Vandermonde Matrix, Polynomial Interpolation & Orthogonal Basis

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In summary, in polynomial interpolation, there is a connection between the Vandermonde matrix, the monomial basis, and the fact that the monomial basis is not a good basis due to its components not being very orthogonal. The reason why a Vandermonde matrix is often ill-conditioned is not entirely clear, and an orthogonal basis typically leads to better conditioned problems. Orthogonality allows for the isolation of arbitrary effects to one subset of elements, simplifying complicated processes.
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azay
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In polynomial interpolation:

I see some connection between:

The Vandermonde matrix, the monomial basis and the fact that 'the monomial basis is not a good basis because it's components are not very orthogonal'.

Now, I still don't really grasp sufficiently the reason why exactly a Vandermonde matrix is often ill-conditioned. Also, I don't feel I understand why an orthogonal basis in general leads to better conditioned problems, how self-evident it may look from a certain point of view.

Any insights?
 
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  • #2
Here is a rapid insight,
Obtain, the vector below, at each cases
[itex]\left[\begin{array}{cc}1 &0\\0 &1\end{array}\right]\left(\begin{array}{c}x\\y\end{array}\right)= \left(\begin{array}{c}3\\5\end{array}\right)[/itex]


[itex]\left[\begin{array}{cc}1 &-1\\1 &1\end{array}\right]\left(\begin{array}{c}x\\y\end{array}\right)= \left(\begin{array}{c}3\\5\end{array}\right)[/itex]

Please repeat it for the vector [tex]\left(\begin{array}{c}3.1\\5\end{array}\right)[/tex]

You can relate orthogonality to being able to isolate arbitrary effects to one subset of orthogonal elements. Usually, complicated things are just for simplicity...
 

1. What is a Vandermonde matrix?

A Vandermonde matrix is a special type of matrix that is constructed by raising a sequence of numbers to different powers. The rows of the matrix are created by using the sequence of numbers as the coefficients of a polynomial, with each row representing a different power of the polynomial. The columns of the matrix are created by using the same sequence of numbers as the base of an exponential term, with each column representing a different power of the base. The Vandermonde matrix is widely used in mathematics and computer science for applications such as polynomial interpolation, solving linear systems, and finding roots of polynomials.

2. How is polynomial interpolation related to the Vandermonde matrix?

Polynomial interpolation is a method for finding a polynomial function that passes through a set of given data points. The Vandermonde matrix is used in polynomial interpolation by setting up a system of linear equations using the given data points and their corresponding powers in the polynomial. The coefficients of the polynomial can then be determined by solving this system of equations using the Vandermonde matrix.

3. What is an orthogonal basis?

An orthogonal basis is a set of vectors that are mutually perpendicular to each other. This means that the dot product of any two vectors in the set is equal to zero. An orthogonal basis is useful because it simplifies many mathematical calculations, such as finding the inverse or determinant of a matrix. The most well-known example of an orthogonal basis is the set of unit vectors in Cartesian coordinates (i,j,k).

4. How is the Gram-Schmidt process used to find an orthogonal basis?

The Gram-Schmidt process is a method for finding an orthogonal basis for a given set of vectors. It involves taking the first vector in the set and keeping it as is, then finding the projection of the next vector onto the first vector and subtracting this projection from the second vector. This produces a new vector that is perpendicular to the first vector. The process is then repeated for each subsequent vector until an orthogonal basis is formed.

5. What are some applications of orthogonal bases?

Orthogonal bases have many applications in mathematics, physics, and engineering. They are used in signal processing, image compression, and solving differential equations. In linear algebra, orthogonal bases are used to find eigenvalues and eigenvectors of a matrix, which have applications in areas such as quantum mechanics and data analysis. In addition, orthogonal bases are also used in the construction of wavelets, which are fundamental in the field of data compression.

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