Understanding the solution to this subspace problem in linear algebra

In summary, the solution to the subspace problem in linear algebra involves identifying and characterizing subspaces within vector spaces. This includes understanding their properties, such as closure under addition and scalar multiplication, and determining bases and dimensions. Techniques such as the rank-nullity theorem and the use of linear transformations are crucial for analyzing relationships between different subspaces. Overall, mastering these concepts is essential for solving various linear algebraic problems and applications.
  • #1
MaxJ
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Homework Statement
Below
Relevant Equations
Below
For this problem,
1723493656163.png

The solution for (a) is
1723493686845.png

I am slightly confused for ##p \in W## since I get ##a_3 = 2a_1## and ##a_2 = 2a_0##. Since ##a_3 = 2b##, ##a_2 = 2a##, ##a_1 = b##, ##a_0 = a##.

Anybody have this doubt too?

Kind wishes
 
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  • #2
I agree with your conclusion and assume that whoever wrote what you attached made a typo. Further evidence is that the author also wrote "Thus p(z) ..." when clearly p(x) was intended.
 
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  • #3
MaxJ said:
Homework Statement: Below
Relevant Equations: Below

For this problem,
View attachment 349805
The solution for (a) is
View attachment 349806
I am slightly confused for ##p \in W## since I get ##a_3 = 2a_1## and ##a_2 = 2a_0##. Since ##a_3 = 2b##, ##a_2 = 2a##, ##a_1 = b##, ##a_0 = a##.

Anybody have this doubt too?

Kind wishes
I got the same result as claimed:
\begin{align*}
a+ax+bx^2+bx^3&=\lambda c+\lambda dx+2\lambda cx^2+2\lambda dx^3\\
c&=\lambda^{-1}a\\
d&=\lambda^{-1}a=c\\
a+ax+bx^2+bx^3&=a+ax+2ax^2+2ax^3\\
b&=2a \\[6pt]
U\cap W&=\{a(1+x+2x^2+2x^3)\,|\,a\in \mathbb{F})\}
\end{align*}
but I get confused with the ##a_i## in the book.
 
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FAQ: Understanding the solution to this subspace problem in linear algebra

What is a subspace in linear algebra?

A subspace is a set of vectors that satisfies three main properties: it contains the zero vector, it is closed under vector addition, and it is closed under scalar multiplication. This means that if you take any two vectors in the subspace and add them together, the result is also in the subspace. Similarly, if you multiply any vector in the subspace by a scalar, the result remains in the subspace.

How do you determine if a set of vectors forms a subspace?

To determine if a set of vectors forms a subspace, you need to check the three properties mentioned earlier. First, verify that the zero vector is included in the set. Next, take any two vectors from the set and check if their sum is also in the set. Finally, take any vector from the set and multiply it by any scalar to see if the result is still in the set. If all three conditions are satisfied, the set is a subspace.

What is the significance of the dimension of a subspace?

The dimension of a subspace is the number of vectors in a basis for that subspace, which represents the maximum number of linearly independent vectors that can span the subspace. The dimension provides insight into the "size" of the subspace and helps in understanding its geometric properties. For example, a subspace of dimension 0 is just the zero vector, dimension 1 is a line through the origin, dimension 2 is a plane, and so on.

What is the relationship between subspaces and linear transformations?

Linear transformations often map vectors from one vector space to another, and the images of these transformations can form subspaces. If you have a linear transformation, the kernel (null space) and the image (range) of the transformation are both subspaces of the respective vector spaces. This relationship is critical in understanding how transformations affect the structure of vector spaces.

How can the concept of basis help in solving subspace problems?

The concept of a basis is fundamental in solving subspace problems because it provides a way to represent all vectors in the subspace as linear combinations of the basis vectors. By identifying a basis, you can simplify the problem of determining properties like dimension and span. Additionally, knowing the basis allows for easier computation of projections, intersections, and other operations involving subspaces.

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