_i(x_j) = δ_{ij}$.
If we define normalization constants $l_i = \p{Z_{\vec x \setminus \set{x_i}}(x_i)}^{-1}$ then we get the barycentric form:
$$ L_i(X) = \Z_{\vec x}(X)⋅\frac{l_i}{X - x_i} $$
this form is useful for evaluating the basis in $x∉\vec x$ as $Z_{\vec x}(x)$ can be computed once. The fraction is ill-defined on $\vec x$ though, so this form is more appropriate
$$ L_i(X) = l_i⋅\frac{\Z_{\vec x}(X)}{X - x_i} $$
Derivative
Say we have $P(X) ∈ \F[X]$ represented in evaluations basis on domain $\vec x$ as $\vec p$ and we wish to know its derivative $P'(X)$ in the same basis.
$$ \begin{aligned} P(X) &= \sum_{i∈[0,n)} p_i ⋅ L_i(x) &\quad P'(X) &= \sum_{i∈[0,n)} p_i ⋅ L_i'(x) \end{aligned} $$
so to compute $P'(X)$ in the evaluation basis we need to know $L_i'(x_j)$. Start with computing the derivate
$$ L_i'(X) = \sum_{x∈\mathcal S \setminus \set{x_i}} \frac{L_i(X)}{X - x} $$
$$ L_i'(X) = \frac{\Z_{\vec x \setminus \set{x_i}}'(X)}{\Z_{\vec x \setminus \set{x_i}}(x_i)} $$
$$ \begin{aligned} L_i'(X) &= l_i⋅ \p{\frac{Z_{\vec x}'(X)}{X - x_i} - \frac{Z_{\vec x}(X)}{\p{X - x_i}^2}} \\ &= l_i⋅ \p{ \frac{\sum_{x∈\mathcal S} \frac{\Z_{\mathcal S}(X)}{X - x}}{X - x_i} - \frac{Z_{\vec x}(X)}{\p{X - x_i}^2}} \\ &= l_i⋅ \p{ \sum_{x∈\mathcal S} \frac{\Z_{\mathcal S}(X)}{\p{X - x}⋅\p{X - x_i}} - \frac{Z_{\vec x}(X)}{\p{X - x_i}^2} } \\ &= l_i⋅\sum_{x∈\mathcal S \setminus \set{x_i}} \frac{\Z_{\mathcal S}(X)}{\p{X - x}⋅\p{X - x_i}} \\ &= l_i ⋅ \frac{\Z_{\mathcal S}(X)}{X - x_i}⋅\sum_{x∈\mathcal S \setminus \set{x_i}} \frac{1}{X - x} \\ &= l_i ⋅ \frac{\Z_{\mathcal S}(X)}{X - x_i}⋅\sum_{x∈\mathcal S \setminus \set{x_i}} \frac{1}{X - x} \\ &= L_i(X) ⋅ \sum_{x∈\mathcal S \setminus \set{x_i}} \frac{1}{X - x} \\ &= \sum_{x∈\mathcal S \setminus \set{x_i}} \frac{L_i(X)}{X - x} \\ \end{aligned} $$
The on-diagonal elements $L_i'(x_i)$ are
$$ L_i'(x_i) = \frac{\Z_{\vec x \setminus \set{x_i}}'(x_i)}{\Z_{\vec x \setminus \set{x_i}}(x_i)} = \sum_{x ∈ \vec x \setminus \set{x_i}} \frac{1}{x_j - x} $$
The off-diagonal elements $L_i'(x_j)$ are
$$ L_i'(x_j) = \frac{\Z_{\vec x \setminus \set{x_i}}'(x_j)}{\Z_{\vec x \setminus \set{x_i}}(x_i)} = \frac{\Z_{\vec x \setminus \set{x_i,x_j}}(x_j)}{\Z_{\vec x \setminus \set{x_i}}(x_i)} = \frac{1}{x_j - x_i}⋅ \frac{\Z_{\vec x \setminus \set{x_j}}(x_j)}{\Z_{\vec x \setminus \set{x_i}}(x_i)} = \frac{1}{x_j - x_i}⋅ \frac{\Z_{\vec x}'(x_j)}{\Z_{\vec x}'(x_i)} $$
Todo. What is the rank of this matrix?
Roots of unity
On-diagonal
$$ L_i'(\ω^i) = \frac{\Z_{Ω_n \setminus \set{\ω^i}}'(\ω^i)}{\Z_{Ω_n \setminus \set{\ω^i}}(\ω^i)} = \frac{\Z_{Ω_n \setminus \set{\ω^i}}'(\ω^i)}{\Z_{Ω_n}'(\ω^i)} = \frac{? }{n⋅\ω^{-i}} $$
$$ \Z_{\mathcal A \setminus \mathcal B}'(X) = \D{X} \p{\frac{\Z_{\mathcal A}(X)}{\Z_{\mathcal A ∩ \mathcal B}(X)}} = \frac{\Z_{\mathcal A}'(X)⋅\Z_{\mathcal A ∩ \mathcal B}(X) - \Z_{\mathcal A}(X)⋅\Z_{\mathcal A ∩ \mathcal B}'(X)}{\p{\Z_{\mathcal A ∩ \mathcal B}(X)}^2} $$
$$ \begin{aligned} \Z_{Ω_n \setminus \set{\ω^i}}'(X) &= \frac{\Z_{Ω_n}'(X)⋅\Z_{\set{\ω^i}}(X) - \Z_{Ω_n}(X)⋅\Z_{\set{\ω^i}}'(X)}{\p{\Z_{\set{\ω^i}}(X)}^2} \\ &= \frac{\p{n⋅X^{n -1}}⋅\p{X - \ω^i} - \p{X^n-1}}{\p{X - \ω^i}^2} \\ &= \frac{n⋅X^{n -1}}{X - \ω^i}
- \frac{\p{X^n-1}}{\p{X - \ω^i}^2} \end{aligned} $$
Off-diagonal
$$ L_i'(\ω^j) = \frac{1}{\ω^j - \ω^i}⋅ \frac{\Z_{Ω_n}'(\ω^j)}{\Z_{Ω_n}'(\ω^i)} = \frac{\ω^{i -j}}{\ω^j - \ω^i} $$
This is a Cauchy-like matrix
to do. Test these results numerically.
https://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917825-9/S0025-5718-1988-0917825-9.pdf
https://link.springer.com/article/10.1007/s11075-022-01391-y
https://arxiv.org/pdf/1506.02285.pdf
CRT Basis
$$ E_i(X) = \delim[{\frac{m_1(X)}{M(X)}}]_{m_1}⋅\frac{M(X)}{m_1(X)} $$
NTT Solution
Pick $\vec x$ roots of unity and use $\NTT_n$ to convert to monomial form. Convert back to a coset $\vec y$ using $\NTT_n$. Square all values $2⋅n⋅\Mul_{\F}$, convert to monomial form $\NTT_{2⋅n}$. We now have coefficients of $P^2(X)$.
Basis transformations
Can every polynomial basis be written as a combination of point and derivative?
- Polynomial bases
- Evaluation bases
- Roots of unity bases
- Derivative bases (Taylor series)
- Monomial basis
- Mixed point/derivative bases
- https://en.wikipedia.org/wiki/Category:Orthogonal_polynomials
- Are there more?
- Evaluation bases
The conversion between monomial and roots-of-unity is the $\NTT$.
Multiplication by a constant polynomial
Division by a constant polynomial
Modulo operations
Derivatives
The formal derivative is a linear operator.
$\D{X} P(X)$
$$ \D{X}\p{a_0 + a_1 ⋅X + a_2 ⋅ X^2 + ⋯} = a_1 + 2 ⋅ a_2 ⋅ X + 3⋅a_3⋅X^3 ⋯ $$
Given a basis, linear operators can be expressed as a matrix. The monomial basis almost diagonalizes the derivative operator:
$$ \begin{bmatrix} 0 & 1 & 0 & 0 \\ 0 & 0 & 2 & 0 \\ 0 & 0 & 0 & 3 \\ 0 & 0 & 0 & 0 \\ \end{bmatrix} $$
Q: Which basis diagonalizes it? Is there always such a basis?
https://en.wikipedia.org/wiki/Multiplier_(Fourier_analysis)#On_the_unit_circle
Multiplications
$P(c ⋅ X)$
$$ a_0 + a_1⋅(c⋅ X) + a_2 ⋅ (c ⋅ X)^2 + ⋯ = a_0 + a_1 ⋅ c ⋅ X + a_2 ⋅ c^2 ⋅ X^2 + ⋯ $$
$$ \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & c & 0 & 0 \\ 0 & 0 & c^2 & 0 \\ 0 & 0 & 0 & c^3 \\ \end{bmatrix} $$
Translations
$P(X+δ)$
$$ a_0 + a_1⋅(X + δ) + a_2 ⋅ (X + δ)^2 + ⋯ = \p{a_0 + a_1⋅δ + a_2 ⋅δ^2 + ⋯} + \p{a_1 + 2⋅a_2⋅δ + ⋯}⋅X + \p{a_2 + ⋯} ⋅ X^2 + ⋯ $$
$$ (X + δ)^2 = X^2 + 2⋅δ⋅X + δ^2 $$
Chinese Remainder Theorem
A.B mod X-1
https://en.wikipedia.org/wiki/Companion_matrix#Diagonalizability
HSS Matrices: https://mipals.github.io/pubs/matrix/hss/