linalg

Linear algebra library in Crystal, uses LAPACK.

• direct access to LAPACK methods
• convenient Matrix(T) class, supports T=Float32, Float64 and Complex.
• high-level interface similar to scipy.linalg or MATLAB.

Killing SciPy, one module at a time.

Installation

1. Install LAPACK and BLAS. sudo apt install libopenblas-base liblapack3 for Ubuntu, sudo pacman -S lapack (for better performance use openblas-lapack package from AUR) for Arch. For Windows you need libopenblas.dll (and libopenblas.lib) from https://github.com/xianyi/OpenBLAS/releases

2. (for Ubuntu 18) it seems package doesn't create symlink, so use

• sudo ln -s /usr/lib/lapack/liblapack.so.3 /usr/lib/liblapack.so
• sudo ln -s /usr/lib/openblas-base/libblas.so.3 /usr/lib/libcblas.so

3. Add this to your application's shard.yml:

dependencies:
  linalg:
    github: konovod/linalg

4. Run shards install

Usage

require "linalg"

Basic type aliases are

• Mat = Matrix(Float64)
• Mat32 = Matrix(Float32)
• MatComplex = Matrix(Complex)

Complex consisting of two Float32 isn't supported for now (it is easy, but I'm not sure if it's useful).

Types with prefix G (GMat, GMat32, GMatComplex) are for actually allocated matrices, others are automatically converted to them when needed.

#suggested to don't prefix LA:: everywhere
include LA

# create matrix from array of arrays (or tuple... everything Indexable)
m = GMat[
  [1, 2, 3],
  [4, 5, 6],
  [7, 8, 9],
  [10, 11, 12],
]

# or using block
m = GMat32.new(3, 4) { |i, j| i*3 + j + 1 }
# or using one of other ways, check "spec" directory

# do basic arithmetics
a = Mat.eye(3)
pp 2 * a - Mat.diag([2, 2, 2]) == Mat.zeros(3, 3) # => true

# basic algebra
a = Mat.rand(5, 5) + 2 * Mat.identity(5)
pp (a.inv * a - Mat.identity(5)).norm < 1e-6

b = Mat.rand(5, 1)
x = LA.solve(a, b) # or a.solve(b)
pp (a*x - b).norm < 1e-6

m = GMat[[-2, 4, 1], [2, -4, 1], [1, 1, 1]]
pp m.eigvals # => [-6.0, -1.0, 2.0]

# extract submatrices (memory isn't copied as they reference to basic matrix)
m = GMat[
  [1, 2, 3],
  [4, 5, 6],
  [7, 8, 9],
]
pp m.columns[2] # LA::SubMatrix(Float64) (3x1, None):
# [3.0]
# [6.0]
# [9.0]

x = m[1..1, 1..2]
pp x        # => [5.0, 6.0]
x[0, 0] = 0 # m[1,1] is now 0 (questionable feature? maybe should be ##[]! for modifiable submatrices and ##[] for CoW?)
y = x.clone # now y is a separate matrix
y[0, 0] = 1 # m[1,1] is still 0
pp m[1, 1]

other present features:

• svd (Mat#svd or Mat#svdvals for just values)
• lu decomposition (Mat#lu)

# to just get P L U matrices
p, l, u = a.lu

# to get them in compact form and use for solving linear equations:
a = GMat32[
  [2, 4],
  [2, 8]
  ]

lu = a.lu_factor # lu is LUMatrix(T) - immutable object that can return it's content and solve systems
puts lu.solve(GMat32[[2], [4]])

• matrix rank determination (using SVD or QRP)
• linear least squares problem (LA.solvels to just get decision or LA.lstsq to also get rank and singular values (TODO - and residues))
• cholesky decomposition (#cholesky, #cholesky!, #cho_solve)
• hessenberg form
• qr, rq, lq, ql decompositions
• schur and qz (generalized schur) decomposition
• generalized eigenproblem (eigs(a, b, ...))
• creating special matrices like pascal or toeplitz (check scipy.md for a full list)
• matrix exponent and trigonomertic functions
• matrix exponentiation (to integer powers only atm (TODO - fractional))

There is also concept of Mat#flags that represent properties of matrix (symmetric, positive definite etc), they are used to automatically select faster algorithms from LAPACK. Flags are partially enforced by runtime checks, with the possibility of user override. For example, if we say that a.assume!(MatrixFlags::Symmetric) then a.transpose or a + Mat.diag(*a.size) will also have this flag, so the LAPACK routines for symmetrical matrices will be used. In fact, a.transpose will return matrix clone as for symmetric matrices A=A'.

Supported flags:

enum MatrixFlags
  Symmetric
  Hermitian
  PositiveDefinite
  Orthogonal
  UpperTriangular
  LowerTriangular
  Triangular      = UpperTriangular | LowerTriangular

NOTE for complex matrices Orthogonal flag means Unitary.

Main functions for flags are:

  a.assume!(flag) # sets matrix flag without check, can lead to incorrect results if matrix do not have corresponding property.
  a.detect?(flag) # checks if matrix has property, if yes sets the flag. Returns true if check positive
  a.detect(flag) # same as `detect?`, but returns matrix a
  a.detect # detect all possible flags
  a.flags # returns matrix flags

Most operations - matrix addition, multiplication, inversion, transposition and decompositions correctly update flags, but any direct access like a[i,j] = 0 or a.map!{|v| v+1} resets flags to None, so use a.detect after them if you need to preserve flags (or a.assume!(f) if detection is too slow).

Development

Roadmap:

Important

• saving/loading from files

• ways to evade allocations during calculations

• Matrix exponent and trigonometric

• other matrix functions (fractional power and logarithm)

• Banded matrices

  • mostly done, but some LAPACK routines could be added

• Column-major storage (optional?)

• Other missing features from LAPACK (selectable and orderable eigenvalues, generalized decompositions, new routines etc)

• Sparse matrices

  • COO Matrix
  • CSR Matrix
  • CSC Matrix
    • non-sorted mode
  • sparse-dense multiplication
  • Separate shard for SparseSuite or other libs

• Other missing features from scipy.linalg (lyapunov/ricatti/sylvester equations, other things i don't know algorithms for)

• Support multithreading\reenterability

• Allow matrices of unsupported types (use check_type only when actually needed)

  • type still has to support T.new(value), maybe this requirement can be relaxed
  • also map signature has to be changed?

Not so important

• saving/loading to matlab-like string
• better pretty-print, with alignment and various precision
• use blas for multiplication
• more flags support (inversion of diagonal matrix and other trivial cases)

Contributing

1. Fork it ( https://github.com/konovod/linalg/fork )
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

Contributors

• konovod - creator and maintainer
• RainbowZephyr - pseudoinverse implementation
• jtanderson - arange, open-ended ranges in submatrices
• MatthiasWinkelmann - GMat.max fix