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Linear Algebra

  1. Vectors
    1. Scalars
    2. Vectors vs Sets
    3. Addition and Subtraction
    4. Scalar Multiplication
    5. Zero Vectors
    6. Linear Combinations
    7. Real Dot Product
    8. Length of a Vector
    9. Orthogonal Vectors
    10. Parallel Vectors
    11. Angle Between Vectors
    12. Unit Vectors
  2. Matrices
    1. Notation
    2. Indexing
    3. Submatrices
    4. Matrix-by-Vector Product
    5. Addition and Subtraction
    6. Scalar Multiplication
    7. Transpose
    8. Symmetries
    9. Matrix Multiplication
    10. Identity Matrix
    11. Non-Negative Integer Powers
    12. Reverse Order Law of Transposition
  3. Linear Systems
    1. Inverse Matrices
    2. Singular Matrices
    3. Linear Dependence
    4. Solutions
  4. Planes
    1. Vector Cross Product
  5. Gaussian Elimination
Linear Algebra β€ΊVectors β€ΊScalar Multiplication

Multiplying a Vector by a Scalar

Another very basic operation on vectors is scalar multiplication. For a basic introduction, you can read through my short lesson about scalars.

Vectors can be multiplied by a scalar, such as 222 or βˆ’1-1βˆ’1 or any number ccc. Scalar multiplication by a scalar ccc is defined as

cvβƒ—=[cv1cv2].c\vec{v} = \begin{bmatrix} cv_1 \\ cv_2 \end{bmatrix}.cv=[cv1​cv2​​].

Let us illustrate this through an example:

vβƒ—=[34].\vec{v} = \begin{bmatrix} 3 \\ 4 \end{bmatrix}.v=[34​].

We now multiply this vector by 222:

2vβƒ—=[2β‹…32β‹…4]=[68].2\vec{v} = \begin{bmatrix} 2 \cdot 3 \\ 2 \cdot 4 \end{bmatrix} = \begin{bmatrix} 6 \\ 8 \end{bmatrix}.2v=[2β‹…32β‹…4​]=[68​].

Graphical Representation

I have touched upon the idea of β€œscaling” a vector in my introductory lesson about scalars. This is because, when depicted graphically, the vector cvβƒ—c\vec{v}cv, is simply a scaled version of vβƒ—\vec{v}v:

Multiplying a vector by a negative number (say βˆ’1-1βˆ’1), simply reverses its direction:

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Adding and Subtracting Vectors
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Zero Vectors
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