Jacobi eigenvalue algorithm

Encyclopedia

In numerical linear algebra

, the

for the calculation of the eigenvalues and eigenvectors of a real

symmetric matrix. It is named after Carl Gustav Jacob Jacobi, who first proposed the method in 1846, but only became widely used in the 1950s with the advent of computers.

is symmetric and similar to A.

Furthermore, A′ has entries:

where s = sin(θ) and c = cos(θ).

Since G is orthogonal, A and A′ have the same Frobenius norm ||·||

Set this equal to 0, and rearrange:

if

In order to optimize this effect, A

The Jacobi eigenvalue method repeatedly performs rotations until the matrix becomes almost diagonal. Then the elements in the diagonal are approximations of the (real) eigenvalues of A.

or ,

i.e. the sequence of Jacobi rotations converges at least linearly by a factor to a diagonal matrix.

A number of N Jacobi rotations is called a sweep; let denote the result. The previous estimate yields

i.e. the sequence of sweeps converges at least linearly with a factor ≈ .

However the following result of Schönhage yields locally quadratic convergence. To this end let S have m distinct eigenvalues with multiplicities and let d > 0 be the smallest distance of two different eigenvalues. Let us call a number of

Jacobi rotations a Schönhage-sweep. If denotes the result then

Thus convergence becomes quadratic as soon as .

Typically the Jacobi method converges within numerical precision after a small number of sweeps. Note that multiple eigenvalues reduce the number of iterations since .

It calculates a vector e which contains the eigenvalues and a matrix E which contains the corresponding eigenvectors, i.e. is an eigenvalue and the column an orthonormal eigenvector for , i = 1, …, n.

i, k, l, m, state ∈

s, c, t, p, y ∈

ind ∈

changed ∈

m := k+1

y := e

┌

│S

│

│S

└

! init e, E, and arrays ind, changed

E := I; state := n

m := 1 ! find index (k,l) of pivot p

k := m; l := ind

! calculate c = cos φ, s = sin φ

y := (e

s := √(p

S

! rotate rows and columns k and l

! rotate eigenvectors

┌

│E

│

│E

└

! rows k, l have changed, update rows ind

ind

Then jacobi produces the following eigenvalues and eigenvectors after 3 sweeps (19 iterations) :

these link can explain the example

http://www.scribd.com/doc/24953275/Jacobi-Transformation-Used-for-Eigenproblems-for-Symmetric-Matrix

values are easily calculated.

Singular values

2-norm and spectral radius

Condition number

Rank

Pseudo-inverse

Least squares solution

Matrix exponential

Linear differential equations

, general nonsymmetric real and complex matrices as well as block matrices.

Since singular values of a real matrix are the square roots of the eigenvalues of the symmetric matrix it can also be used for the calculation of these values. For this case, the method is modified in such a way that S must not be explicitly calculated which reduces the danger of round-off error

s. Note that with .

The Jacobi Method is also well suited for parallelism.

Numerical linear algebra

Numerical linear algebra is the study of algorithms for performing linear algebra computations, most notably matrix operations, on computers. It is often a fundamental part of engineering and computational science problems, such as image and signal processing, Telecommunication, computational...

, the

**Jacobi eigenvalue algorithm**is an iterative methodIterative method

In computational mathematics, an iterative method is a mathematical procedure that generates a sequence of improving approximate solutions for a class of problems. A specific implementation of an iterative method, including the termination criteria, is an algorithm of the iterative method...

for the calculation of the eigenvalues and eigenvectors of a real

Real number

In mathematics, a real number is a value that represents a quantity along a continuum, such as -5 , 4/3 , 8.6 , √2 and π...

symmetric matrix. It is named after Carl Gustav Jacob Jacobi, who first proposed the method in 1846, but only became widely used in the 1950s with the advent of computers.

## Description

Let A be a symmetric matrix, and G = G(i,j,θ) be a Givens rotation matrix. Then:is symmetric and similar to A.

Furthermore, A′ has entries:

where s = sin(θ) and c = cos(θ).

Since G is orthogonal, A and A′ have the same Frobenius norm ||·||

_{F}(the square-root sum of squares of all components), however we can choose θ such that A′_{ij}= 0, in which case A′ has a larger sum of squares on the diagonal:Set this equal to 0, and rearrange:

if

In order to optimize this effect, A

_{ij}should be the largest off-diagonal component, called the pivot.The Jacobi eigenvalue method repeatedly performs rotations until the matrix becomes almost diagonal. Then the elements in the diagonal are approximations of the (real) eigenvalues of A.

## Convergence

If is a pivot element, then by definition for . Since S has exactly 2 N := n ( n - 1) off-diag elements, we have or . This impliesor ,

i.e. the sequence of Jacobi rotations converges at least linearly by a factor to a diagonal matrix.

A number of N Jacobi rotations is called a sweep; let denote the result. The previous estimate yields

- ,

i.e. the sequence of sweeps converges at least linearly with a factor ≈ .

However the following result of Schönhage yields locally quadratic convergence. To this end let S have m distinct eigenvalues with multiplicities and let d > 0 be the smallest distance of two different eigenvalues. Let us call a number of

Jacobi rotations a Schönhage-sweep. If denotes the result then

- .

Thus convergence becomes quadratic as soon as .

## Cost

Each Jacobi rotation can be done in n steps when the pivot element p is known. However the search for p requires inspection of all N ≈ ½ n^{2}off-diag elements. We can reduce this to n steps too if we introduce an additional index array with the property that is the index of the largest element in row i, (i = 1, …, n − 1) of the current S. Then (k, l) must be one of the pairs . Since only columns k and l change, only must be updated, which again can be done in n steps. Thus each rotation has O(n) cost and one sweep has O(n^{3}) cost which is equivalent to one matrix multiplication. Additionally the must be initialized before the process starts, this can be done in n^{2}steps.Typically the Jacobi method converges within numerical precision after a small number of sweeps. Note that multiple eigenvalues reduce the number of iterations since .

## Algorithm

The following algorithm is a description of the Jacobi method in math-like notation.It calculates a vector e which contains the eigenvalues and a matrix E which contains the corresponding eigenvectors, i.e. is an eigenvalue and the column an orthonormal eigenvector for , i = 1, …, n.

**procedure**jacobi(S ∈**R**^{n×n};**out**e ∈**R**^{n};**out**E ∈**R**^{n×n})**var**i, k, l, m, state ∈

**N**s, c, t, p, y ∈

**R**ind ∈

**N**^{n}changed ∈

**L**^{n}**function**maxind(k ∈**N**) ∈**N**! index of largest off-diagonal element in row km := k+1

**for**i := k+2**to**n**do****if**│S_{ki}│ > │S_{km}│**then**m := i**endif****endfor****return**m**endfunc****procedure**update(k ∈**N**; t ∈**R**) ! update e_{k}and its statusy := e

_{k}; e_{k}:= y+t**if**changed_{k}and (y=e_{k})**then**changed_{k}:= false; state := state−1**elsif**(not changed_{k}) and (y≠e_{k})**then**changed_{k}:= true; state := state+1**endif****endproc****procedure**rotate(k,l,i,j ∈**N**) ! perform rotation of S_{ij}, S_{kl}┌

_{ }┐ ┌ ┐┌_{ }┐│S

_{kl}│ │c −s││S_{kl}││

_{ }│ := │ ││_{ }││S

_{ij}│ │s c││S_{ij}│└

_{ }┘ └ ┘└_{ }┘**endproc**! init e, E, and arrays ind, changed

E := I; state := n

**for**k := 1**to**n**do**ind_{k}:= maxind(k); e_{k}:= S_{kk}; changed_{k}:= true**endfor****while**state≠0**do**! next rotationm := 1 ! find index (k,l) of pivot p

**for**k := 2**to**n−1**do****if**│S_{k ind}_{k}│ > │S_{m ind}_{m}│**then**m := k**endif****endfor**k := m; l := ind

_{m}; p := S_{kl}! calculate c = cos φ, s = sin φ

y := (e

_{l}−e_{k})/2; t := │y│+√(p^{2}+y^{2})s := √(p

^{2}+t^{2}); c := t/s; s := p/s; t := p^{2}/t**if**y<0**then**s := −s; t := −t**endif**S

_{kl}:= 0.0; update(k,−t); update(l,t)! rotate rows and columns k and l

**for**i := 1**to**k−1**do**rotate(i,k,i,l)**endfor****for**i := k+1**to**l−1**do**rotate(k,i,i,l)**endfor****for**i := l+1**to**n**do**rotate(k,i,l,i)**endfor**! rotate eigenvectors

**for**i := 1**to**n**do**┌

_{ }┐ ┌ ┐┌_{ }┐│E

_{ki}│ │c −s││E_{ki}││

_{ }│ := │ ││_{ }││E

_{li}│ │s c││E_{li}│└

_{ }┘ └ ┘└_{ }┘**endfor**! rows k, l have changed, update rows ind

_{k}, ind_{l}ind

_{k}:= maxind(k); ind_{l}:= maxind(l)**loop****endproc**### Example

LetThen jacobi produces the following eigenvalues and eigenvectors after 3 sweeps (19 iterations) :

these link can explain the example

http://www.scribd.com/doc/24953275/Jacobi-Transformation-Used-for-Eigenproblems-for-Symmetric-Matrix

## Applications for real symmetric matrices

When the eigenvalues (and eigenvectors) of a symmetric matrix are known, the followingvalues are easily calculated.

Singular values

- The singular values of a (square) matrix A are the square roots of the (non-negative) eigenvalues of . In case of a symmetric matrix S we have of , hence the singular values of S are the absolute values of the eigenvalues of S

2-norm and spectral radius

- The 2-norm of a matrix A is the norm based on the Euclidean vectornorm, i.e. the largest value when x runs through all vectors with . It is the largest singular value of A. In case of a symmetric matrix it is largest absolute value of its eigenvectors and thus equal to its spectral radius.

Condition number

- The condition number of a nonsingular matrix A is defined as . In case of a symmetric matrix it is the absolute value of the quotient of the largest and smallest eigenvalue. Matrices with large condition numbers can cause numerically unstable results: small perturbation can result in large errors. Hilbert matrices are the most famous ill-conditioned matrices. For example, the fourth-order Hilbert matrix has a condition of 15514, while for order 8 it is 2.7 × 10
^{8}.

Rank

- A matrix A has rank r if it has r columns that are linearly independent while the remaining columns are linearly dependent on these. Equivalently, r is the dimension of the range of A. Furthermore it is the number of nonzero singular values.
- In case of a symmetric matrix r is the number of nonzero eigenvalues. Unfortunately because of rounding errors numerical approximations of zero eigenvalues may not be zero (it may also happen that a numerical approximation is zero while the true value is not). Thus one can only calculate the numerical rank by making a decision which of the eigenvalues are close enough to zero.

Pseudo-inverse

- The pseudo inverse of a matrix A is the unique matrix for which AX and XA are symmetric and for which AXA = A, XAX = X holds. If A is nonsingular, then '.
- When procedure jacobi (S, e, E) is called, then the relation holds where Diag(e) denotes the diagonal matrix with vector e on the diagonal. Let denote the vector where is replaced by if and by 0 if is (numerically close to) zero. Since matrix E is orthogonal, it follows that the pseudo-inverse of S is given by .

Least squares solution

- If matrix A does not have full rank, there may not be a solution of the linear system Ax = b. However one can look for a vector x for which is minimal. The solution is . In case of a symmetric matrix S as before, one has .

Matrix exponential

- From one finds where exp e is the vector where is replaced by . In the same way, f(S) can be calculated in an obvious way for any (analytic) function f.

Linear differential equations

- The differential equation x' = Ax, x(0) = a has the solution x(t) = exp(t A) a. For a symmetric matrix S, it follows that . If is the expansion of a by the eigenvectors of S, then .
- Let be the vector space spanned by the eigenvectors of S which correspond to a negative eigenvalue and analogously for the positive eigenvalues. If then i.e. the equilibrium point 0 is attractive to x(t). If then , i.e. 0 is repulsive to x(t). and are called stable and unstable manifolds for S. If a has components in both manifolds, then one component is attracted and one component is repelled. Hence x(t) approaches as .

## Generalizations

The Jacobi Method has been generalized to complex Hermitian matricesJacobi method for complex Hermitian matrices

In mathematics, the Jacobi method for complex Hermitian matrices is a generalization of the Jacobi iteration method. The Jacobi iteration method is also explained in "Introduction to Linear Algebra" by .- Derivation :...

, general nonsymmetric real and complex matrices as well as block matrices.

Since singular values of a real matrix are the square roots of the eigenvalues of the symmetric matrix it can also be used for the calculation of these values. For this case, the method is modified in such a way that S must not be explicitly calculated which reduces the danger of round-off error

Round-off error

A round-off error, also called rounding error, is the difference between the calculated approximation of a number and its exact mathematical value. Numerical analysis specifically tries to estimate this error when using approximation equations and/or algorithms, especially when using finitely many...

s. Note that with .

The Jacobi Method is also well suited for parallelism.