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a. Find four matrices \(A \in \mathbb{M}(2,2)\) with \(A^{2}=I\). b. Determine all matrices \(A \in \mathbb{M}(2,2)\) with \(A^{2}=I\).

Short Answer

Expert verified
The four instances of 2x2 matrices which squared will yield the identity matrix are \( \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix} \), \( \begin{pmatrix} -1 & 0 \ 0 & -1 \end{pmatrix} \), \( \begin{pmatrix} 1 & a \ a & 1 \end{pmatrix} \), and \( \begin{pmatrix} -1 & a \ a & -1 \end{pmatrix} \), for \(a \in \mathbb{R}\setminus\{0\}\).

Step by step solution

01

Finding Four Matrices

The identity matrix \(I\) in the set of 2x2 matrices is \( \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix} \). Thus, we need to find matrices \(A\) for which \(A^2 = I\). Four such matrices can be: \( \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix} \), \( \begin{pmatrix} -1 & 0 \ 0 & -1 \end{pmatrix} \),\( \begin{pmatrix} 0 & -1 \ 1 & 0 \end{pmatrix} \), \( \begin{pmatrix} 0 & 1 \ -1 & 0 \end{pmatrix} \)
02

Deriving General Form

The general form of a 2x2 matrix is: \(A= \begin{pmatrix} a & b \ c & d \end{pmatrix} \)If \(A^2=I\), then \(A^2 = \begin{pmatrix} a & b \ c & d \end{pmatrix} \begin{pmatrix} a & b \ c & d \end{pmatrix} = \begin{pmatrix} a^2 + b*c & a*b + b*d \ a*c + c*d & b*c + d^2 \end{pmatrix} = \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix} \).This gives us a system of equations:1. \(a^2 + b*c = 1\)2. \(a*b + b*d = 0\), which gives \(b*(a+d) = 0\)3. \(a*c + c*d = 0\), which gives \(c*(a+d) = 0\)4. \(b*c + d^2 = 1\)From equations 2 and 3, either \(b = 0\), or \(c = 0\), or \(a + d = 0\).Case 1: If \(b = 0\) and \(c = 0\), from equations 1 and 4 we get \(a^2 = d^2 = 1\), hence \(a,d\) can be either 1 or -1. Case 2: If \(a+d = 0\), we substitute \(a = -d\) into equation 1 to get \(-d^2 + d^2 = 1\), which is impossible. Hence \(a+d \neq 0\).Therefore the only solutions to \(A^2 = I\) are matrices where \(a = d = ±1\) and \(b = c = 0\), or \(a = -d\) and \(b = -c = ±1\).
03

Determining all matrices

With the above derivations, it can be determined that all 2x2 matrices \(A\) with \(A^{2}=I\) are: 1. \( \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix} \)2. \( \begin{pmatrix} -1 & 0 \ 0 & -1 \end{pmatrix} \)3. \( \begin{pmatrix} 1 & a \ a & 1 \end{pmatrix} \), \(a \in \mathbb{R}\setminus\{0\}\)4. \( \begin{pmatrix} -1 & a \ a & -1 \end{pmatrix} \), \(a \in \mathbb{R}\setminus\{0\}\)

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Key Concepts

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

Identity Matrix
In the context of linear algebra, the identity matrix plays a crucial role similar to the number one in arithmetic. It is denoted as I and is a square matrix with ones on the diagonal and zeroes everywhere else. The identity matrix acts as a multiplicative identity; when it's multiplied by any matrix A of appropriate size, it leaves A unchanged.

For a 2x2 identity matrix, it is represented as:
\[ I = \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix} \]
This matrix is central to the exercise at hand, where we're looking for matrices that when squared, return the identity matrix.
Matrix Multiplication
Matrix multiplication is a way to combine two matrices to produce a third one, and it's foundational for this problem. The operation isn’t simply multiplying individual entries but rather involves taking the dot product of rows from the first matrix with columns from the second.

A key point to remember is that matrix multiplication is not commutative, meaning that AB ≠ BA in general. Here, we're specifically interested in a matrix A that when multiplied by itself (A times A, or A2), gives the identity matrix, which implies a deeper form of symmetry within A.
System of Linear Equations
A critical part of solving the provided problem is understanding systems of linear equations. These are collections of equations that share the same set of variables. Our goal is to find a solution that satisfies all equations simultaneously.

In this exercise, matrix multiplication yields a system of linear equations, as each element in the resulting matrix equates to the corresponding element in the identity matrix. Solving such a system involves either substitution, elimination, or matrix methods like row reduction. These solutions really pin down the structure of our matrix A, helping us clarify which matrices, when squared, lead back to the identity.
2x2 Matrices
2x2 matrices are the simplest form of square matrices beyond 1x1 and have applications across basic linear algebra problems. A 2x2 matrix consists of four elements arranged in two rows and two columns and is often the go-to example for teaching matrix concepts because of its simplicity.

In our scenario, understanding 2x2 matrices is essential since we're tasked with finding matrices of this size that fulfill the special property of squaring to the identity matrix. The exercise involves exploring the elements of such matrices and how they interact under the operation of matrix multiplication to satisfy the given equation.

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Most popular questions from this chapter

Many characteristics of plants and animals are determined genetically. Suppose the height of a variety of corm is determined by a gene that comes in two forms, \(T\) and \(t\). Each plant has a pair of these genes and can thus be pure dominant type \(T T\), pure recessive type \(t\), or hybrid \(T\). Each seed obtains one of the two genes from one plant and the second from another plant. In each generation, a geneticist randomly selects a pair of plants from all possible offspring. These are used to breed the plants for the next generation. a. Set up a Markov chain to model this experiment. The states will be the pairs of genotypes of the parents. Since it does not matter which parent plant contributed which gene to the offspring, you need only consider six states: \((T T, T T),(T T, T t),(T T, t t),(T t, T t),(T t, t t)\), and \((t t, t t)\). b. Show that this is an absorbing Markov chain. c. Interpret the results of the Fundamental Theorem of Absorbing Markov Chains for this model.

An economist has created a new index that categorizes the daily movements of the stock market as up sharply, up, steady, down, or down sharply. Suppose she determines that from one day to the next, the index either remains in the same category or moves to an adjacent category, with all possibilities being equally likely to occur. a. Formulate this model as a Markov chain. b. Use the Fundamental Theorem of Regular Markov Chains to determine the steady-state distribution of the index. c. In the long run, how often would you expect the index to be up sharply?

a. If \(A\) is an \(m \times n\) matrix, what sizes must the zero matrices be in the equation \(A 0=0\) ? b. Show that with \(\boldsymbol{0}\) matrices of the proper sizes, the equation does indeed hold. c. Give a precise statement and proof of the matrix equation \(\boldsymbol{\theta A}=\mathbf{0}\).

Determine the ranks of the following matrices. a. \(\left[\begin{array}{llll}1 & 4 & 0 & 3 \\ 2 & 8 & 1 & 6 \\ 0 & 1 & 1 & 1\end{array}\right]\) b. \(\left[\begin{array}{ll}4 & 6 \\ 6 & 9 \\ 0 & 0 \\ 2 & 3\end{array}\right]\) c. \(\left[\begin{array}{llll}1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 \\ 1 & 1 & 1 & 1 \\\ 1 & 1 & 1 & 1\end{array}\right]\) d. \(\left[\begin{array}{rrrr}16 & 3 & 2 & 13 \\ 5 & 10 & 11 & 8 \\ 9 & 6 & 7 & 12 \\ 4 & 15 & 14 & 1\end{array}\right]\)

a. Show that \(\left[\begin{array}{r}2 \\ -1\end{array}\right]\) is a right inverse of the matrix \(\left[\begin{array}{ll}1 & 1\end{array}\right]\). b. Show that \(\left[\begin{array}{r}2 \\ -1\end{array}\right]\) is not a left inverse of the matrix \(\left[\begin{array}{ll}1 & 1\end{array}\right]\). c. Show that \(\left[\begin{array}{ll}1 & 1\end{array}\right]\) does not have a left inverse.

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