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  • Terminology
  • Equivalence theorems for injective transformations

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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
  • Equivalence theorem for injective linear transformations: The inverse of T is a linear transformation on its range.
  • Equivalence theorem for injective linear transformations: The null space of T is 0.
  • Equivalence theorem for injective linear transformations: The nullity of T is 0.
  • Equivalence theorem for injective linear transformations: The kernel of T is 0.
  • Equivalence theorem for injective linear transformations: T(x)=0 only for x=0.
  • Equivalence theorem for injective linear transformations: The rank of T is n.
  • Equivalence theorem for injective linear transformations: The rank of T is equals the number of columns in any matrix representation..
  • Equivalence theorem for injective linear transformations: The image of a basis for V is a basis for the range of T.
  • Equivalence theorem for injective linear transformations: The columns of the matrix of T are linearly independent.
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Description:Added one-to-one/injective content
# Definition of one-to-one/injective linear transformation

Put content here**Definition:** A linear transformation \(T: V \to W\) is *one-to-one* (or *injective*) if distinct vectors in the domain map to distinct vectors in the codomain.
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Equivalently: If \(T(\mathbf{u}) = T(\mathbf{v})\), then \(\mathbf{u} = \mathbf{v}\).
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For linear transformations, this is equivalent to: \(\ker(T) = \{\mathbf{0}\}\) (only the zero vector maps to zero).
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**Example (one-to-one):** The rotation \(T: \mathbb{R}^2 \to \mathbb{R}^2\) by 90 degrees, \(T(x,y) = (-y, x)\), is one-to-one.
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**Example (not one-to-one):** The projection \(T: \mathbb{R}^3 \to \mathbb{R}^2\) defined by \(T(x,y,z) = (x,y)\) is not one-to-one because \(T(0,0,1) = T(0,0,2) = (0,0)\).
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**Key fact:** If \(T\) is represented by matrix \(A\), then \(T\) is one-to-one iff the columns of \(A\) are linearly independent.

# Parents

* Terminology
* Equivalence theorems for injective transformations
* Terminology⏎
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