Triangular matrix: Definition from Answers.com

In the mathematical discipline of linear algebra, a triangular matrix is a special kind of square matrix where the entries either below or above the main diagonal are zero. Because matrix equations with triangular matrices are easier to solve they are very important in numerical analysis. The LU decomposition gives an algorithm to decompose any invertible matrix A into two triangular factors: a normed lower triangle matrix L and an upper triangle matrix U.

1 Description
2 Special forms
3 Special properties
4 Generalizations
5 Examples
6 Forward and Back Substitution
7 See also
8 References

Description

A matrix of the form

$\mathbf{L}= \begin{bmatrix} l_{1,1} & & & & 0 \\ l_{2,1} & l_{2,2} & & & \\ l_{3,1} & l_{3,2} & \ddots & & \\ \vdots & \vdots & \ddots & \ddots & \\ l_{n,1} & l_{n,2} & \ldots & l_{n,n-1} & l_{n,n} \end{bmatrix}$

is called lower triangular matrix or left triangular matrix, and analogously a matrix of the form

$\mathbf{U} = \begin{bmatrix} u_{1,1} & u_{1,2} & u_{1,3} & \ldots & u_{1,n} \\ & u_{2,2} & u_{2,3} & \ldots & u_{2,n} \\ & & \ddots & \ddots & \vdots \\ & & & \ddots & u_{n-1,n}\\ 0 & & & & u_{n,n} \end{bmatrix}$

is called upper triangular matrix or right triangular matrix.

The standard operations on triangular matrices conveniently preserve the triangular form: the sum and product of two upper triangular matrices is again upper triangular. The inverse of an upper triangular matrix is also upper triangular, and of course we can multiply an upper triangular matrix by a constant and it will still be upper triangular. This means that the upper triangular matrices form a subalgebra of the ring of square matrices for any given size. The analogous result holds for lower triangular matrices. Note, however, that the product of a lower triangular with an upper triangular matrix does not preserve triangularity.

Special forms

Strictly triangular matrix

A triangular matrix with zero entries on the main diagonal is strictly upper or lower triangular. All strictly triangular matrices are nilpotent.

Unitriangular matrix

If the entries on the main diagonal are 1, the matrix is called (upper or lower) unitriangular. All unitriangular matrices are unipotent. Other names used for these matrices are unit (upper or lower) triangular (of which "unitriangular" might be a contraction), or normed upper/lower triangular (very rarely used). However a unit triangular matrix is not the same as the unit matrix, and a normed triangular matrix has nothing to do with the notion of matrix norm.

Gauss matrix

A Gauss matrix is a special form of a unitriangular matrix, where all of the off-diagonal entries are zero, except for the entries in one column. Such a matrix is also called atomic upper/lower triangular or Gauss transformation matrix. So an atomic lower triangular matrix is of the form

$\mathbf{L}_{i} = \begin{bmatrix} 1 & & & & & & & 0 \\ 0 & \ddots & & & & & & \\ 0 & \ddots & 1 & & & & & \\ 0 & \ddots & 0 & 1 & & & & \\ & & 0 & l_{i+1,i} & 1 & & & \\ \vdots & & 0 & l_{i+2,i} & 0 & \ddots & & \\ & & \vdots & \vdots & \vdots & \ddots & 1 & \\ 0 & \dots & 0 & l_{n,i} & 0 & \dots & 0 & 1 \\ \end{bmatrix}.$

The inverse of an atomic triangular matrix is again atomic triangular. Indeed, we have

$\mathbf{L}_{i}^{-1} = \begin{bmatrix} 1 & & & & & & & 0 \\ 0 & \ddots & & & & & & \\ 0 & \ddots & 1 & & & & & \\ 0 & \ddots & 0 & 1 & & & & \\ & & 0 & -l_{i+1,i} & 1 & & & \\ \vdots & & 0 & -l_{i+2,i} & 0 & \ddots & & \\ & & \vdots & \vdots & \vdots & \ddots & 1 & \\ 0 & \dots & 0 & -l_{n,i} & 0 & \dots & 0 & 1 \\ \end{bmatrix},$

i.e., the the single column of off-diagonal entries are replaced in the inverse matrix by their additive inverses.

Special properties

A matrix which is simultaneously upper and lower triangular is diagonal. The identity matrix is the only matrix which is both upper and lower unitriangular.

A matrix which is simultaneously triangular and normal, is also diagonal. This can be seen by looking at the diagonal entries of A^*A and AA^*, where A is a normal, triangular matrix.

The transpose of an upper triangular matrix is a lower triangular matrix and vice versa.

The determinant of a triangular matrix equals the product of the diagonal entries. Since for any triangular matrix A the matrix $x I - A$ , whose determinant is the characteristic polynomial of A, is also triangular, the diagonal entries of A in fact give the multiset of eigenvalues of A (an eigenvalue with multiplicity m occurs exactly m times as diagonal entry).^[1]

Any complex square matrix is similar to a triangular matrix.^[1] In fact, a matrix A over a field containing all of the eigenvalues of A (for example, any matrix over an algebraically closed field) is similar to a triangular matrix. A more precise statement is given by the Jordan normal form theorem, which states that in this situation, A is similar to an upper triangular matrix of a very particular form. The simpler triangularization result is often sufficient however, and in any case used in proving the Jordan normal form theorem.^[1]^[2]

In the case of complex matrices, it is possible to say more about triangularization, namely, that any square matrix A has a Schur decomposition. This means that A is unitarily equivalent (i.e. similar, using a unitary matrix as change of basis) to an upper triangular matrix.

The variable L is commonly used for lower triangular matrix, standing for lower/left, while the variable U or R is commonly used for upper triangular matrix, standing for upper/right.

Many operations can be performed more easily on triangular matrices than on general matrices. Notably matrix inversion (when possible) can be done by back substitution, avoiding the complications of general Gaussian elimination.

The inverse of a triangular matrix is also triangular.

The product of two lower triangular matrices produces a lower triangular matrix.

The product of two upper triangular matrices produces an upper triangular matrix.

Generalizations

Because the product of two upper triangular matrices is again upper triangular, the set of upper triangular matrices forms an algebra. Algebras of upper triangular matrices have a natural generalization in functional analysis which yields nest algebras on Hilbert spaces.

The set of invertible triangular matrices of a given kind (upper or lower) forms a group, which is a subgroup of the general linear group of all invertible matrices. The group of 2 by 2 upper unitriangular matrices is isomorphic to the additive group of the field of scalars; in the case of complex numbers it corresponds to a group formed of parabolic Möbius transformations; the 3 by 3 upper unitriangular matrices form the Heisenberg group.

The upper triangular matrices are precisely those that stabilize the standard flag. The invertible ones among them form a subgroup of the general linear group, whose conjugate subgroups are those defined as the stabilizer of some (other) complete flag. These subgroups are Borel subgroups. The group of invertible lower triangular matrices is such a subgroup, since it is the stabilizer of the standard flag associated to the standard basis in reverse order.

The stabilizer of a partial flag obtained by forgetting some parts of the standard flag can be described as a set of block upper triangular matrices (but its elements are not all triangular matrices). The conjugates of such a group are the subgroups defined as the stabilizer of some partial flag. These subgroups are called parabolic subgroups.

A non-square (or sometimes any) matrix with zeros above (below) the diagonal is called a lower (upper) trapezoidal matrix. The non-zero entries form the shape of a trapezoid.

Examples

The matrix

$\begin{bmatrix} 1 & 4 & 2 \\ 0 & 3 & 4 \\ 0 & 0 & 1 \\ \end{bmatrix}$

is upper triangular and

$\begin{bmatrix} 1 & 0 & 0 \\ 2 & 8 & 0 \\ 4 & 9 & 7 \\ \end{bmatrix}$

is lower triangular.

The matrix

$\begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 4 & 1 & 0 \\ 0 & 2 & 0 & 1 \\ \end{bmatrix}$

is atomic lower triangular. Its inverse is

$\begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & -4 & 1 & 0 \\ 0 & -2 & 0 & 1 \\ \end{bmatrix}.$

Forward and Back Substitution

A matrix equation in the form $\mathbf{L}\mathbf{x} = \mathbf{b}$ or $\mathbf{U} \mathbf{x} = \mathbf{b}$ is very easy to solve^[3] by an iterative process called forward substitution for lower triangular matrices and analogously back substitution for upper triangular matrices. The process is so called because for lower triangular matrices, one first computes $x 1$ , then substitutes that forward into the next equation to solve for $x 2$ , and repeats through to $x n$ . In an upper triangular matrix, one works backwards, first computing $x n$ , then substituting that back into the previous equation to solve for $x n - 1$ , and repeating through $x 1$ .

Notice that this does not require inverting the matrix.

Forward substitution

The matrix equation Lx = b can be written as a system of linear equations

$\begin{matrix} l_{1,1} x_1 & & & & & = & b_1 \\ l_{2,1} x_1 & + & l_{2,2} x_2 & & & = & b_2 \\ \vdots & & \vdots & \ddots & & & \vdots \\ l_{m,1} x_1 & + & l_{m,2} x_2 & + \dotsb + & l_{m,m} x_m & = & b_m \\ \end{matrix}$

Observe that the first equation ( $l 1,1 x 1 = b 1$ ) only involves $x 1$ , and thus one can solve for $x 1$ directly. The second equation only involves $x 1$ and $x 2$ , and thus can be solved once one substitutes in the already solved value for $x 1$ . Continuing in this way, the $k$ -th equation only involves $x_1,\dots,x_k$ , and one can solve for $x k$ using the previously solved values for $x_1,\dots,x_{k-1}$ .

The resulting formulas are:

$x_1 = \frac{b_1}{l_{1,1}},$

$x_2 = \frac{b_2 - l_{2,1} x_1}{l_{2,2}},$

$\vdots$

$x_m = \frac{b_m - \sum_{i=1}^{m-1} l_{m,i}x_i}{l_{m,m}}.$

A matrix equation with an upper triangular matrix U can be solved in an analogous way, only working backwards.

Algorithm

The following is an example implementation of this algorithm in the C# programming language. Note that the algorithm performs poorly in C# due to the inefficient handling of non-jagged matrices in this language. None the less, the method of forward and backward substitution can be highly efficient.

 double[] luEvaluate(double[,] L, double[,] U, Vector b)
  {
  // Ax = b -> LUx = b. Then y is defined to be Ux
  int i = 0;
  int j = 0;
  int n = b.Count;
  double[] x = new double[n];
  double[] y = new double[n];
  // Forward solve Ly = b
  for (i = 0; i < n; i++)
  {
    y[i] = b[i];
    for (j = 0; j < i; j++)
    {
      y[i] -= L[i, j] * y[j];
    }
    y[i] /= L[i, i];
  }
  // Backward solve Ux = y
  for (i = n - 1; i >= 0; i--)
  {
    x[i] = y[i];
    for (j = i + 1; j < n; j++)
    {
      x[i] -= U[i, j] * x[j];
    }
    x[i] /= U[i, i];
  }
  return x;
 }

Applications

Forward substitution is used in financial bootstrapping to construct a yield curve.

References

^ ^a ^b ^c Sheldon Axler, Linear Algebra Done Right, Springer-Verlag, 1996, ISBN 0-387-98258-2, pp. 86–87, 169.
^ I. N. Herstein, Topics in Algebra (2nd edition), John Wiley and Sons, 1975, ISBN 0-471-01090-1, pp. 285–290.
^ Assuming that L (respectively, U) is invertible (has non-zero diagonal entries), otherwise it cannot be solved in general).

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