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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 ›Matrices

Matrices - Introduction

Before you dive into matrices, make sure you’re comfortable with the concept of vectors.

The next important mathematical structure in the field of linear algebra is the matrix.

What Is a Matrix?

A matrix can be thought of in different ways. I will not give a definition which is too formal, since that would be beside the point, and the knowledge covered in the course would not suffice to understand it.

One way to think of a matrix is as a vector of vectors. Until now, I have only used numbers inside of vectors, but nothing stops us from putting a vector inside another vector. This idea is very common in computer science, since in low-level languages, there’s no such thing as a “matrix”, it’s really just an array of arrays.

Another way to think of a matrix is as a natural extension of a vector. What I mean is that a vector is basically an n×1n \times 1n×1 “grid”, and a matrix is a m×nm \times nm×n “grid”.

Whatever model you like best, in essence, a matrix is just a rectangular arrangement of elements.

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Unit Vectors
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Matrix Notation
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