Courant Minimax Principle
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Courant Minimax Principle

In mathematics, the Courant minimax principle gives the eigenvalues of a real symmetric matrix. It is named after Richard Courant.

## Introduction

The Courant minimax principle gives a condition for finding the eigenvalues for a real symmetric matrix. The Courant minimax principle is as follows:

For any real symmetric matrix A,

${\displaystyle \lambda _{k}=\min \limits _{C}\max \limits _{{\|x\|=1},{Cx=0}}\langle Ax,x\rangle ,}$

where C is any (k − 1) × n matrix.

Notice that the vector x is an eigenvector to the corresponding eigenvalue ?.

The Courant minimax principle is a result of the maximum theorem, which says that for q(x) = <Ax,x>, A being a real symmetric matrix, the largest eigenvalue is given by ?1 = max||x||=1q(x) = q(x1), where x1 is the corresponding eigenvector. Also (in the maximum theorem) subsequent eigenvalues ?k and eigenvectors xk are found by induction and orthogonal to each other; therefore, ?k = max q(xk) with <xj,xk> = 0, j < k.

The Courant minimax principle, as well as the maximum principle, can be visualized by imagining that if ||x|| = 1 is a hypersphere then the matrix A deforms that hypersphere into an ellipsoid. When the major axis on the intersecting hyperplane are maximized — i.e., the length of the quadratic form q(x) is maximized — this is the eigenvector, and its length is the eigenvalue. All other eigenvectors will be perpendicular to this.

The minimax principle also generalizes to eigenvalues of positive self-adjoint operators on Hilbert spaces, where it is commonly used to study the Sturm-Liouville problem.