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phlegmaticprogrammer/LANumerics
LANumerics is a Swift package for doing numerical linear algebra.
.package(url: "https://github.com/phlegmaticprogrammer/LANumerics.git", from: "v0.1.12")

LANumerics

Copyright (c) 2020 Steven Obua

License: MIT License

LANumerics is a Swift package for doing numerical linear algebra.

The package depends on Swift Numerics, as it supports both real and complex numerics for both Float and Double precision in a uniform way. Under the hood it relies on the Accelerate framework for most of its functionality, in particular BLAS and LAPACK, and also vDSP.

Examining the current tests provides a good starting point beyond this README.

Table of Contents


Usage

LANumerics is a normal Swift package and can be added to your app in the usual way. After adding it to your app, import LANumerics (and also Numerics if you use complex numbers).

You can try out if everything works fine by running

import LANumerics
let A : Matrix<Float> = Matrix(columns: [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
print("A: \(A)")

which should output something like

A: 4x3-matrix:
⎛1.0  5.0  9.0 ⎞
⎜2.0  6.0  10.0⎟
⎜3.0  7.0  11.0⎟
⎝4.0  8.0  12.0⎠

LANumeric

The LANumeric protocol denotes the type of numbers on which LANumerics operates. It is implemented by the following types:

  • Float
  • Double
  • Complex<Float>
  • Complex<Double>

Most functionality of LANumerics is generic in LANumeric, e.g. constructing matrices and computing with them, solving a system of linear equations, or computing the singular value decomposition of a matrix.

Constructing Matrices

The main work horse of LANumerics is the Matrix type. For convenience there is also a Vector type, but this is just a typealias for normal Swift arrays.

The expression Matrix([1,2,3]) constructs the matrix:

3x1-matrix:
⎛1.0⎞
⎜2.0⎟
⎝3.0⎠

The expression Matrix(row: [1, 2, 3]) constructs the matrix:

1x3-matrix:
(1.0  2.0  3.0)

The expression Matrix<Float>(rows: 2, columns: 3) constructs a matrix consisting only of zeros:

2x3-matrix:
⎛0.0  0.0  0.0⎞
⎝0.0  0.0  0.0⎠

The expression Matrix(repeating: 1, rows: 2, columns: 3) constructs a matrix consisting only of ones:

2x3-matrix:
⎛1.0  1.0  1.0⎞
⎝1.0  1.0  1.0⎠

Given the two vectors v1 and v2

let v1 : Vector<Float> = [1, 2, 3]
let v2 : Vector<Float> = [4, 5, 6]

we can create a matrix from columns Matrix(columns: [v1, v2]):

3x2-matrix:
⎛1.0  4.0⎞
⎜2.0  5.0⎟
⎝3.0  6.0⎠

or rows Matrix(rows: [v1, v2]):

2x3-matrix:
⎛1.0  2.0  3.0⎞
⎝4.0  5.0  6.0⎠

It is also legal to create matrices with zero columns and/or rows, like Matrix(rows: 2, columns: 0) or Matrix(rows: 0, columns: 0).

SIMD Support

Swift supports simd vector and matrix operations. LANumerics plays nice with simd by providing conversion functions to and from simd vectors and matrices. For example, starting from

import simd
import LANumerics

let m = Matrix(rows: [[1, 2, 3], [4, 5, 6]])
print("m: \(m)")

with output

m: 2x3-matrix:
⎛1.0  2.0  3.0⎞
⎝4.0  5.0  6.0⎠

we can convert m into a simd matrix s via

let s = m.simd3x2
print(s)

resulting in the output

simd_double3x2(columns: (SIMD2<Double>(1.0, 4.0), SIMD2<Double>(2.0, 5.0), SIMD2<Double>(3.0, 6.0)))

Note that simd reverses the role of row and column indices compared to LANumerics (and usual mathematical convention).

We can also convert s back:

print(Matrix(s) == m)

will yield the output true.

Accessing Matrix Elements and Submatrices

Matrix elements and submatrices can be accessed using familiar notation. Given

import simd
import LANumerics

var m = Matrix(rows: [[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(m)

with output

3x3-matrix:
⎛1.0  2.0  3.0⎞
⎜4.0  5.0  6.0⎟
⎝7.0  8.0  9.0⎠

we can access the element at row 2 and column 1 via

m[2, 1]

which yields 8.0. We can also set the element at row 2 and column 1 to some value:

m[2, 1] = 0
print(m)

The output of running this is

3x3-matrix:
⎛1.0  2.0  3.0⎞
⎜4.0  5.0  6.0⎟
⎝7.0  0.0  9.0⎠

We can also access submatrices of m, for example its top-left and bottom-right 2x2 submatrices:

print(m[0 ... 1, 0 ... 1])
print(m[1 ... 2, 1 ... 2])

This will print

2x2-matrix:
⎛1.0  2.0⎞
⎝4.0  5.0⎠
2x2-matrix:
⎛5.0  6.0⎞
⎝0.0  9.0⎠

Finally, using the same notation, we can overwrite submatrices of m:

m[0 ... 1, 0 ... 1] = m[1 ... 2, 1 ... 2]
print(m)

This overwrites the top-left 2x2 submatrix of m with its bottom-right 2x2 submatrix, yielding:

3x3-matrix:
⎛5.0  6.0  3.0⎞
⎜0.0  9.0  6.0⎟
⎝7.0  0.0  9.0⎠

Matrix Arithmetic

LANumerics supports common operations on matrices and vectors, among them:

  • transpose and adjoint
  • matrix multiplication
  • vector products
  • element-wise operations
  • functional operations

In the following, assume the context

import Numerics
import LANumerics
let u = Matrix<Complex<Float>>(rows: [[1, 2 * .i], [3, 4 * .i + 1]])
let v = Matrix<Complex<Float>>(rows: [[.i, 0], [0, 1 + 1 * .i]])
print("u : \(u)\n")
print("v : \(v)\n")

which has output

u : 2x2-matrix:
⎛1.0  2.0i      ⎞
⎝3.0  1.0 + 4.0i⎠

v : 2x2-matrix:
⎛1.0i  0.0       ⎞
⎝0.0   1.0 + 1.0i⎠

Transpose and Adjoint

For real matrices, transpose and adjoint have the same meaning, but for complex matrices the adjoint is the element-wise conjugate of the transpose. Executing

print("u.transpose : \(u.transpose)\n")
print("u.adjoint : \(u.adjoint)\n")

thus yields

u.transpose : 2x2-matrix:
⎛1.0   3.0       ⎞
⎝2.0i  1.0 + 4.0i⎠

u.adjoint : 2x2-matrix:
⎛1.0    3.0       ⎞
⎝-2.0i  1.0 - 4.0i⎠

The adjoint has the advantage over the transpose that many properties involving the adjoint generalize naturally from real matrices to complex matrices. Therefore there is the shortcut notation

u′

for u.adjoint.

Note that is the unicode character "Prime" U+2032. You can use for example Ukelele to make the input of that character smooth. Other alternatives are configuring the touchbar of your macbook, or using a configurable keyboard like Stream Deck.

Matrix Multiplication

Multiplying u and v is done via the expression u * v. Running print("u * v: \(u * v)") results in

u * v: 2x2-matrix:
⎛1.0i  -2.0 + 2.0i⎞
⎝3.0i  -3.0 + 5.0i⎠

Instead of u′ * v one can also use the equivalent, but faster expression u ′* v:

print("u′ * v: \(u′ * v)\n")
print("u ′* v: \(u ′* v)\n")

yields

u′ * v: 2x2-matrix:
⎛1.0i  3.0 + 3.0i⎞
⎝2.0   5.0 - 3.0i⎠

u ′* v: 2x2-matrix:
⎛1.0i  3.0 + 3.0i⎞
⎝2.0   5.0 - 3.0i⎠

Similarly, it is better to use u *′ v than u * v′, and u ′*′ v instead of u′ * v′.

Vector Products

We will view u and v as vectors u.vector and v.vector now, where .vector corresponds to a column-major order of the matrix elements:

u.vector: [1.0, 3.0, 2.0i, 1.0 + 4.0i]
v.vector: [1.0i, 0.0, 0.0, 1.0 + 1.0i]

(Actually, in the above, we used u.vector.toString() and v.vector.toString() for better formatting of complex numbers. We will also do so below where appropriate without further mentioning it.)

The dot product of u.vector and v.vector results in

u.vector * v.vector: -3.0 + 6.0i

Another vector product is

u.vector ′* v.vector: 5.0 - 2.0i

which corresponds to

u.vector′ * v.vector: [5.0 - 2.0i]

Furthermore, there is

u.vector *′ v.vector: 4x4-matrix:
⎛-1.0i       0.0  0.0  1.0 - 1.0i⎞
⎜-3.0i       0.0  0.0  3.0 - 3.0i⎟
⎜2.0         0.0  0.0  2.0 + 2.0i⎟
⎝4.0 - 1.0i  0.0  0.0  5.0 + 3.0i⎠

which is equivalent to

Matrix(u.vector) * v.vector′: 4x4-matrix:
⎛-1.0i       0.0  0.0  1.0 - 1.0i⎞
⎜-3.0i       0.0  0.0  3.0 - 3.0i⎟
⎜2.0         0.0  0.0  2.0 + 2.0i⎟
⎝4.0 - 1.0i  0.0  0.0  5.0 + 3.0i⎠

Element-wise Operations

Element-wise operations like .+, .-, .* and ./ are supported on both vectors and matrices, for example:

u .* v : 2x2-matrix:
⎛1.0i  0.0        ⎞
⎝0.0   -3.0 + 5.0i⎠

Functional Operations

The Matrix type supports functional operations like map, reduce and combine. These come in handy when performance is not that important, and there is no accelerated equivalent available (yet?). For example, the expression

u.reduce(0) { x, y in max(x, y.magnitude) }

results in the value 4. In this case it is better though to use the equivalent expression u.infNorm instead.

Solving Linear Equations

You can solve a system of linear equations like

  1. 7x+5y-3z = 16
  2. 3x-5y+2z = -8
  3. 5x+3y-7z = 0

by converting it into matrix form A * u = b and solving it for u:

import LANumerics
let A = Matrix<Double>(rows: [[7, 5, -3], [3, -5, 2], [5, 3, -7]])
let b : Vector<Double> = [16, -8, 0]
let u = A.solve(b)!
print("A: \(A)\n")
print("b: \(b)\n")
print("u: \(u.toString(precision: 1))")

This results in the output:

A: 3x3-matrix:
⎛7.0  5.0   -3.0⎞
⎜3.0  -5.0  2.0 ⎟
⎝5.0  3.0   -7.0⎠

b: [16.0, -8.0, 0.0]

u: [1.0, 3.0, 2.0]

Therefore the solution is x=1, y=3, z=2.

This example is actually plugged from an article which describes how to use the Accelerate framework directly and without a nice library like LANumerics 😁.

You can solve for multiple right-hand sides simultaneously. For example, you can compute the inverse of A like so:

let Id : Matrix<Double> = .eye(3)
print("Id: \(Id)\n")
let U = A.solve(Id)!
print("U: \(U)\n")
print("A * U: \(A * U)")

This results in the output

Id: 3x3-matrix:
⎛1.0  0.0  0.0⎞
⎜0.0  1.0  0.0⎟
⎝0.0  0.0  1.0⎠

U: 3x3-matrix:
⎛0.11328125  0.1015625             -0.019531249999999997⎞
⎜0.12109375  -0.13281250000000003  -0.08984375          ⎟
⎝0.1328125   0.01562499999999999   -0.1953125           ⎠

A * U: 3x3-matrix:
⎛1.0  -1.0755285551056204e-16  0.0⎞
⎜0.0  1.0                      0.0⎟
⎝0.0  -4.163336342344337e-17   1.0⎠

Linear Least Squares

Extending the above example, you can also solve it using least squares approximation instead:

print(A.solveLeastSquares(b)!.toString(precision: 1))

results in the same solution [1.0, 3.0, 2.0].

Least squares is more general than solving linear equations directly, as it can also deal with situations where you have more equations than variables, or less equations than variables. In other words, it can also handle:

  • non-square matrices A
  • situations with large noise in the data
  • situations which are only approximately linear

There is a shorthand notation available for the expression A.solveLeastSquares(b)!:

A ∖ b

This also works for simultaneously solving for multiple right-hand sides as before, the inverse of A can therefore also be computed using the expression A ∖ .eye(3):

A ∖ .eye(3): 3x3-matrix:
⎛0.11328124999999997  0.10156250000000001   -0.01953124999999994⎞
⎜0.12109375           -0.13281250000000006  -0.08984374999999999⎟
⎝0.13281249999999997  0.015624999999999993  -0.19531249999999994⎠

Note that is the unicode character "Set Minus" U+2216. The same advice for smooth input of this character applies as for the input of earlier.

In addition, there is the operator ′∖ which combines taking the adjoint and solving via least squares. Therefore, to compute the inverse of A′, you could better write A ′∖ .eye(3) instead of A′ ∖ .eye(3):

A ′∖ .eye(3): 3x3-matrix:
⎛0.11328124999999996   0.12109375            0.13281249999999994 ⎞
⎜0.10156250000000001   -0.13281250000000003  0.01562499999999999 ⎟
⎝-0.01953124999999994  -0.08984374999999999  -0.19531249999999994⎠

The inverse of A can more concisely also be obtained via A.inverse!.

Matrix Decompositions

The following matrix decompositions are currently supported:

  • Singular value decomposition of a real or complex matrix A:
    A.svd()
    
  • Eigen decomposition for self-adjoint matrices A (that is for real or complex matrices for which A == A′):
    A.eigen()
    
  • Schur decomposition of a real or complex square-matrix A:
    A.schur()
    

GitHub

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Release Notes

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