Strassen Matrix Multiplication
Goal
 To quickly perform a matrix multiplication operation on two matricies
What is Matrix Multiplication??
Note: If you are already familiar with Linear Algebra/Matrix Multiplication, feel free to skip this section
Before we begin, you may ask what is matrix multiplication? Great question! It is NOT multiplying two matricies termbyterm. Matrix multiplication is a mathematical operation that combines two matricies into a single one. Sounds like multiplying termbyterm huh? It’s not… our lives would be much easier if it were. To see how matrix multiplcation works, let’s look at an example.
Example: Matrix Multiplication
matrix A = 1 2, matrix B = 5 6
3 4 7 8
A * B = C
1 2 * 5 6 = 1*5+2*7 1*6+2*8 = 19 22
3 4 7 8 3*5+4*7 3*6+4*8 43 50
What’s going on here? To start, we’re multiplying matricies A & B. Our new matrix, C’s, elements [i, j]
are determined by the dot product of the first matrix’s ith row and the second matrix’s jth column. See here for a refresher on the dot product.
So the upper left element [i=1, j=1]
of our new matrix is a combination of A’s 1st row and B’s 1st column.
A's first row = [1, 2]
B's first column = [5, 7]
[1, 2] dot [5, 7] = [1*5 + 2*7] = [19] = C[1, 1]
Now let’s try this for [i=1, j=2]
. Because i=1
and j=2
, this will represent the upper right element in our new matrix, C.
A's first row = [1, 2]
B's second column = [6, 8]
[1, 2] dot [6, 8] = [1*6 + 2*8] = [22] = C[1, 2]
If we do this for each row & column of A & B we’ll get our result matrix C!
Here’s a great graphic that visually shows you what’s going on.
Matix Multiplication Algorithm
So how do we implement matrix multiplication in an algoirthm? We’ll start with the basic version and from there move on to Strassen’s Algorithm.
Basic Version
Remember the method we used to solve matrix multiplication above? Let’s try to implement that first! We first assert that the two matricies are the right size.
assert(A.columns == B.rows, "Two matricies can only be matrix mulitiplied if one has dimensions mxn & the other has dimensions nxp where m, n, p are in R")
NOTE: A’s # of columns HAS to equal B’s # of rows for matrix multiplication to work
Next, we loop over A’s columns and B’s rows. Because we know both A’s columns & B’s rows are the same length, we set that length equal to n
.
for i in 0..<n {
for j in 0..<n {
Then, for each row in A and column in B, we take the dot product of the ith row in A with the jth column in B and set that result equal to the [i, j]
element in C. Or C[i, j]
.
for k in 0..<n {
C[i, j] += A[i, k] * B[k, j]
}
Finally, we return our new matrix C!
Here’s the full implementation:
public func matrixMultiply(by B: Matrix<T>) > Matrix<T> {
let A = self
assert(A.columns == B.rows, "Two matricies can only be matrix mulitiplied if one has dimensions mxn & the other has dimensions nxp where m, n, p are in R")
let n = A.columns
var C = Matrix<T>(rows: A.rows, columns: B.columns)
for i in 0..<n {
for j in 0..<n {
for k in 0..<n {
C[i, j] += A[i, k] * B[k, j]
}
}
}
return C
}
This algorithm has a runtime of O(n^3). The O(n^3) comes from the three for
loops. Two from the loop over the rows & columns and one from the dot product!
Now, O(n^3) is not very fast and a great question we should ask is can we do better? Indeed we can!
Strassens Algorithm
Volker Strassen first published his algorithm in 1969. It was the first algorithm to prove that the basic O(n^3) runtime was not optiomal.
The basic idea behind Strassen’s algorithm is to split A & B into 8 submatricies and then recursively compute the submatricies of C. This strategy is called Divide and Conquer.
matrix A = a b, matrix B = e f
c d g h
There will be 8 recursive calls:
 a * e
 b * g
 a * f
 b * h
 c * e
 d * g
 c * f
 d * h
We then use these results to compute C’s submatricies.
matrix C = ae+bg af+bh
ce+dg cf+dh
This step alone, however, doesn’t help our runtime at all. Using the Master Theorem with T(n) = 8T(n/2) + O(n^2)
we still get a runtime of O(n^3)
.
Strassen’s insight was that we don’t actually need 8 recursive calls to complete this process. We can finish the call with 7 recursive calls and a little bit of addition and subtraction.
Strassen’s 7 calls are as follows:
 a * (f  h)
 (a + b) * h
 (c + d) * e
 d * (g  e)
 (a + d) * (e + h)
 (b  d) * (g + h)
 (a  c) * (e + f)
Now we can compute our new matrix C’s new quardents!
matrix C = p5+p4p2+p6 p1+p2 
 p3+p4 p1+p5p3p7
A great reaction right now would be !!??!?!?!!?! How does this even work??
Let’s prove it!
First Submatrix:
p5+p4p2+p6 = (a+d)*(e+h) + d*(ge)  (a+b)*h + (bd)*(g+h)
= (ae+de+ah+dh) + (dgde)  (ah+bh) + (bgdg+bhdh)
= ae+bg ✅
Exactly what we got the first time!
Now let’s prove the others.
Second submatrix:
p1+p2 = a*(fh) + (a+b)*h
= (afah) + (ah+bh)
= af+bh ✅
Third submatrix:
p3+p4 = (c+d)*e + d*(ge)
= (ce+de) + (dgde)
= ce+dg ✅
Fourth submatrix:
p1+p5p3p7 = a*(fh) + (a+d)*(e+h)  (c+d)*e  (ac)*(e+f)
= (afah) + (ae+de+ah+dh) (ce+de)  (aece+afcf)
= cf+dh ✅
Great! The math checks out!
Here’s the process in action.
Implementation
Ok so now to the implementation. We’ll start with the same first step from the basic implementation. We need to assert that A’s # of columns are equal to B’s number of rows.
assert(A.columns == B.rows, "Two matricies can only be matrix mulitiplied if one has dimensions mxn & the other has dimensions nxp where m, n, p are in R")
Now time for some prep work! We make each matrix a square and increase it’s size to the next power of two. This ensures makes Strassen’s Algorithm much easier to manage. We now only need to deal with square matricies that can be broken up an even number of times!
let n = max(A.rows, A.columns, B.rows, B.columns)
let m = nextPowerOfTwo(after: n)
var APrep = Matrix(size: m)
var BPrep = Matrix(size: m)
for i in A.rows {
for j in A.columns {
APrep[i, j] = A[i,j]
}
}
for i in B.rows {
for j in B.columns {
BPrep[i, j] = B[i, j]
}
}
Finally, we recursively compute the matrix using Strassen’s algorithm and the transform our new matrix C
back to the correct dimensions!
let CPrep = APrep.strassenR(by: BPrep)
var C = Matrix(rows: A.rows, columns: B.columns)
for i in 0..<A.rows {
for j in 0..<B.columns {
C[i,j] = CPrep[i,j]
}
}
Recursively Computing the Matrix Multiplication
Next let’s explore this recursive function strassenR
.
We start by initializing 8 submatricies.
var a = Matrix(size: nBy2)
var b = Matrix(size: nBy2)
var c = Matrix(size: nBy2)
var d = Matrix(size: nBy2)
var e = Matrix(size: nBy2)
var f = Matrix(size: nBy2)
var g = Matrix(size: nBy2)
var h = Matrix(size: nBy2)
for i in 0..<nBy2 {
for j in 0..<nBy2 {
a[i,j] = A[i,j]
b[i,j] = A[i, j+nBy2]
c[i,j] = A[i+nBy2, j]
d[i,j] = A[i+nBy2, j+nBy2]
e[i,j] = B[i,j]
f[i,j] = B[i, j+nBy2]
g[i,j] = B[i+nBy2, j]
h[i,j] = B[i+nBy2, j+nBy2]
}
}
We next recursively compute the 7 matrix multiplications.
let p1 = a.strassenR(by: fh) // a * (f  h)
let p2 = (a+b).strassenR(by: h) // (a + b) * h
let p3 = (c+d).strassenR(by: e) // (c + d) * e
let p4 = d.strassenR(by: ge) // d * (g  e)
let p5 = (a+d).strassenR(by: e+h) // (a + d) * (e + h)
let p6 = (bd).strassenR(by: g+h) // (b  d) * (g + h)
let p7 = (ac).strassenR(by: e+f) // (a  c) * (e + f)
Next, we compute the submatricies of C.
let c11 = p5 + p4  p2 + p6 // p5 + p4  p2 + p6
let c12 = p1 + p2 // p1 + p2
let c21 = p3 + p4 // p3 + p4
let c22 = p1 + p5  p3  p7 // p1 + p5  p3  p7
And finally, we combine these submatricies into our new matrix C!
var C = Matrix(size: n)
for i in 0..<nBy2 {
for j in 0..<nBy2 {
C[i, j] = c11[i,j]
C[i, j+nBy2] = c12[i,j]
C[i+nBy2, j] = c21[i,j]
C[i+nBy2, j+nBy2] = c22[i,j]
}
}
As before, we can analyze the time completxity using the Master Theorem. T(n) = 7T(n/2) + O(n^2)
which leads to O(n^log(7))
runtime. This comes out to approxiamtely O(n^2.8074)
which is better than O(n^3)
!
And that’s Strassen’s algorithm! Here’s the full implementation:
// MARK:  Strassen Multiplication
extension Matrix {
public func strassenMatrixMultiply(by B: Matrix<T>) > Matrix<T> {
let A = self
assert(A.columns == B.rows, "Two matricies can only be matrix mulitiplied if one has dimensions mxn & the other has dimensions nxp where m, n, p are in R")
let n = max(A.rows, A.columns, B.rows, B.columns)
let m = nextPowerOfTwo(after: n)
var APrep = Matrix(size: m)
var BPrep = Matrix(size: m)
A.forEach { (i, j) in
APrep[i,j] = A[i,j]
}
B.forEach { (i, j) in
BPrep[i,j] = B[i,j]
}
let CPrep = APrep.strassenR(by: BPrep)
var C = Matrix(rows: A.rows, columns: B.columns)
for i in 0..<A.rows {
for j in 0..<B.columns {
C[i,j] = CPrep[i,j]
}
}
return C
}
private func strassenR(by B: Matrix<T>) > Matrix<T> {
let A = self
assert(A.isSquare && B.isSquare, "This function requires square matricies!")
guard A.rows > 1 && B.rows > 1 else { return A * B }
let n = A.rows
let nBy2 = n / 2
/*
Assume submatricies are allocated as follows
matrix A = a b, matrix B = e f
c d g h
*/
var a = Matrix(size: nBy2)
var b = Matrix(size: nBy2)
var c = Matrix(size: nBy2)
var d = Matrix(size: nBy2)
var e = Matrix(size: nBy2)
var f = Matrix(size: nBy2)
var g = Matrix(size: nBy2)
var h = Matrix(size: nBy2)
for i in 0..<nBy2 {
for j in 0..<nBy2 {
a[i,j] = A[i,j]
b[i,j] = A[i, j+nBy2]
c[i,j] = A[i+nBy2, j]
d[i,j] = A[i+nBy2, j+nBy2]
e[i,j] = B[i,j]
f[i,j] = B[i, j+nBy2]
g[i,j] = B[i+nBy2, j]
h[i,j] = B[i+nBy2, j+nBy2]
}
}
let p1 = a.strassenR(by: fh) // a * (f  h)
let p2 = (a+b).strassenR(by: h) // (a + b) * h
let p3 = (c+d).strassenR(by: e) // (c + d) * e
let p4 = d.strassenR(by: ge) // d * (g  e)
let p5 = (a+d).strassenR(by: e+h) // (a + d) * (e + h)
let p6 = (bd).strassenR(by: g+h) // (b  d) * (g + h)
let p7 = (ac).strassenR(by: e+f) // (a  c) * (e + f)
let c11 = p5 + p4  p2 + p6 // p5 + p4  p2 + p6
let c12 = p1 + p2 // p1 + p2
let c21 = p3 + p4 // p3 + p4
let c22 = p1 + p5  p3  p7 // p1 + p5  p3  p7
var C = Matrix(size: n)
for i in 0..<nBy2 {
for j in 0..<nBy2 {
C[i, j] = c11[i,j]
C[i, j+nBy2] = c12[i,j]
C[i+nBy2, j] = c21[i,j]
C[i+nBy2, j+nBy2] = c22[i,j]
}
}
return C
}
private func nextPowerOfTwo(after n: Int) > Int {
return Int(pow(2, ceil(log2(Double(n)))))
}
}
Appendix
Number Protocol
I use a number protocol to enable by Matrix to be generic.
The Number protocol ensures three things:
 Everything that is a number can be multiplied
 Everything that is a number can be added/subtracted
 Everything that is a number has a zero value
Extending Int
, Float
, and Double
to conform to this protocol is now very straightforward. All you need to do is implement the static var zero
!
public protocol Number: Multipliable, Addable {
static var zero: Self { get }
}
public protocol Addable {
static func +(lhs: Self, rhs: Self) > Self
static func (lhs: Self, rhs: Self) > Self
}
public protocol Multipliable {
static func *(lhs: Self, rhs: Self) > Self
}
Dot Product
I extend Array
to include a dot product function for when the Array’s element conform to the Number
protocol.
extension Array where Element: Number {
public func dot(_ b: Array<Element>) > Element {
let a = self
assert(a.count == b.count, "Can only take the dot product of arrays of the same length!")
let c = a.indices.map{ a[$0] * b[$0] }
return c.reduce(Element.zero, { $0 + $1 })
}
}
Resources

[Intro to Matrix Multiplication Khan Academy](https://www.khanacademy.org/math/precalculus/precalcmatrices/multiplyingmatricesbymatrices/v/matrixmultiplicationintro) 
[Matrix Multiplication Wikipedia](https://en.wikipedia.org/wiki/Matrix_multiplication) 
[Strassen Algorithm Wikipedia](https://en.wikipedia.org/wiki/Strassen_algorithm) 
[Strassen Algorithm Wolfram MathWorld](http://mathworld.wolfram.com/StrassenFormulas.html) 
[Strassens Algorithm Geeks for Geeks](http://www.geeksforgeeks.org/strassensmatrixmultiplication/)
Written for Swift Algorithm Club by Richard Ash