I gram-schmidtt ortogonalization method false?.

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In summary, the Gram-Schmidt method allows for the construction of an orthonormal basis from a general basis of functions. However, when applied to the set of functions {x**n+y**n, with n=1,2,3...}, it is not possible to construct an orthonormal series. This is because the set only spans a certain subspace and the Gram-Schmidt process will only result in an orthonormal basis for that subspace.
  • #1
eljose79
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In fact is suposed that with gthe G-s method from any series of function you can construct and ortonormal methods..but what would happen when applied to the set of functions {x**n+y**n, with n=1,2,3...}?..in fact with this set you can not construct an orthonormal series.
 
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Originally posted by eljose79
In fact is suposed that with gthe G-s method from any series of function you can construct and ortonormal methods..but what would happen when applied to the set of functions {x**n+y**n, with n=1,2,3...}?..in fact with this set you can not construct an orthonormal series.

this is a strange question, eljose. I think you know the answer
so tell me if what I say is right:

the G-S process allows one to construct an ON basis from a general basis.

If one starts with {1, x2,...,x2n,...}

this will result in an ON basis not of ALL the functions, say on the unit interval, but only a basis of the EVEN functions
f(-x) = f(x)
and the even functions are a vectorspace, a subspace of the whole, and you will get an ON basis of that subspace.

You always get an ON basis of the vectorspace which is SPANNED by the original set.


Likewise your set only spans a certain subspace to start with.
So GS will give you an ON basis of that subspace.
 
  • #3
answer?..

In fact i do not know if you take the set of functions (x**n+y**n with n=1,2,3...}..would you have an ortonormal set of functions in a given interval?.such us (-11)..well then try to expand the xy function in this set...¡it is imposbile¡..
 
  • #4
Did you not understand what Marcus wrote? If you start with any set of functions and apply the Gram-Schmidt orthogonalization process, you will wind up with an orthonormal set of functions that spans the SAME SUBSPACE of all functions as the original set spanned.
You cannot expand "xy" with the orthonormal set because you could not do it with the original set.
 

1. What is the Gram-Schmidt orthogonalization method?

The Gram-Schmidt orthogonalization method is a mathematical technique used to find an orthogonal (perpendicular) basis for a vector space. It is commonly used in linear algebra to transform a set of linearly dependent vectors into a set of linearly independent orthogonal vectors.

2. How does the Gram-Schmidt orthogonalization method work?

The Gram-Schmidt orthogonalization method works by taking a set of linearly dependent vectors and transforming them into a set of orthogonal vectors. This is done by projecting each vector onto the orthogonal complement of the previously generated vectors. The resulting vectors are then normalized to have a unit length.

3. Is the Gram-Schmidt orthogonalization method always accurate?

No, the Gram-Schmidt orthogonalization method is not always accurate. It can produce numerical errors when working with vectors that are very close to being linearly dependent, or when working with large matrices that can cause round-off errors.

4. What is the purpose of using the Gram-Schmidt orthogonalization method?

The primary purpose of using the Gram-Schmidt orthogonalization method is to transform a set of linearly dependent vectors into a set of linearly independent orthogonal vectors. This can be useful in various mathematical and scientific applications, such as solving systems of linear equations or performing matrix operations.

5. Is the statement "Gram-Schmidt orthogonalization method is false" true?

No, it is not true. The Gram-Schmidt orthogonalization method is a well-established mathematical technique that has been used for decades in various fields of science and engineering. It is a valid and useful method for finding orthogonal bases and is not false.

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