What is the Orthogonal Decomposition of x from b in terms of RowA and NulA?

In summary, A has a rowspace of {( 1, -2, -1, 2), (0, 1, -1, 0)} and a nullspace of {(3, 1, 1, 0), (-2, 0, 0, 1)}. For part b), the parametric vector form of x is (8, -1, 0, 0) + x3 (3, 1, 1, 0) + x4 (-2, 0, 0, 1). For part c), the orthogonal decomposition of x is x = r + n, where r ∈ RowA and n ∈ NulA. For part d),
  • #1
413
41
0
Let A=
[ 1 -2 -1 2]
[-1 0 3 -2 ]
[ 3 -4 -5 6]

(sorry, can't line up the columns)

I think I've done a) and b) correctly, I don't really understand c), d) and e)


a) Find RowA and Nul A

RowA={( 1, -2, -1, 2), (0, 1, -1, 0)}
Nul A= {(3, 1, 1, 0), (-2, 0, 0, 1)}

b) If b=(10 -8 28), find parametric vector form of x, the general vector in the set S={ x | Ax=b }


x= (8, -1, 0, 0) + x3 (3, 1, 1, 0) +x4 (-2, 0, 0, 1)


c) We know that (RowA) perp = Nul A. Find the orthogonal decompositon of x, the vector from b), as a sum of two vectors r ∈ RowA and n ∈ NulA.

d) Use c) to find the set S ∩ Row A, the set of elements that are in both S and RowA.

e) Suppose that A is an m x n matrix. Prove if the set S={ x | Ax=b } is non empty, then the set S ∩ RowA consists of a single vector.

Thanks.
 
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  • #2
nevermind, i figured it out.
 
  • #3
The rowspace of A has two vectors?
 

Related to What is the Orthogonal Decomposition of x from b in terms of RowA and NulA?

What is "Orthogonal decomposition"?

Orthogonal decomposition is a mathematical technique used to break down a vector or function into a sum of orthogonal components. In other words, it allows us to express a complex object as a combination of simpler, more easily understood parts.

How is "Orthogonal decomposition" used in science?

Orthogonal decomposition is a useful tool in many scientific disciplines, including physics, engineering, and data analysis. It can be used to simplify complex systems and understand their underlying structures, as well as to identify and remove noise or unwanted components from data sets.

What is the difference between orthogonal decomposition and other decomposition methods?

Unlike other decomposition methods, such as principal component analysis or Fourier analysis, orthogonal decomposition guarantees that the resulting components are independent of each other. This makes it particularly useful for analyzing systems with multiple interacting components.

What are the benefits of using "Orthogonal decomposition"?

One of the main benefits of using orthogonal decomposition is that it allows us to simplify complex systems and identify their essential components. This can help us gain a deeper understanding of the system and make predictions about its behavior. Additionally, orthogonal decomposition can help to reduce the dimensionality of data, making it easier to analyze and visualize.

Are there any limitations to "Orthogonal decomposition"?

One limitation of orthogonal decomposition is that it assumes the components are orthogonal, or perpendicular, to each other. This may not always be the case in real-world systems, leading to errors in the decomposition. Additionally, orthogonal decomposition can be computationally intensive for large data sets, making it less practical in some situations.

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