Orthogonal Transformation and condition

In summary, in order to prove the orthogonal condition, you must use the invariance of the length of a vector and the linear transformation to show that aijaik is equal to δjk. Simply writing aij2=xj2xk2 does not demonstrate the required equality for the orthogonal condition.
  • #1
eoghan
210
7
Hi there!
In order to proof the orthogonal condition aijaik=[tex]\delta_{jk}[/tex] j,k=1,2,3
I write the invariance of the length of a vector in two coordinate systems:
x'ix'i=xixi
Using the linear transformation:
x'i=ai1xi1+ai2xi2+ai3xi3
the first term becomes:
aijaikxjxk

My question is: why can't I write
aij2=xj2
 
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  • #2
xk2to prove the orthogonal condition?The answer is that you cannot write aij2=xj2xk2 to prove the orthogonal condition because this equation does not necessarily demonstrate the equality of the two sides of the equation. The orthogonal condition requires that aijaik is equal to the Kronecker delta, δjk. To demonstrate the orthogonal condition, you must use the linear transformation and the invariance of the length of a vector to show that aijaik is equal to δjk.
 

FAQ: Orthogonal Transformation and condition

What is an orthogonal transformation?

An orthogonal transformation is a type of linear transformation in which the angle between any two vectors is preserved. This means that the length of the vectors may change, but their orientation with respect to each other remains the same.

What is the significance of orthogonal transformations?

Orthogonal transformations are important in mathematics and science because they preserve the geometric properties of objects. This makes them useful in areas such as linear algebra, geometry, and physics.

How do you determine if a matrix represents an orthogonal transformation?

A matrix represents an orthogonal transformation if its columns (or rows) form an orthonormal basis. This means that the vectors are all perpendicular to each other and have a magnitude of 1.

What is the condition number of an orthogonal transformation?

The condition number of an orthogonal transformation is a measure of how sensitive the transformation is to changes in the input. A low condition number indicates that the transformation is stable and small changes in the input will not greatly affect the output.

How are orthogonal transformations used in data analysis?

In data analysis, orthogonal transformations are used to simplify and reduce the dimensionality of data. This makes it easier to interpret and analyze the data, and can also help with data visualization and machine learning tasks.

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