What Are the Properties of Linear Transformations and Subspaces?

In summary, linear algebra is a branch of mathematics that deals with linear equations and their representations in vector spaces. It has various applications in fields like engineering, physics, and machine learning, and involves basic concepts such as vectors, matrices, and linear transformations. To solve linear algebra problems, methods like Gaussian elimination, matrix inversion, and eigenvalue decomposition are used. To prepare for an exam, it is important to review basic concepts, practice solving problems, and seek out additional resources for supplemental learning.
  • #1
Ted123
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Homework Statement



Suppose [itex]\phi : V \to W[/itex] is a linear transformation over a field [itex]\mathbb{K}[/itex] .

(a) (i) Define the kernel of [itex]\phi, \; Ker(\phi)[/itex]. [2 marks]

(ii) Show that [itex]\phi[/itex] is 1-1 if and only if [itex]Ker ( \phi )=\{\bf{0}\}[/itex]. [4 marks]

(b) Suppose X is a subspace of W. Define the set [itex]U_X[/itex] by

[itex]U_X = \{ v\in V : \phi (v) \in X \}[/itex] .

Show that [itex]U_X[/itex] is a subspace containing [itex]Ker ( \phi )[/itex]. [7 marks]

(c) Suppose that Y is another subspace of W. Show that [itex]U_X \cap U_Y = U_{X \cap Y}[/itex]. [4 marks]

(d) What does it mean to say that W is the direct sum of two subspaces X and Y (written [itex]W=X \oplus Y[/itex] )? [2 marks]

(e) Show that if [itex]W=X \oplus Y[/itex] then [itex]U_X \cap U_Y = Ker(\phi)[/itex]. [6 marks]

Total [25 marks]

The Attempt at a Solution



Done (a)

For (b), [itex]\phi(\bf{0})=\bf{0} \in X[/itex] since X is a subspace, so [itex]\bf{0}\in U_X[/itex] and [itex]U_X[/itex] is non-empty.

Suppose [itex]u,v\in U_X[/itex] .

Then [itex]\phi(u+v) = \phi(u) + \phi(v) \in X[/itex] since X is a subspace, so [itex]U_X[/itex] is closed under vector addition.

Suppose [itex]\alpha \in \mathbb{K}[/itex] and [itex]v\in U_X[/itex].

Then [itex]\phi(\alpha v) = \alpha \phi(v) \in X[/itex] since X is a subspace.

Hence [itex]U_X[/itex] is a subspace containing [itex]\bf{0}[/itex] and hence [itex]Ker( \phi )[/itex] .

For (c) is this alright?

[itex]U_X \cap U_Y = \{ v\in V : \phi(v) \in X \} \cap \{ v\in V : \phi(v) \in Y \}[/itex]

[itex]= \{ v\in V : \phi(v) \in X\;\text{and}\;\phi(v)\in Y \}[/itex]

[itex]= \{ v\in V : \phi(v) \in X \cap Y \} = U_{X\cap Y}[/itex]

For (d), the direct sum [itex]W=X \oplus Y[/itex] means [itex]W=X+Y[/itex] and [itex]X\cap Y = \{ \bf {0}\}[/itex]

For (e) is this alright?

If [itex]W=X \oplus Y[/itex] then [itex]X\cap Y = \{ \bf {0}\}[/itex] . So

[itex]U_X \cap U_Y = U_{X \cap Y}[/itex] (by part (c))

[itex]= U_{\{\bf{0}\}} = \{v\in V : \phi(v)\in \{\bf{0}\} \} = \{v\in V : \phi(v) = \bf{0} \} = Ker( \phi )[/itex]
 
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  • #2
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Your solution looks good overall. Here are a few minor suggestions:

- In part (a), you could also mention that \phi is 1-1 if and only if \text{Ker}(\phi)=\{\bf{0}\} because this means that the only element in the kernel is the zero vector, which implies that \phi is injective.
- In part (c), you could add a little more explanation as to why U_X \cap U_Y = \{ v\in V : \phi(v) \in X \} \cap \{ v\in V : \phi(v) \in Y \} = \{ v\in V : \phi(v) \in X\;\text{and}\;\phi(v)\in Y \}.
- In part (d), you could clarify that X+Y is the sum of the subspaces X and Y, and X\cap Y = \{\bf{0}\} means that the only element in common between X and Y is the zero vector.
- In part (e), you could mention that U_X \cap U_Y = U_{X \cap Y} because X\cap Y = \{\bf{0}\} implies that any vector that satisfies both \phi(v)\in X and \phi(v)\in Y must also satisfy \phi(v)\in \{\bf{0}\}.
 

FAQ: What Are the Properties of Linear Transformations and Subspaces?

1. What is linear algebra?

Linear algebra is a branch of mathematics that deals with the study of linear equations and their representations in vector spaces. It involves the use of algebraic techniques to solve problems related to systems of linear equations, matrices, and vectors.

2. What are the applications of linear algebra?

Linear algebra has various applications in fields such as engineering, physics, economics, computer graphics, and machine learning. It is used to solve problems in optimization, data analysis, and image processing. It is also essential in the development of machine learning algorithms and computer simulations.

3. What are the basic concepts in linear algebra?

The basic concepts in linear algebra include vectors, matrices, vector spaces, linear transformations, and eigenvalues and eigenvectors. Vectors are quantities that have both magnitude and direction, while matrices are arrays of numbers or symbols arranged in rows and columns. Vector spaces are sets of vectors that satisfy certain properties, and linear transformations are functions that map one vector space to another. Eigenvalues and eigenvectors are used to describe the behavior of linear transformations.

4. What are the methods used to solve linear algebra problems?

There are several methods used to solve linear algebra problems, including Gaussian elimination, matrix inversion, and eigenvalue decomposition. Gaussian elimination is a systematic process of reducing a system of linear equations to a simpler form, while matrix inversion involves finding the inverse of a given matrix. Eigenvalue decomposition is used to find the eigenvalues and eigenvectors of a given matrix.

5. How can I prepare for a linear algebra exam?

To prepare for a linear algebra exam, it is essential to review the basic concepts and principles of linear algebra, including vector operations, matrix operations, and solving systems of linear equations. Practice solving different types of problems and work on sample exams to familiarize yourself with the format and types of questions that may be asked. It is also helpful to seek out additional resources, such as textbooks, online tutorials, and study groups, to supplement your learning.

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