numidr/docs/VectorsMatrices.md

Working with Vectors and Matrices

As linear algebra is one of the main concerns of NumIdr, most of its provided functions are dedicated to vectors (rank-1 arrays) and matrices (rank-2 arrays).

The Generalized Multiplication Operator

A linear algebra library wouldn't be very useful without matrix multiplication! While Idris's standard (*) operator would be a natural choice for this, the Num interface only allows for homogeneous multiplication, in which the inputs and output are all of the same type. To get around this, (*) is used for element-wise multiplication (a.k.a. the Hadamard product), and NumIdr defines a new interface Mult:

interface Mult a b c where
  (*.) : a -> b -> c

-- Synonym for homogeneous cases:
Mult' : Type -> Type
Mult' a = Mult a a a

The generalized multiplication operator (*.) covers matrix multiplication, scalar-vector multiplication, and any other operation that's vaguely multiplication-like.

Vectors

Algebraic Operations

Vectors can be added together with (+), which performs element-wise addition. Scalar-vector multiplication is done with the generalized multiplication operator (*.).

2 *. (vector [1, 1] + vector [2, 3])
  == vector [6, 8]

A few other basic linear algebra operations are available:

• dot, The dot product
• cross, The cross product
• perp, The perpendicular product (sometimes called the 2D cross product)
• triple, The scalar triple product

Indexing

NumIdr provides special versions of index and indexNB and their infix forms (!!) and (!?) for use with vectors. These take a single numeric index instead of a list.

Vector.index 2 v == index [2] v

v !! 2 == v !! [2]

For convenience, when working with two- or three-dimensional vectors, there are postfix accessors (.x), (.y), and (.z):

v = vector [5, 6, 2]

v.x == 5
v.y == 6
v.z == 2

Other Operations

• toVect - Convert a vector into a Vect
• dim - Returns the vector's length
• (++) - Concatenate two vectors

Matrices

Arithmetic Operations

Like vectors, matrices can be added together using (+). Matrix multiplication, as well as matrix-vector and matrix-scalar multiplication, are performed using (*.).

For the purposes of working with matrices and matrix-like objects, the sub-interfaces MultMonoid and MultGroup are defined:

interface Mult' a => MultMonoid a where
  identity : a

interface MultMonoid a => MultGroup a where
  inverse : a -> a

The identity function returns an identity matrix, and inverse calculates a matrix's inverse. Note that inverse cannot tell you if an inverse of your matrix does not exist; if you want to handle that case, use tryInverse instead.

tryInverse : FieldCmp a => Matrix' n a -> Maybe (Matrix' n a)

LU and LUP Decomposition

The functions decompLU and decompLUP compute LU and LUP decomposition on a matrix.

decompLU : Field a => (mat : Matrix m n a) -> Maybe (DecompLU mat)

decompLUP : FieldCmp a => (mat : Matrix m n a) -> DecompLUP mat

DecompLU and DecompLUP are record types holding the results of the corresponding decomposition. The accessors lower, upper and permute can be applied to get each component of the decomposition; lower and upper return matrices, and permute returns a Permutation value.

Other Algebraic Operations

• trace - The sum of the matrix's diagonal
• outer - The matrix-valued outer product (or tensor product) of two vectors
• det - Determinant of the matrix
• solve - Apply an inverse matrix to a vector, useful for solving linear equations

The det and solve operations require computing an LUP decomposition, which can be expensive. To avoid duplicating work, the variants detWithLUP and solveWithLUP allow a pre-computed LUP decomposition to be passed in.

det m == detWithLUP m (decompLUP m)

Indexing

Aside from the usual array indexing functions, there are a few functions specialized to matrix indexing:

• getRow and getColumn - Returns a specific row or column of the matrix
• diagonal - Returns the diagonal elements of the matrix as a vector
• minor - Removes a single row and column from the matrix

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