/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 10 Prove Theorem 10.8: In Theorem 1... [FREE SOLUTION] | 91影视

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Prove Theorem 10.8: In Theorem 10.7 (Problem 10.9 ), if \(f(t)\) is the minimal polynomial of \(T\) (and \(g(t)\) and \(h(t)\) are monic), then \(g(t)\) is the minimal polynomial of the restriction \(T_{1}\) of \(T\) to \(U\) and \(h(t)\) is the minimal polynomial of the restriction \(T_{2}\) of \(T\) to \(W\) .Let \(m_{1}(t)\) and \(m_{2}(t)\) be the minimal polynomials of \(T_{1}\) and \(T_{2}\), respectively. Note that \(g\left(T_{1}\right)=0\) and \(h\left(T_{2}\right)=0\) because \(U=\operatorname{Ker} g(T)\) and \(W=\operatorname{Ker} h(T) .\) Thus \(m_{1}(t)\) divides \(g(t) \quad\) and \(\quad m_{2}(t)\) divides \(h(t)\) By Problem \(10.9, f(t)\) is the least common multiple of \(m_{1}(t)\) and \(m_{2}(t) .\) But \(m_{1}(t)\) and \(m_{2}(t)\) are relatively prime because \(g(t)\) and \(h(t)\) are relatively prime. Accordingly, \(f(t)=m_{1}(t) m_{2}(t) .\) We also have that \(f(t)=g(t) h(t) .\) These two equations together with (1) and the fact that all the polynomials are monic imply that \(g(t)=m_{1}(t)\) and \(h(t)=m_{2}(t),\) as required.

Short Answer

Expert verified
Using the given information, we find that m鈧(t) divides g(t) and m鈧(t) divides h(t). Since f(t) is the least common multiple of m鈧(t) and m鈧(t) and f(t) = g(t)h(t), we have f(t) = m鈧(t)m鈧(t). Since all polynomials are monic and their products are equal, we can conclude that g(t) = m鈧(t) and h(t) = m鈧(t). Therefore, g(t) is the minimal polynomial of \(T_1\) and h(t) is the minimal polynomial of \(T_2\), as required.

Step by step solution

01

Use the given information

We have U = Ker g(T) and W = Ker h(T), so g(T鈧) = 0 and h(T鈧) = 0.
02

Determine the relationships between the minimal polynomials

From the given information, we know that: $$ m_{1}(t) \text { divides } g(t) \quad \text { and } \quad m_{2}(t) \text { divides } h(t) $$
03

Find the least common multiple of the minimal polynomials

Using Problem 10.9, we find that f(t) is the least common multiple of m鈧(t) and m鈧(t).
04

Determine if m鈧(t) and m鈧(t) are relatively prime

We also know that m鈧(t) and m鈧(t) are relatively prime because g(t) and h(t) are relatively prime.
05

Set f(t) equal to the product of the minimal polynomials and compare to the given product

Now, from Steps 3 and 4, we have f(t) = m鈧(t)m鈧(t), and it is given that f(t) = g(t)h(t).
06

Conclude that g(t) and h(t) are the minimal polynomials of T鈧 and T鈧

Since all polynomials are monic and m鈧(t) divides g(t), m鈧(t) divides h(t), and their products are equal, we can conclude that g(t) = m鈧(t) and h(t) = m鈧(t). So, g(t) is the minimal polynomial of T鈧 and h(t) is the minimal polynomial of T鈧, as required.

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Key Concepts

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

Linear Transformations
A linear transformation is a fundamental concept in linear algebra. It is a function that maps vectors from one vector space to another while preserving the operations of vector addition and scalar multiplication. This means that if you have two vectors, say \( \mathbf{u} \) and \( \mathbf{v} \), and a scalar \( c \), a linear transformation \( T \) satisfies:
  • \( T(\mathbf{u} + \mathbf{v}) = T(\mathbf{u}) + T(\mathbf{v}) \)
  • \( T(c \mathbf{u}) = cT(\mathbf{u}) \)
Linear transformations are represented by matrices when the vector spaces are finite-dimensional. The matrix provides a convenient way to compute the transformation of any vector in the space. In the context of the problem, \( T \) is a linear transformation whose minimal polynomial we investigate. Understanding these transformations is essential as they form the basis of many operations in linear algebra, such as rotations, translations, and more.
Kernels of Linear Operators
The kernel, or null space, of a linear operator is a set of all vectors that are mapped to the zero vector by this operator. For a linear transformation \( T: V \to W \), the kernel is defined as: \[\text{Ker}(T) = \{ \mathbf{v} \in V \mid T(\mathbf{v}) = \mathbf{0} \}\]It is a crucial concept because it provides insights into the properties of a linear transformation, such as:
  • Rank: The dimension of the kernel helps determine the rank of the transformation.
  • Solvability: Knowing the kernel can show whether a linear transformation (or a system of equations) is solvable.
In the exercise, the kernels \( \text{Ker}(g(T)) \) and \( \text{Ker}(h(T)) \) are used to define subspaces. These kernels help in identifying the minimal polynomials of the restricted transformations \( T_1 \) and \( T_2 \).
Least Common Multiple
The least common multiple (LCM) of two polynomials is the smallest degree polynomial that both original polynomials divide. It works similarly to the LCM of numbers. When two minimal polynomials are given, their LCM helps find a polynomial that still annihilates both linear transformations. To find the LCM, you must identify the highest powers of each factor present in the factorizations of the original polynomials and use them.
In the problem, \( f(t) \), the minimal polynomial of \( T \), is said to be the least common multiple of \( m_1(t) \) and \( m_2(t) \), which indicates that \( f(t) \) can capture the behaviors described by both \( T_1 \) and \( T_2 \). This conceptual link is critical in proving the properties of linear transformations and their minimal polynomials.
Relatively Prime Polynomials
Polynomials are relatively prime if they have no common roots, similar to how two numbers are relatively prime if they share no divisors other than 1. This means that their greatest common divisor (GCD) is 1.
  • For example, \( t^2 + 1 \) and \( t - 1 \) are relatively prime as they do not share any roots.
In this context, being relatively prime is significant because it allows separate treatment of different parts of a linear transformation. If the minimal polynomials \( g(t) \) and \( h(t) \) are relatively prime, then their influence does not overlap when considered in terms of \( T_1 \) and \( T_2 \).
This relationship is essential in the solution as it simplifies the structure of \( f(t) \), the minimal polynomial for \( T \), allowing it to be represented as the product \( m_1(t) m_2(t) \), which, because of their relative primality, reflects their independent minimal annihilators.

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Most popular questions from this chapter

Prove Theorem 10.9: A linear operator \(T: V \rightarrow V\) has a diagonal matrix representation if and only if its minimal polynomal \(m(t)\) is a product of distinct linear polynomials. Suppose \(m(t)\) is a product of distinct linear polynomials, say, \\[ m(t)=\left(t-\lambda_{1}\right)\left(t-\lambda_{2}\right) \cdots\left(t-\lambda_{r}\right) \\] where the \(\lambda_{i}\) are distinct scalars. By the Primary Decomposition Theorem, \(V\) is the direct sum of subspaces \(W_{1}, \ldots, W_{r},\) where \(W_{i}=\operatorname{Ker}\left(T-\lambda_{i} I\right) .\) Thus, if \(v \in W_{i},\) then \(\left(T-\lambda_{i} I\right)(v)=0\) or \(T(v)=\lambda_{i} v .\) In other words, every vector in \(W_{i}\) is an eigenvector belonging to the eigenvalue \(\lambda_{i}\). By Theorem 10.4 , the union of bases for \(W_{1}, \ldots, W_{r}\) is a basis of \(V\). This basis consists of eigenvectors, and so \(T\) is diagonalizable. Conversely, suppose \(T\) is diagonalizable (i.e., \(V\) has a basis consisting of eigenvectors of \(T\) ). Let \(\lambda_{1}, \ldots, \lambda_{s}\) be the distinct eigenvalues of \(T .\) Then the operator \\[ f(T)=\left(T-\lambda_{1} I\right)\left(T-\lambda_{2} I\right) \cdots\left(T-\lambda_{s} I\right) \\] maps each basis vector into \(0 .\) Thus, \(f(T)=0,\) and hence, the minimal polynomial \(m(t)\) of \(T\) divides the polynomial \\[ f(t)=\left(t-\lambda_{1}\right)\left(t-\lambda_{2}\right) \cdots\left(t-\lambda_{s} I\right) \\] Accordingly, \(m(t)\) is a product of distinct linear polynomials.

Let \(C(f(t))\) denote the companion matrix to an arbitrary polynomial \(f(t) .\) Show that \(f(t)\) is the characteristic polynomial of \(C(f(t))\).

Prove Lemma 10.13: Let \(T: V \rightarrow V\) be a linear operator whose minimal polynomial is \(f(t)^{n}\) where \(f(t)\) is a monic irreducible polynomial. Then \(V\) is the direct sum of \(T\) -cyclic subspaces \(Z_{i}=Z\left(v_{i}, T\right), i=1, \ldots, r,\) with corresponding \(T\) -annihilators \\[ f(t)^{n_{1}}, f(t)^{n_{2}}, \ldots, f(t)^{n_{r}}, \quad n=n_{1} \geq n_{2} \geq \cdots \geq n_{r} \\] Any other decomposition of \(V\) into the direct sum of \(T\) -cyclic subspaces has the same number of components and the same set of \(T\) -annihilators. The proof is by induction on the dimension of \(V\). If \(\operatorname{dim} V=1\), then \(V\) is \(T\) -cyclic and the lemma holds. Now suppose dim \(V>1\) and that the lemma holds for those vector spaces of dimension less than that of \(V\) Because the minimal polynomial of \(T\) is \(f(t)^{n}\), there exists \(v_{1} \in V\) such that \(f(T)^{n-1}\left(v_{1}\right) \neq 0 ;\) hence, the \(T\) -annihilator of \(v_{1}\) is \(f(t)^{n}\). Let \(Z_{1}=Z\left(v_{1}, T\right)\) and recall that \(Z_{1}\) is \(T\) -invariant. Let \(V=V / Z_{1}\) and let \(T\) be the linear operator on \(\bar{V}\) induced by \(T\). By Theorem 10.16 , the minimal polynomial of \(\bar{T}\) divides \(f(t)^{n}\); hence, the hypothesis holds for \(\bar{V}\) and \(\bar{T}\). Consequently, by induction, \(\bar{V}\) is the direct sum of \(\bar{T}\) -cyclic subspaces; say, \\[ \bar{V}=Z\left(\bar{v}_{2}, \bar{T}\right) \oplus \cdots \oplus Z\left(\bar{v}_{r}, \bar{T}\right) \\] where the corresponding \(\bar{T}\) -annihilators are \(f(t)^{\text {th }} \sqrt[7]{2}, \ldots, f(t)^{n_{7}}, n \geq n_{2} \geq \cdots \geq n_{r_{i}}\) We claim that there is a vector \(v_{2}\) in the coset \(\bar{v}_{2}\) whose \(T\) -annihilator is \(\bar{f}(t)^{m_{2}},\) the \(\bar{T}\) -annihilator of \(\bar{v}_{2}\). Let \(w\) be any vector in \(\bar{v}_{2}\). Then \(f(T)^{n_{2}}(w) \in Z_{1}\). Hence, there exists a polynomial \(g(t)\) for which \\[ f(T)^{n_{2}}(w)=g(T)\left(v_{1}\right) \\] Because \(f(t)^{n}\) is the minimal polynomial of \(T,\) we have, by (1) \\[ 0=f(T)^{n}(w)=f(T)^{n-n_{2}} g(T)\left(v_{1}\right) \\] But \(f(t)^{n}\) is the \(T\) -annihilator of \(v_{1} ;\) hence, \(f(t)^{n}\) divides \(f(t)^{n-n_{2}} g(t),\) and \(\operatorname{so~} g(t)=f(t)^{n} h(t)\) for some polynomial \(h(t)\). We set \\[ v_{2}=w-h(T)\left(v_{1}\right) \\] Because \(w-v_{2}=h(T)\left(v_{1}\right) \in Z_{1}, v_{2}\) also belongs to the coset \(\bar{v}_{2}\). Thus, the \(T\) -annihilator of \(v_{2}\) is a multiple of the \(T\) -annihilator of \(\bar{v}_{2}\). On the other hand, by (1) \\[ f(T)^{n_{2}}\left(v_{2}\right)=f(T)^{n_{*}}\left(w-h(T)\left(v_{1}\right)\right)=f(T)^{n_{2}}(w)-g(T)\left(v_{1}\right)=0 \\] Consequently, the \(T\) -annihilator of \(v_{2}\) is \(f(t)^{n_{2}}\), as claimed. Similarly, there exist vectors \(v_{3}, \ldots, v_{r} \in V\) such that \(v_{i} \in \bar{v}_{i}\) and that the \(T\) -annihilator of \(v_{i}\) is \(f(t)^{n_{i}}\) the \(\vec{T}\) -annihilator of \(\bar{v}_{i} .\) We set \\[ Z_{2}=Z\left(v_{2}, T\right), \quad \ldots, \quad Z_{r}=Z\left(v_{r}, T\right) \\] Let \(d\) denote the degree of \(f(t),\) so that \(f(t)^{n_{i}}\) has degree \(d n_{l} .\) Then, because \(f(t)^{n_{i}}\) is both the \(T\) -annihilator of \(v_{i}\) and the \(\bar{T}\) -annihilator of \(\bar{v}_{i},\) we know that \\[ \left\\{v_{i}, T\left(v_{i}\right), \ldots, T^{\ln _{i}-1}\left(v_{i}\right)\right\\} \quad \text { and } \quad\left\\{\vec{v}_{i} \cdot \vec{T}\left(\bar{v}_{i}\right), \ldots, \bar{T}^{\operatorname{dr}_{i}-1}\left(\bar{v}_{i}\right)\right\\} \\] are bases for \(Z\left(v_{i}, T\right)\) and \(Z(\overline{v_{i}}, \bar{T}),\) respectively, for \(i=2, \ldots, r .\) But \(\bar{V}=Z(\overline{v_{2}}, T) \oplus \cdots \oplus Z(\overline{v_{r}}, T)\) hence, \\[ \left(\bar{v}_{2}\right), \ldots, \bar{v}_{r}, \ldots, T^{d n_{r}-1}\left(\bar{v}_{r}\right. \\] is a basis for \(V\). Therefore, by Problem 10.26 and the relation \(\vec{T}^{\prime}(\vec{v})=\overline{T^{i}(v)}\) (see Problem 10.27 ), \\[ \left\\{v_{1}, \ldots, T^{d n_{1}-1}\left(v_{1}\right), v_{2}, \ldots, T^{m_{2}-1}\left(v_{2}\right), \ldots, v_{r}, \ldots, T^{d n_{r}-1}\left(v_{r}\right)\right\\} \\] is a basis for \(V .\) Thus, by Theorem \(10.4, V=Z\left(v_{1}, T\right) \oplus \cdots \oplus Z\left(v_{r}, T\right),\) as required. It remains to show that the exponents \(n_{1}, \ldots, n_{r}\) are uniquely determined by \(T .\) Because \(d=\operatorname{degree}\) of \(f(t)\) \\[ \operatorname{dim} V=d\left(n_{1}+\cdots+n_{r}\right) \quad \text { and } \quad \operatorname{dim} Z_{i}=d n_{i}, \quad i=1, \ldots, r \\] Also, if \(s\) is any positive integer, then (Problem 10.59 ) \(f(T)^{3}\left(Z_{i}\right)\) is a cyclic subspace generated by \(f(T)^{s}\left(v_{i}\right),\) and it has dimension \(d\left(n_{i}-s\right)\) if \(n_{i}>s\) and dimension 0 if \(n_{i} \leq s\) Now any vector \(v \in V\) can be written uniquely in the form \(v=w_{1}+\cdots+w_{r},\) where \(w_{i} \in Z_{i}\) Hence, any vector in \(f(T)^{s}(V)\) can be written uniquely in the form \\[ f(T)^{s}(v)=f(T)^{s}\left(w_{1}\right)+\cdots+f(T)^{s}\left(w_{r}\right) \\] where \(f(T)^{s}\left(w_{i}\right) \in f(T)^{s}\left(Z_{i}\right) .\) Let \(t\) be the integer, dependent on \(s,\) for which \\[ n_{1}>s, \quad \ldots, \quad n_{t}>s, \quad n_{t+1} \geq s \\] Then \\[ f(T)^{s}(V)=f(T)^{s}\left(Z_{1}\right) \oplus \cdots \oplus f(T)^{s}\left(Z_{t}\right) \\] and so \\[ \operatorname{dim}\left[f(T)^{s}(V)\right]=d\left[\left(n_{1}-s\right)+\cdots+\left(n_{t}-s\right)\right] \\] The numbers on the left of (2) are uniquely determined by \(T\). Set \(s=n-1\), and (2) determines the number of \(n_{i}\) equal to \(n\). Next set \(s=n-2,\) and (2) determines the number of \(n_{i}\) (if any) equal to \(n-1\). We repeat the process until we set \(s=0\) and determine the number of \(n_{i}\) equal to \(1 .\) Thus, the \(n_{l}\) are uniquely determined by \(T\) and \(V\), and the lemma is proved.

Prove Theorem 10.10: Let \(T: V \rightarrow V\) be a nilpotent operator of index \(k .\) Then \(T\) has a unique block diagonal matrix representation consisting of Jordan nilpotent blocks \(N .\) There is at least one \(N\) of order \(k,\) and all other \(N\) are of orders \(\leq k .\) The total number of \(N\) of all orders is equal to the nullity of \(T\) Suppose \(\operatorname{dim} V=n .\) Let \(W_{1}=\operatorname{Ker} T, W_{2}=\operatorname{Ker} T^{2}, \ldots, W_{k}=\operatorname{Ker} T^{k} .\) Let us set \(m_{i}=\operatorname{dim} W_{i},\) for \(i=1, \ldots, k .\) Because \(T\) is of index \(k, W_{k}=V\) and \(W_{k-1} \neq V\) and so \(m_{k-1}1 \\ 0 & \text { for } j=1 \end{array}\right. \\] Now it is clear [see Problem \(10.13(\mathrm{d})]\) that \(T\) will have the desired form if the \(v(i, j)\) are ordered lexicographically: beginning with \(v(1,1)\) and moving up the first column to \(v(1, k),\) then jumping to \(v(2,1)\) and moving up the second column as far as possible. Moreover, there will be exactly \(m_{k}-m_{k-1}\) diagonal entries of order \(k\). Also, there will be \(\left(m_{k-1}-m_{k-2}\right)-\left(m_{k}-m_{k-1}\right)=2 m_{k-1}-m_{k}-m_{k-2} \quad\) diagonal entries of order \(k-1\) \(2 m_{2}-m_{1}-m_{3} \quad\) diagonal entries of order 2 \\[ 2 m_{1}-m_{2} \\] diagonal entries of order as can be read off directly from the table. In particular, because the numbers \(m_{1}, \ldots, m_{k}\) are uniquely determined by \(T,\) the number of diagonal entries of each order is uniquely determined by \(T .\) Finally, the identity \\[ m_{1}=\left(m_{k}-m_{k-1}\right)+\left(2 m_{k-1}-m_{k}-m_{k-2}\right)+\cdots+\left(2 m_{2}-m_{1}-m_{3}\right)+\left(2 m_{1}-m_{2}\right) \\] shows that the nullity \(m_{1}\) of \(T\) is the total number of diagonal entries of \(T\)

Suppose \(A\) is a supertriangular matrix (i.e., all entries on and below the main diagonal are 0 ). Show that \(A\) is nilpotent.

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