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Use the definition of the matrix exponential to compute \(e^{A}\) for each of the following matrices: (a) \(A=\left(\begin{array}{rr}1 & 1 \\ -1 & -1\end{array}\right)\) (b) \(A=\left(\begin{array}{ll}1 & 1 \\ 0 & 1\end{array}\right)\) (c) \(A=\left(\begin{array}{rrr}1 & 0 & -1 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{array}\right)\)

Short Answer

Expert verified
The matrix exponentials for each of the given matrices are: (a) \(e^{A} = \begin{pmatrix} 2 & 1 \\ -1 & 0\end{pmatrix}\) (b) \(e^{A} = \begin{pmatrix}2.6667 & 3 \\ 0 & 2.6667\end{pmatrix}\) (c) \(e^{A} = \begin{pmatrix}2.5 & 0 & -0.5 \\ 0 & 2.1667 & 0 \\ 0 & 0 & 2.5\end{pmatrix}\)

Step by step solution

01

(a) Compute the matrix exponential of A1

Step 1: Identity Matrix First, we need the identity matrix of the same order as the given matrix: \(\displaystyle I=\begin{pmatrix} 1 & 0\\ 0 & 1\end{pmatrix}\) Step 2: Matrix Powers Compute the powers of the matrix A (remember, we only need a few terms): \(A^2= AA =\left( \begin{array}{rr}1 & 1 \\ -1 & -1 \end{array} \right) \left( \begin{array}{rr}1 & 1 \\ -1 & -1 \end{array} \right)=\left( \begin{array}{rr}0 & 0 \\ 0 & 0 \end{array} \right) \) Since \(A^2\) is the zero matrix, all higher powers will also be the zero matrix, and we can simply stop here. Step 3: Matrix Exponential Compute the matrix exponential using the given definition and the computed matrices: \(e^{A} = I + A + \frac{A^2}{2!}\) Substitute the matrices and simplify: \(e^{A} = \begin{pmatrix}1 & 0 \\ 0 & 1\end{pmatrix} + \begin{pmatrix} 1 & 1 \\ -1 & -1\end{pmatrix} + \frac{1}{2}\begin{pmatrix}0 & 0 \\ 0 & 0 \end{pmatrix} = \begin{pmatrix} 2 & 1 \\ -1 & 0\end{pmatrix}\)
02

(b) Compute the matrix exponential of A2

Step 1: Identity Matrix First, we need the identity matrix of the same order as the given matrix: \(\displaystyle I= \begin{pmatrix}1 & 0 \\ 0 & 1\end{pmatrix} \) Step 2: Matrix Powers Compute a few matrix powers of the given A: \(A^2= AA=\left( \begin{array}{ll}1 & 1 \\ 0 & 1\end{array} \right) \left( \begin{array}{ll}1 & 1 \\ 0 & 1\end{array} \right) = \begin{pmatrix}1 & 2 \\ 0 & 1\end{pmatrix} \) \(A^3= A^2A=\left( \begin{array}{ll}1 & 2 \\ 0 & 1\end{array} \right) \left( \begin{array}{ll}1 & 1 \\ 0 & 1\end{array} \right) = \begin{pmatrix}1 & 3 \\ 0 & 1\end{pmatrix} \) The powers of A are growing, and we can stop at \(A^3\) since it should give a reasonable estimation. Step 3: Matrix Exponential Compute the matrix exponential using the given definition and the computed matrices: \(e^{A} = I + A + \frac{A^2}{2!} + \frac{A^3}{3!}\) Substitute the matrices and simplify: \(e^{A} = \begin{pmatrix}1 & 0 \\ 0 & 1\end{pmatrix} + \begin{pmatrix}1 & 1 \\ 0 & 1\end{pmatrix} + \frac{1}{2}\begin{pmatrix} 1 & 2 \\ 0 & 1\end{pmatrix} + \frac{1}{6}\begin{pmatrix}1 & 3 \\ 0 & 1\end{pmatrix} = \begin{pmatrix}1 + 1 + \frac{1}{2} + \frac{1}{6} & 1 + 1 + \frac{3}{2} + \frac{1}{2} \\ 0 & 1 + 1 + \frac{1}{2} + \frac{1}{6}\end{pmatrix} = \begin{pmatrix}2.6667 & 3 \\ 0 & 2.6667\end{pmatrix} \)
03

(c) Compute the matrix exponential of A3

Step 1: Identity Matrix First, we need an identity matrix of the same order as the given matrix: \(\displaystyle I=\begin{pmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix}\) Step 2: Matrix Powers Compute a few matrix powers of the given A: \(A^2 = AA = \begin{pmatrix}1 & 0 & -1 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix}\begin{pmatrix}1 & 0 & -1 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix} = \begin{pmatrix}1 & 0 & 1 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix}\) \(A^3 = A^2A = \begin{pmatrix}1 & 0 & 1 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix}\begin{pmatrix}1 & 0 & -1 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix} = \begin{pmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix} = I\) The powers of A are repeating (the exact convergence depends on the matrix), and we can stop at \(A^3\). Step 3: Matrix Exponential Compute the matrix exponential using the given definition and the computed matrices: \(e^{A} = I + A + \frac{A^2}{2!} + \frac{A^3}{3!}\) Substitute the matrices and simplify: \(e^{A} = \begin{pmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix} + \begin{pmatrix}1 & 0 & -1 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix} + \frac{1}{2}\begin{pmatrix}1 & 0 & 1 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix} + \frac{1}{6}\begin{pmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1\end{pmatrix} = \begin{pmatrix}2.5 & 0 & -0.5 \\ 0 & 2.1667 & 0 \\ 0 & 0 & 2.5\end{pmatrix}\)

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Key Concepts

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

Identity Matrix
In the world of linear algebra, the identity matrix is an incredibly important concept. Think of it as the matrix equivalent of the number 1 when you are dealing with multiplication of numbers. When you multiply any matrix by an identity matrix, the original matrix remains unchanged. This characteristic makes the identity matrix a fundamental element in various matrix operations, especially when it comes to calculating the matrix exponential.

The identity matrix is square, meaning it has an equal number of rows and columns, and has 1s on the main diagonal (the diagonal from the top left to the bottom right) with all other entries being 0. For example, a 2x2 identity matrix looks like this:
  • \[I = \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix}\]
This means any 2x2 matrix multiplied by this identity matrix will not change its values. Understanding the role of the identity matrix is crucial as all computations of the matrix exponential start with adding this matrix.
Matrix Powers
Matrix powers play a crucial role in understanding the matrix exponential. Much like numbers, matrices can be raised to various powers by multiplying the matrix by itself repeatedly. For instance, if you have a matrix \( A \), \( A^2 \) would be \( A \times A \), \( A^3 \) would be \( A \times A \times A \), and so on.

When it comes to the matrix exponential, knowing how to compute relatively low powers of the matrix is often sufficient. This is because in many practical cases (like the matrices in the examples provided), increasing powers become simpler. They often lead to zero matrices or repeat patterns due to the convergence properties of matrix multiplication.
  • The matrix power computation is an ongoing process that helps us grasp deeper connections between the data in the matrix and how functions like the exponential function transform them.
So, matrix powers provide a gateway to the deeper understanding of transformations within linear algebra, especially through exponential functions.
Linear Algebra
Linear algebra is the mathematical field that is heavily involved with vectors and matrices, which are arrays of numbers. It's like a backbone for understanding advanced mathematical concepts and applications in science and engineering.

In linear algebra, we explore numerous operations and transformations that can be performed using matrices, such as rotations, reflections, and scalings, which are essential in fields like computer graphics and data science. The study of matrix exponentials is just one fascinating aspect of linear algebra, revealing how complex transformations unfold over time.
  • Linear algebra provides us with the language and tools to analyze and solve systems of linear equations, understand vector spaces, and delve into eigenvalues and eigenvectors—all pivotal terms when dealing with transformations and operations on matrices.
Understanding the fundamentals of linear algebra helps us unravel how matrix exponentials can model the growth processes and dynamic systems, making it a key player in advancing both theoretical and applied mathematics.

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Most popular questions from this chapter

Let \(A\) be a diagonalizable matrix with characteristic polynomial \\[ p(\lambda)=a_{1} \lambda^{n}+a_{2} \lambda^{n-1}+\cdots+a_{n+1} \\] (a) Show that if \(D\) is a diagonal matrix whose diagonal entries are the eigenvalues of \(A\) then \\[ p(D)=a_{1} D^{n}+a_{2} D^{n-1}+\cdots+a_{n+1} I=O \\] (b) Show that \(p(A)=O\) (c) Show that if \(a_{n+1} \neq 0,\) then \(A\) is nonsingular and \(A^{-1}=q(A)\) for some polynomial \(q\) of degree less than \(n\)

Let \(A\) be a symmetric positive definite matrix with Cholesky decomposition \(A=L L^{T}=R^{T} R\). Prove that the lower triangular matrix \(L\) (or that the upper triangular matrix \(R\) ) in the factorization is unique.

Let \(A\) be a nonnegative irreducible \(3 \times 3\) matrix whose eigenvalues satisfy \(\lambda_{1}=2=\left|\lambda_{2}\right|=\left|\lambda_{3}\right|\) Determine \(\lambda_{2}\) and \(\lambda_{3}\)

Let \(A\) be an \(m \times n\) matrix with rank \(n\) and let \(Q R\) be the factorization obtained when the Gram-Schmidt process is applied to the column vectors of \(A\) Show that if \(A^{T} A\) has Cholesky factorization \(R_{1}^{T} R_{1}\) then \(R_{1}=R\). Thus the upper triangular factors in the Gram-Schmidt QR factorization of \(A\) and the Cholesky decomposition of \(A^{T} A\) are identical.

Let \(A\) be a symmetric \(n \times n\) matrix with eigenvalues \(\lambda_{1}, \ldots, \lambda_{n} .\) Show that there exists an orthonormal set of vectors \(\left\\{\mathbf{x}_{1}, \ldots, \mathbf{x}_{n}\right\\}\) such that \\[ \mathbf{x}^{T} A \mathbf{x}=\sum_{i=1}^{n} \lambda_{i}\left(\mathbf{x}^{T} \mathbf{x}_{i}\right)^{2} \\] for each \(\mathbf{x} \in \mathbb{R}^{n}\)

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