Linear Algebra for Team-Based Inquiry Learning
2023 Early Edition
Steven Clontz | Drew Lewis |
---|---|
University of South Alabama | University of South Alabama |
December 22, 2022
Section 3.2: Standard Matrices (AT2)
Remark 3.2.1
Recall that a linear map T:V\rightarrow W satisfies
T(\vec{v}+\vec{w}) = T(\vec{v})+T(\vec{w}) for any \vec{v},\vec{w} \in V\text{.}
T(c\vec{v}) = cT(\vec{v}) for any c \in \IR,\vec{v} \in V\text{.}
In other words, a map is linear when vector space operations can be applied before or after the transformation without affecting the result.
Activity 3.2.2 (~5 min)
Suppose T: \IR^3 \rightarrow \IR^2 is a linear map, and you know T\left(\left[\begin{array}{c} 1 \\ 0 \\ 0 \end{array}\right] \right) = \left[\begin{array}{c} 2 \\ 1 \end{array}\right] and T\left(\left[\begin{array}{c} 0 \\ 0 \\ 1 \end{array}\right] \right) = \left[\begin{array}{c} -3 \\ 2 \end{array}\right] \text{.} What is T\left(\left[\begin{array}{c} 3 \\ 0 \\ 0 \end{array}\right]\right)\text{?}
- \displaystyle \left[\begin{array}{c} 6 \\ 3\end{array}\right]
- \displaystyle \left[\begin{array}{c} -9 \\ 6 \end{array}\right]
- \displaystyle \left[\begin{array}{c} -4 \\ -2 \end{array}\right]
- \displaystyle \left[\begin{array}{c} 6 \\ -4 \end{array}\right]
Activity 3.2.3 (~5 min)
Suppose T: \IR^3 \rightarrow \IR^2 is a linear map, and you know T\left(\left[\begin{array}{c} 1 \\ 0 \\ 0 \end{array}\right] \right) = \left[\begin{array}{c} 2 \\ 1 \end{array}\right] and T\left(\left[\begin{array}{c} 0 \\ 0 \\ 1 \end{array}\right] \right) = \left[\begin{array}{c} -3 \\ 2 \end{array}\right] \text{.} What is T\left(\left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right]\right)\text{?}
\displaystyle \left[\begin{array}{c} 2 \\ 1\end{array}\right]
\displaystyle \left[\begin{array}{c} 3 \\ -1 \end{array}\right]
\displaystyle \left[\begin{array}{c} -1 \\ 3 \end{array}\right]
\displaystyle \left[\begin{array}{c} 5 \\ -8 \end{array}\right]
Activity 3.2.4 (~5 min)
Suppose T: \IR^3 \rightarrow \IR^2 is a linear map, and you know T\left(\left[\begin{array}{c} 1 \\ 0 \\ 0 \end{array}\right] \right) = \left[\begin{array}{c} 2 \\ 1 \end{array}\right] and T\left(\left[\begin{array}{c} 0 \\ 0 \\ 1 \end{array}\right] \right) = \left[\begin{array}{c} -3 \\ 2 \end{array}\right] \text{.} What is T\left(\left[\begin{array}{c} -2 \\ 0 \\ -3 \end{array}\right]\right)\text{?}
\displaystyle \left[\begin{array}{c} 2 \\ 1\end{array}\right]
\displaystyle \left[\begin{array}{c} 3 \\ -1 \end{array}\right]
\displaystyle \left[\begin{array}{c} -1 \\ 3 \end{array}\right]
\displaystyle \left[\begin{array}{c} 5 \\ -8 \end{array}\right]
Activity 3.2.5 (~5 min)
Suppose T: \IR^3 \rightarrow \IR^2 is a linear map, and you know T\left(\left[\begin{array}{c} 1 \\ 0 \\ 0 \end{array}\right] \right) = \left[\begin{array}{c} 2 \\ 1 \end{array}\right] and T\left(\left[\begin{array}{c} 0 \\ 0 \\ 1 \end{array}\right] \right) = \left[\begin{array}{c} -3 \\ 2 \end{array}\right] \text{.} What piece of information would help you compute T\left(\left[\begin{array}{c}0\\4\\-1\end{array}\right]\right)\text{?}
The value of T\left(\left[\begin{array}{c} 0\\-4\\0\end{array}\right]\right)\text{.}
The value of T\left(\left[\begin{array}{c} 0\\1\\0\end{array}\right]\right)\text{.}
The value of T\left(\left[\begin{array}{c} 1\\1\\1\end{array}\right]\right)\text{.}
Any of the above.
Fact 3.2.6
Consider any basis \{\vec b_1,\dots,\vec b_n\} for V\text{.} Since every vector \vec v can be written as a linear combination of basis vectors, \vec v = x_1\vec b_1+\dots+ x_n\vec b_n\text{,} we may compute T(\vec v) as follows:
Therefore any linear transformation T:V \rightarrow W can be defined by just describing the values of T(\vec b_i)\text{.}
Put another way, the images of the basis vectors completely determine the transformation T\text{.}
Definition 3.2.7
Since a linear transformation T:\IR^n\to\IR^m is determined by its action on the standard basis \{\vec e_1,\dots,\vec e_n\}\text{,} it is convenient to store this information in an m\times n matrix, called the standard matrix of T\text{,} given by [T(\vec e_1) \,\cdots\, T(\vec e_n)]\text{.}
For example, let T: \IR^3 \rightarrow \IR^2 be the linear map determined by the following values for T applied to the standard basis of \IR^3\text{.}
Then the standard matrix corresponding to T is
Activity 3.2.8 (~3 min)
Let T: \IR^4 \rightarrow \IR^3 be the linear transformation given by
Activity 3.2.9 (~5 min)
Let T: \IR^3 \rightarrow \IR^2 be the linear transformation given by
Part 1.
Compute T(\vec e_1)\text{,} T(\vec e_2)\text{,} and T(\vec e_3)\text{.}
Activity 3.2.9 (~5 min)
Let T: \IR^3 \rightarrow \IR^2 be the linear transformation given by
Part 2.
Find the standard matrix for T\text{.}
Fact 3.2.10
Because every linear map T:\IR^m\to\IR^n has a linear combination of the variables in each component, and thus T(\vec e_i) yields exactly the coefficients of x_i\text{,} the standard matrix for T is simply an ordered list of the coefficients of the x_i\text{:}
Activity 3.2.11 (~5 min)
Let T: \IR^3 \rightarrow \IR^3 be the linear transformation given by the standard matrix
Part 1.
Compute T\left(\left[\begin{array}{c} 1\\ 2 \\ 3 \end{array}\right] \right) \text{.}
Activity 3.2.11 (~5 min)
Let T: \IR^3 \rightarrow \IR^3 be the linear transformation given by the standard matrix
Part 2.
Compute T\left(\left[\begin{array}{c} x\\ y \\ z \end{array}\right] \right) \text{.}
Activity 3.2.12 (~15 min)
Compute the following linear transformations of vectors given their standard matrices.
Part 1.
Activity 3.2.12 (~15 min)
Compute the following linear transformations of vectors given their standard matrices.
Part 2.
Activity 3.2.12 (~15 min)
Compute the following linear transformations of vectors given their standard matrices.
Part 3.