Orthogonal Matrices: Definition & Examples

In summary, to determine if a matrix is an orthogonal matrix, you cannot use infinite precision arithmetic. Instead, you can create your own function by considering the rank, unit vectors, and orthogonality of the matrix. Matlab's rank function can be used for the first item, but the other two may require the use of tolerances to account for approximate values.
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Unless you are using infinite precision arithmetic, you cannot tell whether a matrix truly is or is an orthogonal matrix. You can't use infinite precision arithmetic, so your answer will necessarily be approximate.

What you can do is roll your own function, using the fact that an orthogonal NxN matrix has rank N, that each row (or column) is a unit vector, and that each row is orthogonal to all other rows (or columns). Matlab has the rank function to help with the first item. This one is fairly cut and dry. An NxN matrix that has rank less than N cannot be an orthogonal matrix.

The other two items are going to be problematic. You'll need some tolerances such that a row whose magnitude is not quite 1 but is within some ε of 1 is still deemed to be a unit vector, and such that a pair of rows whose inner product is not quite zero but is within some other ε of 0 are still deemed to be orthogonal to one another.
 

Related to Orthogonal Matrices: Definition & Examples

1. What is an orthogonal matrix?

An orthogonal matrix is a square matrix whose rows and columns are orthogonal unit vectors. This means that the dot product of any two rows or columns is equal to 0, and the magnitude of each row or column is equal to 1.

2. How is an orthogonal matrix different from a regular matrix?

An orthogonal matrix differs from a regular matrix in that its rows and columns are not only perpendicular to each other, but also have a magnitude of 1. This property makes orthogonal matrices useful for transformations, as they preserve the length and angle between vectors.

3. What are some examples of orthogonal matrices?

Some examples of orthogonal matrices include rotation matrices, reflection matrices, and permutation matrices. A rotation matrix, for example, is an orthogonal matrix that represents a rotation in 2D or 3D space.

4. What is the inverse of an orthogonal matrix?

The inverse of an orthogonal matrix is equal to its transpose. This means that if we multiply an orthogonal matrix by its transpose, we will get the identity matrix. In other words, an orthogonal matrix is its own inverse.

5. How are orthogonal matrices used in real-world applications?

Orthogonal matrices are commonly used in computer graphics, robotics, and physics to represent and manipulate rotations, transformations, and reflections. They are also used in statistics and data analysis, as they can be used to perform orthogonal transformations on data sets.

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