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Matrix multiplication

Matrix multiplication is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Matrix multiplication rather than just read about it. In short: In mathematics, specifically in linear algebra, matrix multiplication is a binary operation that produces a matrix from two matrices. For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the second matrix.

Matrix multiplication — main illustration
Matrix multiplication — illustration

Key takeaways

  • Matrix multiplication belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Matrix multiplication to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Matrix multiplication from memory before moving on to harder problems.

Reference excerpt

In mathematics, specifically in linear algebra, matrix multiplication is a binary operation that produces a matrix from two matrices. For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the second matrix. The resulting matrix, known as the matrix product, has the number of rows of the first and the number of columns of the second matrix. The product of matrices A and B is denoted as AB. Matrix multiplication was first described by the French mathematician Jacques Philippe Marie Binet in 1812, to represent the composition of linear maps that are represented by matrices. Matrix multiplication is thus a basic tool of linear algebra, and as such has numerous applications in many areas of mathematics, as well as in applied mathematics, statistics, physics, economics, and engineering. Computing matrix products is a central operation in all computational applications of linear algebra.

Notation This article will use the following notational conventions: matrices are represented by capital letters in bold, e.g. A; vectors in lowercase bold, e.g. a; and entries of vectors and matrices are italic (they are numbers from a field), e.g. A and a. Index notation is often the clearest way to express definitions, and is used as standard in the literature. The entry in row i, column j of matrix A is indicated by (A)ij, Aij or aij. In contrast, a single subscript, e.g. A1, A2, is used to select a matrix (not a matrix entry) from a collection of matrices.

Definitions

Matrix times matrix If A is an m × n matrix and B is an n × p matrix,

A = ( a 11 a 12 ⋯ a 1 n a 21 a 22 ⋯ a 2 n ⋮ ⋮ ⋱ ⋮ a m 1 a m 2 ⋯ a m n ) , B = ( b 11 b 12 ⋯ b 1 p b 21 b 22 ⋯ b 2 p ⋮ ⋮ ⋱ ⋮ b n 1 b n 2 ⋯ b n p ) {\displaystyle \mathbf {A} ={\begin{pmatrix}a_{11}&a_{12}&\cdots &a_{1n}\\a_{21}&a_{22}&\cdots &a_{2n}\\\vdots &\vdots &\ddots &\vdots \\a_{m1}&a_{m2}&\cdots &a_{mn}\\\end{pmatrix}},\quad \mathbf {B} ={\begin{pmatrix}b_{11}&b_{12}&\cdots &b_{1p}\\b_{21}&b_{22}&\cdots &b_{2p}\\\vdots &\vdots &\ddots &\vdots \\b_{n1}&b_{n2}&\cdots &b_{np}\\\end{pmatrix}}}

the matrix product C = AB (denoted without multiplication signs or dots) is defined to be the m × p matrix

… excerpt ends here. Continue reading the full article.

Illustrations

Matrix multiplication: For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the second matrix. The result matrix has the number of rows of the first and the number of columns of the second matrix.
For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the second matrix. The result matrix has the number of rows of the first and the number of columns of the second matrix.
Matrix multiplication illustration
Matrix multiplication: The computation of the bottom left entry of 
  
    
      
        
          A
          B
        
      
    
    {\displaystyle \mathbf {AB} }
  
 corresponds to the consideration of all paths (highlighted) from basic commodity 
  
    
      
        
          b
          
            4
          
        
      
    
    {\displaystyle b_{4}}
  
 to final product 
  
    
      
        
          f
          
            1
          
        
      
    
    {\displaystyle f_{1}}
  
 in the production flow graph.
The computation of the bottom left entry of A B {\displaystyle \mathbf {AB} } corresponds to the consideration of all paths (highlighted) from basic commodity b 4 {\displaystyle b_{4}} to final product f 1 {\displaystyle f_{1}} in the production flow graph.
Matrix multiplication: Improvement of estimates of exponent ω over time for the computational complexity of matrix multiplication 
  
    
      
        O
        (
        
          n
          
            ω
          
        
        )
      
    
    {\displaystyle O(n^{\omega })}
Improvement of estimates of exponent ω over time for the computational complexity of matrix multiplication O ( n ω ) {\displaystyle O(n^{\omega })}

Worked examples

Example 1 — a first encounter with Matrix multiplication

Start with the simplest possible case. Write down what Matrix multiplication claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Matrix multiplication before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Matrix multiplication ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Matrix multiplication

In research
Matrix multiplication appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Matrix multiplication in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Matrix multiplication is common in secondary-school and first-year university syllabi. It links to neighbouring topics Bilinear maps, Matrix theory, Multiplication, so understanding it makes those chapters shorter.
In everyday life
Look for Matrix multiplication outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Matrix multiplication in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Matrix multiplication means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Matrix multiplication out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Matrix multiplication in simple terms?

In mathematics, specifically in linear algebra, matrix multiplication is a binary operation that produces a matrix from two matrices. For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the second matrix.

Why does Matrix multiplication matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Matrix multiplication?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Matrix multiplication.

Tags

  • Bilinear maps
  • Matrix theory
  • Multiplication
  • Numerical linear algebra

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