Orthogonal Matrices: Definition & Examples

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SUMMARY

An orthogonal matrix is defined as an NxN matrix with rank N, where each row and column is a unit vector and orthogonal to all other rows and columns. Due to limitations in precision arithmetic, determining if a matrix is orthogonal requires approximate calculations. Implementing a custom function is necessary, utilizing Matlab's rank function to verify rank and applying tolerances for unit vector magnitude and orthogonality of rows. This approach ensures practical verification of orthogonality in real-world applications.

PREREQUISITES
  • Understanding of orthogonal matrices and their properties
  • Familiarity with Matlab programming and its rank function
  • Knowledge of vector norms and inner products
  • Basic concepts of numerical precision and tolerances in computations
NEXT STEPS
  • Explore Matlab's rank function and its applications in matrix analysis
  • Research methods for implementing tolerance checks in numerical computations
  • Learn about the properties of unit vectors and their significance in linear algebra
  • Investigate alternative programming languages for matrix operations, such as Python with NumPy
USEFUL FOR

Mathematicians, data scientists, engineers, and anyone involved in linear algebra and numerical analysis who needs to verify matrix orthogonality in practical applications.

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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.
 

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