Downloaded 19 times
![SPECIAL SQUARE MATRICES
Symmetric Matrices
A Matrix (A, for example) is Symmetrical if the following holds true:
AT = A
In other words, the Transpose of A is identical to the Matrix A, itself.
This can also be written as:
For all [i,j]: aij = aji
A Matrix is a Skew-Symmetric Matrix if the following holds true:
AT = -A
For all [i,j]: aij = -aji
Thus, it is clear to see that, in a Skew-Symmetric Matrix, all diagonal
entries (where, in aij, i = j) must have the value of Zero, as it is implied
that:
axx = -axx
axx = 0
An example of a Skew-Symmetric Matrix
Orthogonal Matrices
To begin, we first re-establish the definition of Orthogonal.
“Orthogonal”: Two Vectors, u and v, are said to be Orthogonal if their
Dot, or Inner, Product is Zero.
A Matrix (A, for example) is Orthogonal if the following holds true:
AT = A-1
In other words, the Transpose of A is identical to the Inverse of A.
A different way of describing the situation is:
An example of a 3x3 Orthogonal Matrix process, thus implying that A is Orthogonal.
Via Matrix Multiplication, we can see that:
For I11:
Yet I12:
Thus, it is observed that every row of the Matrix A is Orthogonal with
every other row of A (but not with itself). Furthermore, each row can
also be seen as Unit Vectors, and thus described as u1, u2, u3.
Generalising this case, we can say that, if {u1, u2, …, un} are all Unit
Vectors and Orthogonal to each other, {u1, u2, …, un} in Rn form an
Orthonormal Set. This can be displayed as:
Where is the Kronecker Delta Function, i.e. the function that
returns 1 if all its arguments are equal, and 0 if they are not.
Normal Matrices
A Matrix is said to be Normal if it commutes with its transpose. In
other words, the following must be true:
Thus, the above Symmetric, Skew-Symmetric and Orthogonal
Matrices are all Normal.](https://image.slidesharecdn.com/specialsquarematrices-111228172847-phpapp02/85/Special-Square-Matrices-1-320.jpg)
Uploaded byChristopher Gratton
Special Square Matrices
1) A symmetric matrix is a matrix whose transpose is equal to itself. A skew-symmetric matrix is a matrix whose transpose is equal to its negative. 2) An orthogonal matrix is a matrix whose transpose is equal to its inverse. The rows of an orthogonal matrix are orthogonal unit vectors. 3) A normal matrix is a matrix that commutes with its transpose, meaning the matrix multiplied by its transpose is equal to the transpose multiplied by the matrix. Symmetric, skew-symmetric, and orthogonal matrices are all examples of normal matrices.
More Related Content
![Determining the Dependent and Independent Variables [Autosaved].pptx](https://cdn.slidesharecdn.com/ss_thumbnails/determiningthedependentandindependentvariablesautosaved-221125062001-e11bf218-thumbnail.jpg?width=640&height=640&fit=bounds)
Similar to Special Square Matrices
Special Square Matrices
- 1. SPECIAL SQUARE MATRICES SymmetricMatrices A Matrix (A, for example) is Symmetrical if the following holds true: AT = A In other words, the Transpose of A is identical to the Matrix A, itself. This can also be written as: For all [i,j]: aij = aji A Matrix is a Skew-Symmetric Matrix if the following holds true: AT = -A For all [i,j]: aij = -aji Thus, it is clear to see that, in a Skew-Symmetric Matrix, all diagonal entries (where, in aij, i = j) must have the value of Zero, as it is implied that: axx = -axx axx = 0 An example of a Skew-Symmetric Matrix Orthogonal Matrices To begin, we first re-establish the definition of Orthogonal. “Orthogonal”: Two Vectors, u and v, are said to be Orthogonal if their Dot, or Inner, Product is Zero. A Matrix (A, for example) is Orthogonal if the following holds true: AT = A-1 In other words, the Transpose of A is identical to the Inverse of A. A different way of describing the situation is: An example of a 3x3 Orthogonal Matrix process, thus implying that A is Orthogonal. Via Matrix Multiplication, we can see that: For I11: Yet I12: Thus, it is observed that every row of the Matrix A is Orthogonal with every other row of A (but not with itself). Furthermore, each row can also be seen as Unit Vectors, and thus described as u1, u2, u3. Generalising this case, we can say that, if {u1, u2, …, un} are all Unit Vectors and Orthogonal to each other, {u1, u2, …, un} in Rn form an Orthonormal Set. This can be displayed as: Where is the Kronecker Delta Function, i.e. the function that returns 1 if all its arguments are equal, and 0 if they are not. Normal Matrices A Matrix is said to be Normal if it commutes with its transpose. In other words, the following must be true: Thus, the above Symmetric, Skew-Symmetric and Orthogonal Matrices are all Normal.