⬜ Kernel A basis for the kernel of the linear transformation (Definition KLT).
\begin{equation*}
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\end{equation*}
⬜ Injective? Is the linear transformation injective (Definition ILT)? Yes.
Since \(\krn{T}=\set{\zerovector}\text{,}\)Theorem KILT tells us that \(T\) is injective.
⬜ Spanning Set for Range A spanning set for the range of a linear transformation (Definition RLT) can be constructed easily by evaluating the linear transformation on a standard basis (Theorem SSRLT).
⬜ Range A basis for the range of the linear transformation (Definition RLT). If the linear transformation is injective, then the spanning set just constructed is guaranteed to be linearly independent (Theorem ILTLI) and is therefore a basis of the range with no changes. Injective or not, this spanning set can be converted to a “nice” linearly independent spanning set by making the vectors the rows of a matrix (perhaps after using a vector representation), row-reducing, and retaining the nonzero rows (Theorem BRS), and perhaps un-coordinatizing.
⬜ Surjective? Is the linear transformation surjective (Definition SLT)? No.
The dimension of the range is 3, and the codomain (\(\complex{5}\)) has dimension 5. So the transformation is not surjective. Notice too that since the domain \(\complex{3}\) has dimension 3, it is impossible for the range to have a dimension greater than 3, and no matter what the actual definition of the function, it cannot possibly be surjective in this situation.
To be more precise, verify that \(\colvector{2\\1\\-3\\2\\6}\not\in\rng{T}\text{,}\) by setting the output equal to this vector and seeing that the resulting system of linear equations has no solution, i.e. is inconsistent. So the preimage, \(\preimage{T}{\colvector{2\\1\\-3\\2\\6}}\text{,}\) is empty. This alone is sufficient to see that the linear transformation is not onto.
⬜ Subspace Dimensions Subspace dimensions associated with the linear transformation (Definition ROLT, Definition NOLT). Verify Theorem RPNDD, and examine parallels with earlier results for matrices.
⬜ Invertible? Is the linear transformation invertible (Definition IVLT, and examine parallels with the existence of matrix inverses.)? No.
The relative dimensions of the domain and codomain prohibit any possibility of being surjective, so apply Theorem ILTIS.
⬜ Matrix Representation Matrix representation of the linear transformation, as described in Theorem MLTCV. (See also Example MOLT.) If \(A\) is the matrix below, then \(\lteval{T}{\vect{x}} = A\vect{x}\text{.}\) This computation may also be viewed as an application of Definition MR and Theorem FTMR from Section MR, where the bases are chosen to be the standard bases of \(\complex{m}\) (Definition SUV).
