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In linear algebra, a Vandermonde matrix, named after Alexandre-Théophile Vandermonde, is a matrix with the terms of a geometric progression in each row: an matrix with entries , the jth power of the number , for all zero-based indices and . Most authors define the Vandermonde matrix as the transpose of the above matrix. The determinant of a square Vandermonde matrix (when ) is called a Vandermonde determinant or Vandermonde polynomial. Its value is: This is non-zero if and only if all are distinct (no two are equal), making the Vandermonde matrix invertible. The polynomial interpolation problem is to find a polynomial which satisfies for given data points . This problem can be reformulated in terms of linear algebra by means of the Vandermonde matrix, as follows. computes the values of at the points via a matrix multiplication , where is the vector of coefficients and is the vector of values (both written as column vectors): If and are distinct, then V is a square matrix with non-zero determinant, i.e. an invertible matrix. Thus, given V and y, one can find the required by solving for its coefficients in the equation : . That is, the map from coefficients to values of polynomials is a bijective linear mapping with matrix V, and the interpolation problem has a unique solution. This result is called the unisolvence theorem, and is a special case of the Chinese remainder theorem for polynomials. In statistics, the equation means that the Vandermonde matrix is the design matrix of polynomial regression. In numerical analysis, solving the equation naïvely by Gaussian elimination results in an algorithm with time complexity O(n3). Exploiting the structure of the Vandermonde matrix, one can use Newton's divided differences method (or the Lagrange interpolation formula) to solve the equation in O(n2) time, which also gives the UL factorization of . The resulting algorithm produces extremely accurate solutions, even if is ill-conditioned. (See polynomial interpolation.) The Vandermonde determinant is used in the representation theory of the symmetric group.
[2]Paolo Prandoni, [3]LCAV, EPFL
The Parks-McClellan (PM) algorithm is a clever application of advanced
polynomial fitting techniques to the problem of FIR filter design. In
this notebook we will explore the key ideas behind the method by
considering the design of a simple Type-I lowpass filter.
NOTE: This notebook uses interactive widgets. If the interactive
controls do not work or show up, please fo [2]Paolo Prandoni, [3]LCAV, EPFL
In [1]:
[2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will implement a simple fractional resampler that
uses local Lagrange interpolation. The resampler can be used to perform
any rational sampling rate change (but beware of aliasing when
downsampling!)
In [1]:
import numpy as np
import IPython
from scipy.io import wavfile
Local Lagrange interpolation[4]¶
To perform resampling we will need to compute subsample values and, in [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will explore various ways to implement a system
that changes the voice of a speaker. Typical applications for these
techniques, strictly sorted by plausibility, include international spy
missions and prank telephone calls, and devices such as the one
displayed here can be easily bought on the web... or in a novelty shop.
No description has been provided for t