Linear Map T: Proving Existence of KerT=U if dimU≥dimV-dimW

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In summary, if V and W are finite dimensional and U is a subspace of V, and dimU ≥ dimV - dimW, then there exists a linear map T from V to W such that kerT = U. This can be proven by considering the linear map T |u> = |u> for elements of V that do not belong to U and T |u> = |0> for elements of U. However, this map is not valid as it is from V to V, not to W, and R3 - R2 does not give R3. Instead, the correct answer is ℝ3 - ℝ2 = {(0,0,z): z is real}, which is a line and not a subs
  • #1
kostas230
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Suppose that V and W are finite dimensional and that U is a subspace of V. If dimU≥dimV-dimW prove that there exists a linear map T from V to W such that kerT=U.

My answer is this:

Consider the following linear map:
T|u>=|u> if |u> belongs to V-U and T|u>=|0> if |u> belongs to U​

Therefore kerT=U
Is this correct?
 
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  • #2
hi kostas230! :smile:
kostas230 said:
Consider the following linear map:
T|u>=|u> if |u> belongs to V-U and T|u>=|0> if |u> belongs to U​

Therefore kerT=U
Is this correct?

nooo …

i] that's a map from V to V, not to W
ii] what do you mean by V-U ? :confused: (eg what is R3 - R2 ?)
 
  • #3
tiny-tim said:
i] that's a map from V to V, not to W

Silly me, I overlooked it xD

ii] what do you mean by V-U ? :confused: (eg what is R3 - R2 ?)

I mean the elements of V that do not belong in U.
 
  • #4
kostas230 said:
I mean the elements of V that do not belong in U.

then R3 - R2 would include all elements with z ≠ 0 :confused:
 
  • #6
kostas230 said:
Well, the correct answer would be: ℝ^3-ℝ^2={(0,0,z):z is real

that's a line

add a line to R2 and you don't get R3 :redface:
 
  • #7
R2 direct sum a line gives R3 but that is not at all what you said!R3- R2 is all (x, y, z) such that [itex]z\ne 0[/itex] which is not a subspace.
I think you need to review your definitions!
 

1. What is a linear map?

A linear map, also known as a linear transformation, is a function that maps one vector space to another in a way that preserves the operations of addition and scalar multiplication. In simpler terms, it is a mathematical operation that takes in one vector and produces another vector.

2. What are the properties of a linear map?

There are two main properties of a linear map: additivity and homogeneity. Additivity means that the map preserves the operation of vector addition, while homogeneity means that the map preserves the operation of scalar multiplication. In other words, the linear map of the sum of two vectors is equal to the sum of the linear maps of each vector separately, and the linear map of a scalar multiple of a vector is equal to the scalar multiple of the linear map of the original vector.

3. How is a linear map represented?

A linear map can be represented in various ways, depending on the context. In general, it can be represented as a matrix, a set of equations, or a function. In a matrix representation, the linear map is written as a matrix that operates on a vector to produce another vector. In an equation representation, the linear map is written as a set of equations that describe the transformation of the vector coordinates. In a function representation, the linear map is written as a function that takes in a vector and outputs another vector.

4. What is the difference between a linear map and a nonlinear map?

A linear map preserves the operations of addition and scalar multiplication, while a nonlinear map does not. This means that a nonlinear map may change the shape or direction of a vector, while a linear map only stretches or rotates the vector. Additionally, a linear map can be represented as a matrix, while a nonlinear map cannot.

5. What are the applications of linear maps?

Linear maps have many applications in mathematics, physics, and engineering. They are used to model physical systems, such as electrical circuits and mechanical systems, and to solve problems in linear algebra, calculus, and differential equations. They are also used in computer graphics and data analysis to transform and manipulate data. In general, linear maps are useful whenever there is a need to map one set of data onto another set in a way that preserves certain properties.

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