Swivl - Swift Vector Library
A set of BLAS-accelerated linerar algebra structures and functions. Includes dense and sparse matrices and vectors, and various solvers. Features:
- BLAS accelerated operations
- Sparse and Dense support
- Cholesky, LU, Eigen- decompositions and more
- Matlab-style
solve
function chooses an appropriate solver
Examples
Basic Operations
import Foundation
import Swivl
let v = Vector<Double>([1,2,3,4])
print(Vector.add(v, v))
print(v + v)
print(v + 2)
let v2 = VectorXf([5,6,7,8])
print(v2 + v2)
>>> Vector<Double> 4x1 [2.0, 4.0, 6.0, 8.0]
>>> Vector<Double> 4x1 [2.0, 4.0, 6.0, 8.0]
>>> Vector<Double> 4x1 [3.0, 4.0, 5.0, 6.0]
>>> Vector<Float> 4x1 [10.0, 12.0, 14.0, 16.0]
Matrix Mutliplication
let A = MatrixXd(rows: [
[1,2,3],
[4,5,6],
[7,8,9]
])
let B = MatrixXd(rows: [
[0,1,0],
[1,0,0],
[0,0,1]
])
let x = VectorXd([10,20,30])
print(A*B)
print(A.*B)
print(A*x)
>>> Matrix<Double> 3x3
[ 2, 1, 3 ]
[ 5, 4, 6 ]
[ 8, 7, 9 ]
>>> Matrix<Double> 3x3
[ 0, 2, 0 ]
[ 4, 0, 0 ]
[ 0, 0, 9 ]
>>> Vector<Double> 3x1 [140.0, 320.0, 500.0]
Dense Matrix Creation
let A = MatrixXd(rows: [
[10, -7, 0],
[-3, 2, 6],
[ 5, -1, 5]
])
let B = MatrixXd(flat: [
1.0000, 0.5000, 0.3333, 0.2500,
0.5000, 1.0000, 0.6667, 0.5000,
0.3333, 0.6667, 1.0000, 0.7500,
0.2500, 0.5000, 0.7500, 1.0000
], shape: (4,4))
Matrix Properties
let M = MatrixXd(rows: [
[1,2,3,4],
[5,6,7,8],
[9,0,1,2],
[3,4,5,6]
])
print(M.det)
print(M.trace)
print(M.T)
print(M.diag())
>>> 1.5987211554602252e-13
>>> 14.0
>>> Matrix<Double> 4x4
[ 1, 5, 9, 3 ]
[ 2, 6, 0, 4 ]
[ 3, 7, 1, 5 ]
[ 4, 8, 2, 6 ]
>>> Vector<Double> 4x1 [1.0, 6.0, 1.0, 6.0]
Slicing
let M = MatrixXd(rows: [
[1,2,3,4,5],
[6,7,8,9,0]
])
print(M[0, ...])
print(M[..., [0,2,3]])
print(M[..., 3...])
print(M[...0, 2...4])
>>> Vector<Double> 5x1 [1.0, 2.0, 3.0, 4.0, 5.0]
>>> Matrix<Double> 2x3
[ 1, 3, 4 ]
[ 6, 8, 9 ]
>>> Matrix<Double> 2x2
[ 4, 5 ]
[ 9, 0 ]
>>> Matrix<Double> 1x3
[ 3, 4, 5 ]
Decomposition
var PSD = MatrixXd([
[1, 0, 1],
[0, 2, 0],
[1, 0, 3]
])
print(PSD.isDefinite())
print(try? PSD.chol())
var nonPSD = MatrixXd([
[1, 1, 1, 1, 1],
[1, 2, 3, 4, 5],
[1, 3, 6, 10, 15],
[1, 4, 10, 20, 35],
[1, 5, 15, 35, 69]])
print(nonPSD.isDefinite())
print(try? nonPSD.chol())
>>> Optional(Matrix<Double> 3x3
[ 1.000, 0.000, 1.000 ]
[ 0.000, 1.414, 0.000 ]
[ 0.000, 0.000, 1.414 ])
>>> true
>>> nil
>>> false
Sparse Matrix Creation
let rows: [Int] = [0, 0, 1, 1, 2, 2, 2]
let cols: [Int] = [1, 3, 1, 4, 0, 1, 3]
let vals: [Double] = [1, 2, 5, 7, 8, 9, 4]
let A = SparseMatrix(rows, cols, vals)
let M = MatrixXd([
[0, 1, 0, 2, 0],
[0, 5, 0, 0, 7],
[8, 9, 0, 4, 0]
])
let B = SparseMatrix(M)
let C = SparseMatrix<Double>(.eye(4))
let D = SparseMatrix<Double>.eye(4)