Problem 7
Question
Find the dimension of each matrix. Identify any square, column, or row matrices. Do not use a calculator. $$[-9]$$
Step-by-Step Solution
Verified Answer
The matrix \([-9]\) is a 1x1 square matrix.
1Step 1: Identify the Dimensions of the Matrix
The given matrix is \([-9]\). It is represented by a single element within square brackets. The dimensions of a matrix are given as "rows \( \times \) columns". Since this is a single element, it has 1 row and 1 column.
2Step 2: Determine if the Matrix is Square
A square matrix has the same number of rows and columns. Since this matrix has 1 row and 1 column, it is a square matrix.
3Step 3: Determine if the Matrix is a Row or Column Matrix
A row matrix has 1 row and more than 1 column, while a column matrix has 1 column and more than 1 row. Since this matrix has both 1 row and 1 column, it is neither a row matrix nor a column matrix.
Key Concepts
Square MatrixRow MatrixColumn Matrix
Square Matrix
A square matrix is a special type of matrix where the number of rows is equal to the number of columns. This gives the matrix a symmetrical form, making it "square" in shape. For example, a 3x3 matrix has three rows and three columns, leading to a square arrangement. Square matrices are critical in many mathematical operations, such as computing determinants and finding inverse matrices.
Square matrices can take various dimensions, such as 1x1, 2x2, 3x3, and so on. Each dimension adds more elements to the matrix while maintaining its square structure. In linear algebra, square matrices can represent transformations in vector spaces. Many special types of matrices, like the identity matrix or a diagonal matrix, are also square matrices.
Recognizing a square matrix is crucial because it opens the door to understanding operations unique to such matrices, including solving systems of equations and performing eigenvalue analysis.
Square matrices can take various dimensions, such as 1x1, 2x2, 3x3, and so on. Each dimension adds more elements to the matrix while maintaining its square structure. In linear algebra, square matrices can represent transformations in vector spaces. Many special types of matrices, like the identity matrix or a diagonal matrix, are also square matrices.
Recognizing a square matrix is crucial because it opens the door to understanding operations unique to such matrices, including solving systems of equations and performing eigenvalue analysis.
Row Matrix
A row matrix, as the name suggests, has a single row with one or more columns. This type of matrix looks like a single line of elements, horizontally arranged. In mathematical notation, a row matrix is typically described as a 1xN matrix, where N can be any number greater than one.
For instance, the matrix \([1, 2, 3]\)\ is a row matrix with dimensions 1x3, because it consists of one row and three columns. Row matrices are often used to represent data points in a dataset or coefficients in linear programming.
Understanding row matrices is essential when performing matrix addition or multiplication, as these operations often require specific dimensions for compatibility. A row matrix can also be transposed into a column matrix by "flipping" its orientation, useful in various calculations.
For instance, the matrix \([1, 2, 3]\)\ is a row matrix with dimensions 1x3, because it consists of one row and three columns. Row matrices are often used to represent data points in a dataset or coefficients in linear programming.
Understanding row matrices is essential when performing matrix addition or multiplication, as these operations often require specific dimensions for compatibility. A row matrix can also be transposed into a column matrix by "flipping" its orientation, useful in various calculations.
Column Matrix
A column matrix features a single column with one or more rows, meaning its elements are arranged vertically. In terms of dimensions, a column matrix is often denoted as Nx1, where N is greater than one, representing the number of rows.
As an example, \([\begin{matrix}1 \ 2 \ 3\end{matrix}]\)\ is a column matrix with a dimension of 3x1, containing three rows and one column. This type of matrix is frequently used in systems of equations, where each element of the column can represent a separate equation's coefficients.
Column matrices are vital in vector mathematics, as they can represent a vector in vector spaces and are used extensively in transformations and operations like dot products and cross products. Transforming a column matrix into a row matrix can be easily done via transposition, making it flexible for various mathematical applications.
As an example, \([\begin{matrix}1 \ 2 \ 3\end{matrix}]\)\ is a column matrix with a dimension of 3x1, containing three rows and one column. This type of matrix is frequently used in systems of equations, where each element of the column can represent a separate equation's coefficients.
Column matrices are vital in vector mathematics, as they can represent a vector in vector spaces and are used extensively in transformations and operations like dot products and cross products. Transforming a column matrix into a row matrix can be easily done via transposition, making it flexible for various mathematical applications.
Other exercises in this chapter
Problem 7
Find each determinant. Do not use a calculator. $$\operatorname{det}\left[\begin{array}{rr}3 & 4 \\\5 & -2\end{array}\right]$$
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If the equations are dependent, write the solution set in terms of the variable \(z\). (Hint: In Exercises 33-36, let \(t=\frac{1}{x}, u=\frac{1}{y},\) and \(v=
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Write the augmented matrix for each system. Do not solve the system. $$\begin{array}{l} 2 x+3 y=11 \\ x+2 y=8 \end{array}$$
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Find the partial fraction decomposition for each rational expression. $$\frac{2}{x^{2}(x+3)}$$
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