Linear Algebra with Maple
The output here is in character mode, rather than the default "high-resolution" mode. It's still formatted in two dimensions, so you need a monospaced font (for preformatted material) and a "sufficiently" wide window.
BlockDiagonal, GramSchmidt, JordanBlock, Wronskian, add,
addcol, addrow, adj, adjoint, angle, augment, backsub, band,
basis, bezout, blockmatrix, charmat, charpoly, col, coldim
colspace, colspan, companion, concat, cond, copyinto,
crossprod, curl, definite, delcols, delrows, det, diag,
diverge, dotprod, eigenvals, eigenvects, entermatrix, equal,
exponential, extend, ffgausselim, fibonacci, frobenius,
gausselim, gaussjord, genmatrix, grad, hadamard, hermite,
hessian, hilbert, htranspose, ihermite, indexfunc,
innerprod, intbasis, inverse, ismith, iszero, jacobian,
jordan, kernel, laplacian, leastsqrs, linsolve, matrix,
minor, minpoly, mulcol, mulrow, multiply, norm, normalize,
nullspace, orthog, permanent, pivot, potential, randmatrix,
randvector, rank, ratform, row, rowdim, rowspace, rowspan,
rref, scalarmul, singularvals, smith, stack, submatrix,
subvector, sumbasis, swapcol, swaprow, sylvester, toeplitz,
trace, transpose, vandermonde, vecpotent, vectdim, vector
This is a list of the functions added by loading the "linalg" package. To find out more about vector, for example, type ?vector
v := [ a, b, c ]
[ a b ]
m := [ ]
[ c d ]
[2 a + 3 b, 2 c + 3 d]
- 2 d + 3 b - 2 c + 3 a
[ -----------, - ----------- ]
b c - a d b c - a d
a d - b c
2 2 1/2
1/2 a + 1/2 d + 1/2 (a - 2 a d + d + 4 b c) ,
2 2 1/2
1/2 a + 1/2 d - 1/2 (a - 2 a d + d + 4 b c)
- 1/2 a + 1/2 d - 1/2 %1
[1/2 a + 1/2 d + 1/2 %1, 1, {[ - ------------------------, 1 ]}]
c
,
- 1/2 a + 1/2 d + 1/2 %1
[1/2 a + 1/2 d - 1/2 %1, 1, {[ - ------------------------, 1 ]}]
c
2 2 1/2
%1 := (a - 2 a d + d + 4 b c)
A common error is trying to compute the determinant, or eigenvalues, of a large symbolic n by n matrix. The problem is called combinatorial explosion -- there might be n2 expressions in the answer which are each n! terms long!
The Hilbert matrix is a favorite example of numeric (as opposed to symbolic) linear algebra.
[ 1 1/2 1/3 1/4 1/5 ]
[ ]
[ 1/2 1/3 1/4 1/5 1/6 ]
[ ]
H5 := [ 1/3 1/4 1/5 1/6 1/7 ]
[ ]
[ 1/4 1/5 1/6 1/7 1/8 ]
[ ]
[ 1/5 1/6 1/7 1/8 1/9 ]
The Hilbert matrix is badly conditioned, which means that it corresponds to a system of linear equations which are hard to solve accurately.
943656
This means that errors [for example in the measured values of the right-hand side of the equations] may be multiplied by a factor of nearly a million!
A curious feature of the Hilbert matrix is that the entries of its inverse are all integers. Maple computes them exactly:
[ 25 -300 1050 -1400 630 ]
[ ]
[ -300 4800 -18900 26880 -12600 ]
[ ]
[ 1050 -18900 79380 -117600 56700 ]
[ ]
[ -1400 26880 -117600 179200 -88200 ]
[ ]
[ 630 -12600 56700 -88200 44100 ]
The method for finding eigenvectors of m that Maple used above would not be appropriate for this matrix; if it were symbolic, this matrix would be too big; but more importantly, since it's numeric, there is another method [the QR method] which is less sensative to the numeric instability implied by a big condition number, and is also computed more quickly. Maple uses that method, if the inputs are floating-point [decimal] numbers.
eigsys :=
[.01140749151, 1,
{ [ .2142136245, -.7241021311, -.1204532771, .3095739659, .5651934124 ] }],
[1.567050691, 1,
{ [ .7678547356, .4457910609, .3215782948, .2534389442, .2098226368 ] }],
[.2085342186, 1,
{ [-.6018714786, .2759134174, .4248766220, .4439030401, .4290133540 ] }],
-5
[.3288052655*10 , 1,
{ [ .006173874786, -.1166928633, .5061638445, -.7671911293, .3762453886 ] }],
[.0003058977746, 1,
{ [ .04716180523, -.4326673028, .6673503032, .2330247284, -.5576000510 ] }]
"eigsys" is a list of compound objects: the eigenvalues, each with its multiplicity and corresponding eigenvector.
To select the second component of the second eigenvector, do this:
.4457910609
To sort this out, examine in turn
- eigsys[2](the "system:" second eigenvector, its eigenvalue and multiplicity),
- eigsys[2][3](the set consisting of the second eigenvector system's vector),
- eigsys[2][3][1](the vector itself).
CAUTION: The order of the eigensystem cannot be predicted, from one session to the next.
The next command finds the singular values to 4 floating-point digits, among other things.
svdlist := [ 1.567, .2086, .01136, .0002933, .00001142
[ 1.567 0 0 0 0 ]
[ ]
[ 0 .2086 0 0 0 ]
[ ]
d := [ 0 0 .01136 0 0 ]
[ ]
[ 0 0 0 .0002933 0 ]
[ ]
[ 0 0 0 0 .00001142 ]
This next command checks the accuracy of the result just computed, carrying 20 digits:
[ ]
[ -18 -19 -19 -19 -19 ]
[ -.1*10 -.5*10 .4*10 -.2*10 -.3*10 ]
[ ]
[ -20 -19 -20 -20 -20 ]
[ -.9*10 -.1*10 .2*10 .4*10 .5*10 ]
[ ]
[ -19 -20 -21 -20 ]
[ -.108*10 .61*10 0 .9*10 -.49*10 ]
[ ]
[ -19 -20 -20 -20 -20 ]
[ -.27040*10 -.3301*10 .6147*10 -.973*10 -.5038*10 ]
[ ]
[ -20 -20 -20 -20 -21 ]
[ -.395257*10 -.619981*10 .750459*10 -.182306*10 .7701*10 ]
[ ]
