GNU Free Documentation License, Version 1.2

- User documentation for MatrixOps
- Maintainer documentation for MatrixOps
- Bugs, Shortcomings and other ideas
- Main changes

`MatrixOps`

gathers together a number of operations on matrices; in
most cases these operations are happy to accept a `MatrixView`

(see `MatrixView`

) as argument.

When not specified, a matrix argument is of type `ConstMatrixView`

.

`M[i,j]`

read the`(i,j)`

-entry of matrix`M`

`SetEntry(M,i,j, val)`

set the`(i,j)`

-entry of matrix`M`

`GetRow(M,i)`

return the`i`

-th row of`M`

as a`vector<RingElem>`

`GetCol(M,j)`

return the`j`

-th col of`M`

as a`vector<RingElem>`

`GetRows(M)`

return the rows of`M`

as a`vector<vector<RingElem>>`

`GetCols(M)`

return the cols of`M`

as a`vector<vector<RingElem>>`

Note that`GetRow`

,`GetCol`

,`GetRows`

and`GetCols`

make copies of the matrix entries.

There are two ways of multiplying two matrices together. The infix
operators return a `DenseMatrix`

; the procedural version may be
slightly faster than the infix operator.

`mul(matrix& lhs, M1, M2)`

-- a procedure equivalent to`lhs = M1*M2;`

, note that`lhs`

might be a`SparseMatrix`

**(not yet implemented)**`operator*(M1, M2)`

-- the product`M1*M2`

`operator+(M1, M2)`

-- the sum`M1+M2`

`operator-(M1, M2)`

-- the difference`M1-M2`

`power(M, n)`

compute`n`

-th power of`M`

; if`n`

is negative then`M`

must be invertible`operator*(n, M1)`

-- scalar multiple of`M1`

by`n`

(integer or RingElem)`operator*(M1, n)`

-- scalar multiple of`M1`

by`n`

(integer or RingElem)`operator/(M1, n)`

-- scalar multiple of`M1`

by`1/n`

(where`n`

is integer or RingElem)`operator-(M1)`

-- scalar multiple of`M1`

by -1

Here are some matrix norms. The result is an element of the ring
containing the matrix elements. Note that `FrobeniusNorm2`

gives the
**square** of the Frobenius norm (so that the value surely lies in the
same ring).

`FrobeniusNorm2(M)`

-- the**square**of the Frobenius norm`OperatorNormInfinity(M)`

-- the infinity norm, ring must be ordered`OperatorNorm1(M)`

-- the one norm, ring must be ordered

Here are some fairly standard functions on matrices.

`det(M)`

-- determinant of`M`

(M must be square)`rk(M)`

-- rank of`M`

(the base ring must be an integral domain)`inverse(M)`

-- inverse of`M`

as a`DenseMatrix`

`adj(M)`

-- classical adjoint of`M`

as a`DenseMatrix`

; sometimes called "adjugate".`RowReducedForm(M)`

-- compute a row reduced form of`M`

(orig. matrix is unchanged)`PseudoInverse(M)`

-- PseudoInverse of`M`

as a`DenseMatrix`

. I suspect that it requires that the matrix be of full rank.`LinSolve(M,rhs)`

-- solve for`x`

the linear system`M*x = rhs`

; result is a`DenseMatrix`

; if no soln exists, result is the 0-by-0 matrix`LinKer(M)`

-- solve for`x`

the linear system`M*x = 0`

; returns a`DenseMatrix`

whose columns are a base for`ker(M)`

Here are some standard operations where the method used is specified explicitly. It would usually be better to use the generic operations above, as those should automatically select the most appropriate method for the given matrix.

`det2x2(M)`

-- only for 2x2 matrices`det3x3(M)`

-- only for 3x3 matrices`det4x4(M)`

-- only for 4x4 matrices`det5x5(M)`

-- only for 5x5 matrices`DetByGauss(M)`

-- matrix must be over an integral domain`RankByGauss(std::vector<long>& IndepRows, M)`

`InverseByGauss(M)`

-- some restrictions (needs gcd)`AdjointByDetOfMinors(M)`

`AdjointByInverse(M)`

-- base ring must be integral domain`LinSolveByGauss(M,rhs)`

-- solve a linear system using gaussian elimination (base ring must be a field), result is a`DenseMatrix`

`LinSolveByHNF(M,rhs)`

-- solve a linear system using Hermite NormalForm (base ring must be a PID), result is a`DenseMatrix`

`LinSolveByModuleRepr(M,rhs)`

-- solve a linear system using module element representation, result is a`DenseMatrix`

`void GrammSchmidtRows(MatrixView& M)`

-- NYI`void GrammSchmidtRows(MatrixView& M, long row)`

-- NYI

Most impls are quite straightforward.

`power`

is slightly clever with its iterative impl of binary powering.

`LinSolveByGauss`

is a little complicated because it tries to handle all
cases (*e.g.* full rank or not, square or more rows than cols or more cols than rows)

Can we make a common "gaussian elimination" impl which is called by the various algorithms needing it, rather than having several separate implementations?

Is the procedure `mul`

really any faster than the infix operator?

**2012**

- June: Added negation, multiplication and division of a matrix by a scalar.
- April: Added LinSolve family (incl. LinSolveByGauss, LinSolveByHNF, LinSolveByModuleRepr)

**2011**

- May: Added power fn for matrices: cannot yet handle negative powers.
- March: added multiplication by RingElem