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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 onto/surjective content
# Definition of onto/surjective linear transformation

Put content here**Definition:** A linear transformation \(T: V \to W\) is *onto* (or *surjective*) if every vector in the codomain \(W\) is the image of at least one vector in the domain \(V\).
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Equivalently: \(T\) is onto iff \(\text{range}(T) = W\).
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Equivalently: For every \(\mathbf{w} \in W\), there exists \(\mathbf{v} \in V\) such that \(T(\mathbf{v}) = \mathbf{w}\).
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**Example (onto):** The projection \(T: \mathbb{R}^3 \to \mathbb{R}^2\) defined by \(T(x,y,z) = (x,y)\) is onto because every vector \((a,b) \in \mathbb{R}^2\) is the image of \((a,b,0)\).
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**Example (not onto):** The map \(T: \mathbb{R}^2 \to \mathbb{R}^3\) defined by \(T(x,y) = (x,y,0)\) is not onto because no vector maps to \((0,0,1)\).
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**Key fact:** If \(T\) is represented by matrix \(A\), then \(T\) is onto iff the columns of \(A\) span \(W\).

# Parents

* Terminology
* Terminology
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