mathjs
Version:
Math.js is an extensive math library for JavaScript and Node.js. It features a flexible expression parser with support for symbolic computation, comes with a large set of built-in functions and constants, and offers an integrated solution to work with dif
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JavaScript
// test lup
var assert = require('assert'),
approx = require('../../../../tools/approx'),
math = require('../../../../index');
/**
* Tests whether `Q` and `R` are the valid QR decomposition of `A`.
*
* Given a real matrix `A`, `Q` and `R` should be the solutions to the equation
* `A = Q*R` where Q is [orthoganal](https://en.wikipedia.org/wiki/Orthogonal_matrix) and
* R is [upper triangular](https://en.wikipedia.org/wiki/Triangular_matrix).
*
* If `A` is a complex matrix then `Q` should be a [unitary](https://en.wikipedia.org/wiki/Unitary_matrix)
*
* Syntax:
*
* math.isValidQRDecomposition(A);
*
* Example:
*
* var m = [
* [1, -1, 4],
* [1, 4, -2],
* [1, 4, 2],
* [1, -1, 0]
* ];
* var result = math.qr(m);
* // r = {
* // Q: [
* // [0.5, -0.5, 0.5],
* // [0.5, 0.5, -0.5],
* // [0.5, 0.5, 0.5],
* // [0.5, -0.5, -0.5],
* // ],
* // R: [
* // [2, 3, 2],
* // [0, 5, -2],
* // [0, 0, 4],
* // [0, 0, 0]
* // ]
* // }
*
* isValidQRDecomposition(m, r.Q, r.R);
* // true
*
* r.Q[2][1] = 9;
*
* isValidQRDecomposition(m, r.Q, r.R);
* // false
*
*
* @param {Matrix | Array} A A two dimensional matrix or array from which the QR decomposition was formed.
* @param {Matrix | Array} Q A two dimensional matrix or array equal to `Q` is an QR decomposition.
* @param {Matrix | Array} R A two dimensional matrix or array equal to `R` is an QR decomposition.
*
* @return {Boolean} Returns true if `Q` and `R` form a valid QR decomposition of `A`
*/
function assertValidQRDecomposition(A, Q, R) {
var Asize = math.size(A).valueOf();
var rows = Asize[0];
var cols = Asize[1];
// sizes match
assert.deepEqual(math.size(Q).valueOf(), [rows, rows]);
assert.deepEqual(math.size(R).valueOf(), [rows, cols]);
// A = Q * R
approx.deepEqual(math.multiply(Q, R).valueOf(), A.valueOf());
// Q has unitary (orthonormal for real A) columns
// use math.equal as approx.deepEqual cannot handle complex vs real number comparision
assert(math.equal(math.multiply(math.conj(math.transpose(Q)), Q).valueOf(), math.eye([Asize[0], Asize[0]]).valueOf()),
'Matrix Q is not unitary/orthonormal');
// R is upper triangular
for (var i = 0; i < rows; i++) {
for (var j = 0; j < i && j < cols; j++) {
assert(math.isZero(math.subset(R, math.index(i, j))), 'R is not an upper triangular matrix');
}
}
// All elements on leading diagonal of R are positive
for (var i = 0; i < Math.min(rows, cols); i++) {
var diagonalElement = math.subset(R, math.index(i, i))
assert(!math.isNegative(math.re(diagonalElement)),
'R has elements on the leading diagonal with a negative real part (R[' + i + '][' + i + '] = ' + diagonalElement + ')');
}
}
describe('qr', function () {
it('should decompose matrix, n x n, no permutations, array', function () {
var m = [[15, 42], [20, 81]];
var r = math.qr(m);
// L
approx.deepEqual(r.Q.valueOf(), [[0.6, -0.8], [0.8, 0.6]]);
// U
approx.deepEqual(r.R.valueOf(), [[25, 90], [0, 15]]);
// verify
assertValidQRDecomposition(m, r.Q, r.R);
var m2 = [
[7.507, 9.868, 5.057],
[4.482, 2.536, 9.744],
[6.527, 1.094, 3.321],
];
var r2 = math.qr(m2);
assertValidQRDecomposition(m2, r2.Q, r2. R);
});
it('should throw a helpfull error for sparse matricies', function () {
var m = math.matrix([[15, 42], [20, 81]], 'sparse');
var r = assert.throws(math.qr.bind(null, m));
});
it('should decompose matrix, n x n, dense format', function () {
var m = math.matrix([[15, 42], [20, 81]], 'dense');
var r = math.qr(m);
// Q
approx.deepEqual(r.Q.valueOf(), [[0.6, -0.8], [0.8, 0.6]]);
// R
approx.deepEqual(r.R.valueOf(), [[25, 90], [0, 15]]);
// verify
assertValidQRDecomposition(m, r.Q, r.R);
});
it('should decompose matrix, n x n, with a column of zeros dense format', function () {
var m = math.matrix([[5, 0, 15], [223, 0, 34.5], [1, 0, 19]], 'dense');
var r = math.qr(m);
// Q
approx.deepEqual(
r.Q.valueOf(),
[
[ 0.02241566559605479, 0.9997386855840484, -0.004483133119210979],
[ 0.9997386855840484, -0.02243532343507404, -0.004383698101188009 ],
[ 0.004483133119210979, 0.004383698101188009, 0.9999803421609812 ]
]);
// R
approx.deepEqual(
r.R.valueOf(),
[
[ 223.0582883463423, -0, 34.912399165855504 ],
[ -0, -0, 14.305351889173245 ],
[ -0, -0, 18.781141919779493 ]
]);
// verify
assertValidQRDecomposition(m, r.Q, r.R);
});
it('should decompose matrix, m x n, m < n, dense format', function () {
var m = math.matrix(
[
[15, 42, -11, 9],
[20, 81, 52, 112]
],
'dense'
);
var r = math.qr(m);
// Q
approx.deepEqual(
r.Q,
math.matrix(
[
[0.6, -0.8],
[0.8, 0.6],
]
));
// R
approx.deepEqual(
r.R,
math.matrix(
[
[25, 90, 35, 95],
[0 , 15, 40, 60],
]
));
// verify
assertValidQRDecomposition(m, r.Q, r.R);
var m2 = math.matrix([
[7.865, 9.293, 0.534, 7.023, 9.526, 6.005, 5.007, 5.581],
[3.842, 7.807, 8.208, 2.108, 3.947, 1.154, 6.086, 6.21 ],
[3.003, 4.084, 5.593, 4.738, 9.48 , 0.927, 7.294, 5.225]
]);
var r2 = math.qr(m2);
assertValidQRDecomposition(m, r.Q, r.R);
});
it('should decompose matrix, m x n, m > n, dense format', function () {
var m = math.matrix(
[
[8, 4],
[2, -12],
[9, -2],
[1, 94],
],
'dense'
);
var r = math.qr(m);
// Q
assert.deepEqual(
r.Q,
math.matrix(
[
[ 0.6531972647421809 , -0.0050729188524001045, -0.7248169493126636 , -0.21897029208715485 ],
[ 0.16329931618554522, -0.13865978196560358 , -0.14374377465457616, 0.9661493287513265 ],
[ 0.7348469228349535 , -0.07440280983520192 , 0.6732450861047025 , -0.034717084043718795 ],
[ 0.08164965809277261, 0.9875282032672256 , 0.026817368868139818, 0.13191743558805435 ]
]
));
// R
assert.deepEqual(
r.R,
math.matrix(
[
[ 12.24744871391589, 6.858571279792898],
[ -0 , 94.62008243496727],
[ -0 , -0 ],
[ -0 , -0 ],
]
));
// verify
assertValidQRDecomposition(m, r.Q, r.R);
});
/*
it('should decompose matrix, n x n, dense format', function () {
var m = math.matrix(
[
[16, -120, 240, -140],
[-120, 1200, -2700, 1680],
[240, -2700, 6480, -4200],
[-140, 1680, -4200, 2800]
]
);
var r = math.lup(m);
// L
approx.deepEqual(
r.L.valueOf(),
[
[1, 0, 0, 0],
[-0.5, 1, 0, 0],
[-0.5833333333333334, -0.7, 1, 0],
[0.06666666666666667, -0.4, -0.5714285714285776, 1]
]);
// U
approx.deepEqual(
r.U.valueOf(),
[
[240, -2700, 6480, -4200],
[0, -150, 540, -420],
[0, 0, -42, 56],
[0, 0, 0, 4]
]);
// P
assert.deepEqual(r.p, [3, 1, 0, 2]);
// verify
approx.deepEqual(math.multiply(_p(r.p), m).valueOf(), math.multiply(r.L, r.U).valueOf());
});
/*
it('should decompose matrix, 3 x 3, zero pivote value, dense format', function () {
var m = math.matrix(
[
[1, 2, 3],
[2, 4, 6],
[4, 8, 9]
]);
var r = math.lup(m);
// L
approx.deepEqual(
r.L.valueOf(),
[
[1, 0, 0],
[0.5, 1, 0],
[0.25, 0, 1.0]
]);
// U
approx.deepEqual(
r.U.valueOf(),
[
[4, 8, 9],
[0, 0, 1.5],
[0, 0, 0.75]
]);
// P
assert.deepEqual(r.p, [2, 1, 0]);
// verify
approx.deepEqual(math.multiply(_p(r.p), m).valueOf(), math.multiply(r.L, r.U).valueOf());
});
*/
it('should decompose matrix, n x n, complex numbers, dense format', function () {
var m = math.matrix(
[
[math.complex(24, 3) , math.complex(10) ],
[math.complex(12, 53) , math.complex(1.46, 10.6)],
[math.complex(0.345345, 234), math.complex(1) ],
]);
var r = math.qr(m);
// Q
assert.deepEqual(
r.Q,
math.eval('[\
[0.09940285751055641 + 0.012425357188819552i, 0.6771044400000075 + 0.0032268934486674216i, 0.7225638487314755 + 0.09687792016125076i],\
[0.049701428755278255 + 0.2195146436691456i, 0.07692808877592644 + 0.6944571280351147i, 0.00524374167953522 - 0.6790632951693036i],\
[0.0014303449927909801 + 0.969177860727926i, 0.009498908256891047 - 0.23073860039312136i, -0.03522342137225792 + 0.07823687113774894i]\
]'));
// R
assert.deepEqual(
r.R,
math.eval('[\
[241.44175128417413 + 0i, 3.3948782289740067 - 0.8870876675671249i],\
[0 + 0i, 14.254103875042043 - 4.440892098500626e-16i],\
[0 + 0i, 0 + 0i]\
]'));
// verify
assertValidQRDecomposition(m, r.Q, r.R);
});
it('should decompose matrix, m x n, n > m, complex numbers, dense format', function () {
var m = math.eval('[\
[-0.3264527816002377 + 2.493709974375747i, 27.144413452851555 - 95.38310595714056i, 24.851291758133694 - 31.358002980198492i, 17.60452153083572 - 58.02180107190187i, 29.062500250928192 - 57.24316264710557i, 5.699170296748263 - 65.11241969628546i, 19.819861372592023 + 25.900390198129045i, 16.557353232092076 - 37.25486567332457i],\
[8.548264534732331 - 47.59913064936665i, 14.40138539657334 - 90.80495969865513i, 29.343082104326758 - 15.039062252958018i, 27.20916452240602 + 25.774841219390325i, 19.38506691927698 - 95.11167912062224i, 29.17634152715012 - 95.07970712229994i, 2.1987345350210092 - 9.041770826482406i, 2.806832236244097 + 2.0385477771778966i], [24.20532702537307 + 12.879358968749457i, 25.839682426729887 - 18.102222530229938i, 29.093489513094948 - 9.581972254775465i, 12.65038940459419 - 55.38946414968438i, -0.7049513892161683 - 23.70085292748422i, 7.910814607291806 + 24.701861346839564i, 2.4219941297871004 + 28.36329723916822i, 16.535587534250833 - 38.86239252709116i],\
[25.78464278752434 - 59.91370905634549i, 29.424608924558413 - 19.120899022196383i, 25.6548685301034 + 6.075863297676378i, 3.693006642780766 - 63.363384338945906i, 15.716418860938354 - 73.40923022486281i, 28.9161836809681 - 58.38357844908446i, 10.13807260697836 - 3.5085542186585883i, 16.925761654754282 - 37.905623267161424i]\
]');
var r = math.qr(m);
// Q
assert.deepEqual(
r.Q,
math.eval('[\
[-0.0038074725834465403 + 0.029084550335153184i, 0.22686378024210954 - 0.8031909609489004i, -0.1539944364016218 - 0.08044026151398012i, 0.15914274660150135 - 0.4970365797781979i],\
[0.09969981781897692 - 0.5551565039665838i, 0.03656768230049788 - 0.4048572821234369i, 0.03460099750064215 + 0.4176688417721519i, 0.06529314053052465 + 0.5802645116992661i],\
[0.2823107175583003 + 0.1502140858641239i, 0.04201869101132175 - 0.25276582362981437i, 0.7610890159088707 - 0.3999596125636107i, -0.24146613640405268 + 0.18587678263056984i],\
[0.3007305375258921 - 0.6987834610763923i, 0.02974780206453512 + 0.2676367453654318i, 0.23430030839452232 - 0.007054866167671124i, -0.024719751847322398 - 0.5414711325141984i]\
]'));
// R
assert.deepEqual(
r.R,
math.eval('[\
[85.74002161421444 - 1.7763568394002505e-15i, 75.75511004703746 + 4.3347264490288016i, 20.511425451943854 + 26.86626726613313i, 27.288058950461433 - 16.62801026736354i, 105.22335436327181 - 17.027323945468076i, 109.21486260617472 + 15.233872631050161i, 16.361518290342467 + 13.316745322711627i, 28.409955756511188 - 11.605326516313891i],\
[0 + 0i, 121.47784233162547 + 1.7763568394002505e-15i, 44.01977059734889 + 24.441930600590624i, 38.83986358402923 + 10.93198966397847i, 78.56760829656308 + 7.162388196994509i, 72.474482997425 - 8.297010771192621i, -20.270457048330027 + 21.34082444731987i, 33.83280850600839 + 2.9469680307519037i],\
[0 + 0i, 0 + 0i, 25.372653909655675 + 5.329070518200751e-15i, 46.75701662904174 - 52.038112884483404i, -25.7821433027293 - 35.64391269354021i, -31.014234782164266 + 3.4985227007956983i, -15.936684410229294 + 18.179762871924087i, 33.75717971935531 - 25.758933854786893i],\
[0 + 0i, 0 + 0i, 0 + 0i, 69.24128415239949 + 0i, 14.27806840079945 + 4.055317531798819i, 13.583401274164364 - 21.002114936285405i, -8.485891575536547 + 10.384078077176659i, 31.408176714183693 + 17.21736552045245i]\
]'));
// verify
assertValidQRDecomposition(m, r.Q, r.R);
});
});