Orthonormal procces question

  • Thread starter transgalactic
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In summary, the conversation discusses the process of orthogonalization and checking for independence and orthonormality of a set of vectors. It is suggested to normalize each vector as it is calculated and to check for the correct dot product values to ensure accuracy. The formula for orthogonalization is also mentioned.
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  • #2
is that correct??
 
  • #3
I'm not going to go through all of that calculation! You should be able to check for your self if they are independent (if so then since there are three vectors, yes, they form a basis for the subspace) and if they are orthonormal.

I will make two comments. You do not "divide by the normal". You "normalize" a vector by dividing by its length. And it is generally simpler to normalize each vector (divide by its length) as you calculate each vector, not at the end.
 
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  • #4
so first i divide each vector by its length
and then i do the orthogonalization process
did i wrote the orthogonalization formula correctly
??
 
Last edited:
  • #5
transgalactic said:
so first i divide each vector by its length
and then i do the orthogonalization process
did i wrote the orthogonalization formula correctly
??

After you have found a vector perpendicular to all the previous vectors, divide by its length. That will simplify the rest of the calculation.

Again, check it yourself. Do all the vectors have length 1 (is the dot product of any vector with itself equal to 1)? Are all the vector orthogonal to all the others (is the dot product of two different vectors 0)?
 
  • #6
ok after,
whats the right formula for the orthogonolazation process

did i used the correct formula??
did i put the vectors in the right place?
 

1. What is an orthonormal process?

An orthonormal process is a mathematical concept used to describe a sequence of random variables that are both orthogonal and normalized. This means that the variables are uncorrelated and have a magnitude of one. Orthonormal processes are commonly used in signal processing and time series analysis.

2. How is an orthonormal process different from a normal process?

A normal process is a sequence of random variables that are uncorrelated but may not be normalized. In contrast, an orthonormal process has the added condition of being both orthogonal and normalized. This means that the variables in an orthonormal process are not only uncorrelated, but they also have a magnitude of one, making them easier to work with mathematically.

3. What are some applications of orthonormal processes?

Orthonormal processes have a wide range of applications, such as in image and signal processing, data compression, and time series analysis. They are also commonly used in mathematical models for physical systems, such as in quantum mechanics and statistical mechanics.

4. How do you determine if a process is orthonormal?

To determine if a process is orthonormal, you need to check if the sequence of random variables is both orthogonal and normalized. This can be done by calculating the inner product between each pair of variables and checking if it equals zero for orthogonality, and if the magnitude of each variable is equal to one for normalization.

5. What are the advantages of using an orthonormal process?

There are several advantages to using an orthonormal process. First, it simplifies mathematical calculations and makes them easier to interpret. Additionally, an orthonormal process can help reduce the dimensionality of data, leading to more efficient storage and analysis. It also has applications in noise reduction and data compression, making it a useful tool in various scientific fields.

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