## How does recursive FFT work?

The basic idea of the FFT is to apply divide and conquer. We divide the coefficient vector of the polynomial into two vectors, recursively compute the DFT for each of them, and combine the results to compute the DFT of the complete polynomial. A(x)=A0(x2)+xA1(x2).

## What is point value form of polynomial?

Another way of representing a polynomial is called point-value representation. In point-value form, multiplication 𝐶(𝑥) = 𝐴(𝑥)𝐵(𝑥) is given by 𝐶(𝑥𝑘) = 𝐴(𝑥𝑘) . 𝐵(𝑥𝑘) for any point (𝑥𝑘). If 𝐴 and 𝐵 are of degree-bound ‘n’, then 𝐶 is of degree-bound ‘2n’.

**What is the FFT algorithm?**

A fast Fourier transform (FFT) is an algorithm that computes the discrete Fourier transform (DFT) of a sequence, or its inverse (IDFT). Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa.

**How is FFT algorithm calculated?**

The FFT algorithm computes the DFT using O(N log N) multiplies and adds. There are many variants of the FFT algorithm. We’ll discuss one of them, the “decimation- in-time” FFT algorithm for sequences whose length is a power of two (N = 2r for some integer r).

### What is Strassen matrix multiplication?

Strassen’s Algorithm is an algorithm for matrix multiplication. Strassen algorithm is a recursive method for matrix multiplication where we divide the matrix into 4 sub-matrices of dimensions n/2 x n/2 in each recursive step. For example, consider two 4 x 4 matrices A and B that we need to multiply.

### How do you multiply polynomials easily?

Using FOIL to Multiply Binomials

- Multiply the first terms of each binomial.
- Multiply the outer terms of the binomials.
- Multiply the inner terms of the binomials.
- Multiply the last terms of each binomial.
- Add the products.
- Combine like terms and simplify.

**How is time complexity calculated for polynomial multiplication?**

It’s just calculation of values of A (x) at some x for n different points, so time complexity is O (). Now that the polynomial is converted into point value, it can be easily calculated C (x) = A (x)*B (x) again using horner’s method. This takes O (n) time.

**Which is the Fourier transformation of a polynomial?**

DFT is evaluating values of polynomial at n complex nth roots of unity . So, for k = 0, 1, 2, …, n-1, y = (y0, y1, y2, …, yn-1) is Discrete fourier Transformation (DFT) of given polynomial. The product of two polynomials of degree-bound n is a polynomial of degree-bound 2n.

#### When to use DFT for complex roots of unity?

If we choose “complex roots of unity” as the evaluation points, we can produce a point-value representation by taking the discrete Fourier transform (DFT) of a coefficient vector. We can perform the inverse operation, interpolation, by taking the “inverse DFT” of point-value pairs, yielding a coefficient vector.

#### How is inverse DFT performed in Fast Fourier transform?

We can perform the inverse operation, interpolation, by taking the “inverse DFT” of point-value pairs, yielding a coefficient vector. Fast Fourier Transform (FFT) can perform DFT and inverse DFT in time O (nlogn).