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  • Terminology

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  • Definition of augmented matrix (of a linear system)
  • Definition of coefficient matrix of a linear system
  • Definition of constant vector of a linear system
  • Definition of solution vector of a linear system
  • Definition of matrix representation of a linear system
  • Definition of domain of a linear transformation
  • Definition of codomain of a linear transformation
  • Definition of image (of a point) under a linear transformation
  • Definition of pre-image (of a point) under a linear transformation
  • Definition of onto/surjective linear transformation
  • Definition of one-to-one/injective linear transformation
  • Definition of range of linear transformation
  • Definition of kernel of linear transformation
  • Definition of invertible linear transformation
  • Definition of inverse of a linear transformation
  • Non-example of a linear transformation
  • Definition of linear transformation/homomorphism
  • Definition of identity linear transformation
  • Definition of sum of linear transformations
  • The sum of linear transformations is a linear transformation
  • Definition of scalar multiple of a linear transformation
  • A scalar multiple of a linear transformation is a linear transformation
  • Definition of pre-image of linear transformation
  • Definition of range of a linear transformation
  • Definition of invertible/nonsingular linear transformation
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Description:Added codomain definition content
# Definition of codomain of a linear transformation

Put content here**Definition:** The *codomain* of a linear transformation \(T\) is the vector space \(W\) into which the transformation maps its output vectors.
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Notation: If \(T: V \to W\), then \(W\) is the codomain of \(T\).
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The codomain specifies the *type* of output vectors. It may be larger than the actual set of outputs (which is called the *range* or *image*).
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**Example:** For \(T: \mathbb{R}^2 \to \mathbb{R}^3\) defined by \(T(x,y) = (x, y, 0)\), the codomain is \(\mathbb{R}^3\), but the range is only the \(xy\)-plane within \(\mathbb{R}^3\).

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