Gram Schmidt Method (calculating orthogonal vector set)

In summary, the conversation discusses the Gram Schmidt method for creating an orthogonal vector set. The method involves constructing the second vector, v2, as a linear combination of u2 and v1, satisfying the condition that v2.v1=0. The conversation also explores the possibility of using the equation v2=v1+c1u2 instead of v2=u2+c1v1, and the importance of properly determining the constant c1. It is concluded that using the first constant instead of the second may result in incorrect answers due to the different methods of constructing an orthogonal vector.
  • #1
mess1n
24
0
Hey, I'm going over the Gram Schmidt method, and need some help understanding it. I understand that you're intending to create an orthogonal vector (i'll call them v) set based on a set of vectors you already have (i'll call them u). Then:

Let v1 = u1

Now, construct the second orthogonal vector v2 using a linear combination of u2 and v1 i.e:

v2 = u2 + c1v1

where the condition [ v2.v1 = 0 ] is satisfied.

However, what I wonder is why at this point I can't use the following equation instead:

v2 = v1 + c1u2

Surely I'm still creating a linear combination for v2 which will still satisfy the condition? However whenever I use this second form, my answer comes out wrong! Why is this?

Andrew
 
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  • #2
Excuse me if I'm wrong, but I don't think the only condition for forming v_2 is that v_2 is a linear combination of u_2 and v_1. I believe v_2 needs to be a vector formed by subtraction from u_2 its projection onto the subspace. If my reasoning is correct I think that doing this is motivated from the orthogonal decomposition theorem.
 
  • #3
Field Duck is essentially correct.

Just using c1 disguises the true nature of your constant. How do you determine c1?
 
Last edited:
  • #4
^obviously
c1=-(v1.u2) /(v1.v1)

For the original question one can infact use
v2 = a1v1 + b1u2 often
 
  • #5
Thanks for the responses. So I can use the second form of the equation? How comes I get wrong answers when I use it! Is it just a coincidence and I'm making a mistake with the numbers? It makes sense that the vectors would be different... but when I check if they're all orthogonal afterwards it turns out that they aren't. This is the problem when I use the second form of the equation.
 
  • #6
Perhaps if you answered the question about c1 someone may be able to help?

You have two constants c1 which must be different.

How did you obtain each?
 
  • #7
Studiot:

I only have one constant c1.

It is obtained as lurflurf explained.

Perhaps you're misunderstanding my question. I'm wondering why I can't use:

"v2 = v1 + c1u2"

INSTEAD OF:

"v2 = u2 + c1v1"
 
  • #8
How have you only got one constant?

Compare

[tex]\begin{array}{l}
{v_2} = {v_1} + {c_1}{u_2} \\
{v_2}.{v_1} = \left( {{v_1} + {c_1}{u_2}} \right).{v_1} \\
0 = {v_1}.{v_1} + {c_1}{u_2}.{v_1} \\
{c_1} = - \frac{{{v_1}.{v_1}}}{{{u_2}.{v_1}}} \\
\end{array}[/tex]

With

[tex]\begin{array}{l}
{v_2} = {u_2} + {c_1}{v_1} \\
{v_2}.{v_1} = \left( {{u_2} + {c_1}{v_1}} \right).{v_1} \\
0 = {u_2}.{v_1} + {c_1}{v_1}.{v_1} \\
{c_1} = - \frac{{{u_2}.{v_1}}}{{{v_1}.{v_1}}} \\
\end{array}[/tex]
 
  • #9
Studiot said:
How have you only got one constant?

Compare

[tex]\begin{array}{l}
{v_2} = {v_1} + {c_1}{u_2} \\
{v_2}.{v_1} = \left( {{v_1} + {c_1}{u_2}} \right).{v_1} \\
0 = {v_1}.{v_1} + {c_1}{u_2}.{v_1} \\
{c_1} = - \frac{{{v_1}.{v_1}}}{{{u_2}.{v_1}}} \\
\end{array}[/tex]

With

[tex]\begin{array}{l}
{v_2} = {u_2} + {c_1}{v_1} \\
{v_2}.{v_1} = \left( {{u_2} + {c_1}{v_1}} \right).{v_1} \\
0 = {u_2}.{v_1} + {c_1}{v_1}.{v_1} \\
{c_1} = - \frac{{{u_2}.{v_1}}}{{{v_1}.{v_1}}} \\
\end{array}[/tex]

So why can't I use the first constant instead of the second? Surely I've still constructed an orthogonal vector using the first method?

Cheers,
Andrew
 

What is the Gram Schmidt method?

The Gram Schmidt method is a mathematical algorithm used to find an orthogonal set of vectors given a set of linearly independent vectors. It is often used in linear algebra and other fields of mathematics.

What is the purpose of using the Gram Schmidt method?

The Gram Schmidt method is used to find an orthogonal set of vectors, which can be useful in solving systems of linear equations, computing orthogonal projections, and performing other calculations in mathematics, physics, and engineering.

How does the Gram Schmidt method work?

The Gram Schmidt method works by taking a set of linearly independent vectors and transforming them into an orthogonal set by subtracting the projections of each vector onto the previously calculated vectors. This process is repeated for each vector in the set.

What are the steps involved in using the Gram Schmidt method?

The steps involved in using the Gram Schmidt method are:

  1. Start with a set of linearly independent vectors
  2. Calculate the first vector of the orthogonal set by normalizing the first vector in the original set
  3. Calculate the remaining vectors of the orthogonal set by subtracting the projections of the original vectors onto the previously calculated orthogonal vectors
  4. Normalize each new vector in the orthogonal set
  5. Repeat until all vectors in the original set have been transformed into an orthogonal set

What are some applications of the Gram Schmidt method?

The Gram Schmidt method has many applications, including solving systems of linear equations, computing orthogonal projections, finding least squares solutions, and performing other calculations in mathematics, physics, and engineering. It is also used in data analysis, signal processing, and other fields.

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