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If \(T: V \rightarrow W\) is a linear transformation, show that \(T\left(\mathbf{v}-\mathbf{v}_{1}\right)=T(\mathbf{v})-T\left(\mathbf{v}_{1}\right)\) for all \(\mathbf{v}\) and \(\mathbf{v}_{1}\) in \(V\).

Short Answer

Expert verified
The linear transformation \(T\) distributes over subtraction: \(T(\mathbf{v} - \mathbf{v}_{1}) = T(\mathbf{v}) - T(\mathbf{v}_{1})\).

Step by step solution

01

Understanding the Problem

We need to show that the linear transformation \(T\) distributes over subtraction for any vectors \(\mathbf{v}\) and \(\mathbf{v}_{1}\) in the vector space \(V\). This means demonstrating that the transformation of a difference is the difference of the transformations.
02

Applying the Property of a Linear Transformation

By definition, a linear transformation \(T: V \rightarrow W\) satisfies two properties. The first is additivity, meaning \(T(\mathbf{u} + \mathbf{v}) = T(\mathbf{u}) + T(\mathbf{v})\) for any vectors \(\mathbf{u}, \mathbf{v} \in V\). The second is homogeneity of degree 1, meaning \(T(c\mathbf{v}) = cT(\mathbf{v})\) for any scalar \(c\).
03

Applying the Additivity Property

To apply the additivity property to the given problem, first consider the subtraction \(\mathbf{v} - \mathbf{v}_{1}\) as the addition \(\mathbf{v} + (-\mathbf{v}_{1})\). By linearity, we have:\[ T(\mathbf{v} - \mathbf{v}_{1}) = T(\mathbf{v} + (-\mathbf{v}_{1})) = T(\mathbf{v}) + T(-\mathbf{v}_{1}). \]
04

Applying the Homogeneity Property

Using the homogeneity property, since \(-\mathbf{v}_{1}\) is the same as \((-1)\mathbf{v}_{1}\), we have:\[ T(-\mathbf{v}_{1}) = T((-1)\mathbf{v}_{1}) = (-1)T(\mathbf{v}_{1}) = -T(\mathbf{v}_{1}). \]
05

Combining Results

Substituting the result of Step 4 back into the result obtained in Step 3, we get:\[ T(\mathbf{v} - \mathbf{v}_{1}) = T(\mathbf{v}) + (-T(\mathbf{v}_{1})) = T(\mathbf{v}) - T(\mathbf{v}_{1}). \]Thus, we have shown that \(T\) distributes over vector subtraction.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Vector Addition
In the context of linear transformations, vector addition is a crucial concept. When we speak of vector addition within a vector space, we are referring to adding two vectors together to produce a third vector. This is similar to adding numbers, but with vectors having direction and magnitude, the addition must respect these properties.

The principle of vector addition is supported by the geometric rule often called the 'tip-to-tail' method. You place the tail of the second vector at the tip of the first. The vector sum, or resultant vector, is then the vector from the tail of the first to the tip of the second.
  • For linear transformations, vector addition is captured by the additivity property. This means that applying transformation to a sum of vectors equals summing the transformations of each vector individually.
  • In formula terms: if you have vectors \( \mathbf{u} \) and \( \mathbf{v} \), the property \( T(\mathbf{u} + \mathbf{v}) = T(\mathbf{u}) + T(\mathbf{v}) \) holds.
  • This fundamental property ensures that linear transformations distribute over sums, which is pivotal to understanding how vectors relate and transform.
Homogeneity
Homogeneity in linear transformations refers to how the transformation scales with the vector. Specifically, if you scale a vector by a constant factor, the transformation of that vector scales by the same factor. This is known as homogeneity of degree 1, an important feature of linear functions.

This property can be visualized by imagining that if you double a vector, the result of the transformation is also doubled, or if you reverse a vector, the transformation is also negated, maintaining a consistent direction and inverse proportion.
  • Mathematically, it means for any scalar \( c \) and vector \( \mathbf{v} \), the equation \( T(c\mathbf{v}) = cT(\mathbf{v}) \) holds true.
  • It's pivotal in ensuring that transformations maintain proportional relationships between scalar multiplications of vectors.
  • This property ensures transformations accurately reflect scaling operations within the vector space, which is necessary for predictability and consistency in transformations.
Vector Subtraction
Vector subtraction might seem less straightforward than addition, but it plays an equally vital role in linear transformations. To subtract vectors, we essentially add the opposite of one vector to another. In terms of a linear transformation, it's crucial that a function respects vector subtraction to maintain linearity.

This is achieved by expressing subtraction as vector addition with a negative vector. For example, \( \mathbf{v} - \mathbf{v}_1 \) can be rephrased as \( \mathbf{v} + (-\mathbf{v}_1) \). This allows us to use the properties of addition and homogeneity together to deduce behaviors with subtraction.
  • In linear transformations, we confirm that \( T(\mathbf{v} - \mathbf{v}_1) = T(\mathbf{v}) - T(\mathbf{v}_1) \), maintaining consistency in transformations over vector subtraction.
  • This property is derived from applying both additivity and homogeneity properties, ensuring resultant transformations are accurate to operations carried out in the vector space.
  • It underscores the fact that in a linear transformation, the change (or difference) between vectors is directly transformed by \( T \), ensuring both elements of addition and scaling are proportionally reflected in subtraction too.

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Most popular questions from this chapter

Let \(\left\\{\mathbf{e}_{1}, \ldots, \mathbf{e}_{n}\right\\}\) be a basis of \(\mathbb{R}^{n}\). Given \(k, 1 \leq k \leq n,\) define \(P_{k}: \mathbb{R}^{n} \rightarrow \mathbb{R}^{n}\) by \(P_{k}\left(r_{1} \mathbf{e}_{1}+\cdots+r_{n} \mathbf{e}_{n}\right)=r_{k} \mathbf{e}_{k} .\) Show that \(P_{k}\) a linear trans- formation for each \(k\).

Define \(T: \mathbf{P}_{n} \rightarrow \mathbb{R}\) by \(T[p(x)]=\) the sum of all the coefficients of \(p(x)\). a. Use the dimension theorem to show that \(\operatorname{dim}(\operatorname{ker} T)=n\) b. Conclude that \(\left\\{x-1, x^{2}-1, \ldots, x^{n}-1\right\\}\) is a basis of \(\operatorname{ker} T\)

Let \(S\) and \(T\) be linear transformations \(V \rightarrow W,\) where \(\operatorname{dim} V=n\) and \(\operatorname{dim} W=m\) a. Show that \(\operatorname{ker} S=\operatorname{ker} T\) if and only if \(T=R S\) for some isomorphism \(R: W \rightarrow W\). [Hint: Let \(\left\\{\mathbf{e}_{1}, \ldots, \mathbf{e}_{r}, \ldots, \mathbf{e}_{n}\right\\}\) be a basis of \(V\) such that \(\left\\{\mathbf{e}_{r+1}, \ldots, \mathbf{e}_{n}\right\\}\) is a basis of \(\operatorname{ker} S=\operatorname{ker} T\). Use Theorem 7.2 .5 to extend \(\left\\{S\left(\mathbf{e}_{1}\right), \ldots, S\left(\mathbf{e}_{r}\right)\right\\}\) and \(\left\\{T\left(\mathbf{e}_{1}\right), \ldots, T\left(\mathbf{e}_{r}\right)\right\\}\) to bases of \(\left.W .\right]\) b. Show that \(\operatorname{im} S=\operatorname{im} T\) if and only if \(T=S R\) for some isomorphism \(R: V \rightarrow V\). [Hint: Show that \(\operatorname{dim}(\operatorname{ker} S)=\operatorname{dim}(\) ker \(T)\) and choose bases \(\left\\{\mathbf{e}_{1}, \ldots, \mathbf{e}_{r}, \ldots, \mathbf{e}_{n}\right\\}\) and \(\left\\{\mathbf{f}_{1}, \ldots, \mathbf{f}_{r}, \ldots, \mathbf{f}_{n}\right\\}\) of \(V\) where \(\left\\{\mathbf{e}_{r+1}, \ldots, \mathbf{e}_{n}\right\\}\) and \(\left\\{\mathbf{f}_{r+1}, \ldots, \mathbf{f}_{n}\right\\}\) are bases of ker \(S\) and ker \(T,\) respectively. If \(1 \leq i \leq r,\) show that \(S\left(\mathbf{e}_{i}\right)=T\left(\mathbf{g}_{i}\right)\) for some \(\mathbf{g}_{i}\) in \(V,\) and prove that \(\left\\{\mathbf{g}_{1}, \ldots, \mathbf{g}_{r}, \mathbf{f}_{r+1}, \ldots, \mathbf{f}_{n}\right\\}\) is a basis of \(\left.V .\right]\)

Let \(V \stackrel{T}{\rightarrow} U \stackrel{S}{\rightarrow} W\) be linear transformations. a. If \(S T\) is one-to-one, show that \(T\) is one-to-one and that \(\operatorname{dim} V \leq \operatorname{dim} U\) b. If \(S T\) is onto, show that \(S\) is onto and that \(\operatorname{dim} W \leq \operatorname{dim} U\)

Given \(\left\\{\mathbf{v}_{1}, \ldots, \mathbf{v}_{n}\right\\}\) in a vector space \(V\) define \(T: \mathbb{R}^{n} \rightarrow V\) by \(T\left(r_{1}, \ldots, r_{n}\right)=r_{1} \mathbf{v}_{1}+\cdots+r_{n} \mathbf{v}_{n}\). Show that \(T\) is linear, and that: a. \(T\) is one-to-one if and only if \(\left\\{\mathbf{v}_{1}, \ldots, \mathbf{v}_{n}\right\\}\) is independent. b. \(T\) is onto if and only if \(V=\operatorname{span}\left\\{\mathbf{v}_{1}, \ldots, \mathbf{v}_{n}\right\\}\).

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