simple-squiggle/node_modules/mathjs/lib/cjs/function/algebra/solver/lusolve.js

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"use strict";
Object.defineProperty(exports, "__esModule", {
value: true
});
exports.createLusolve = void 0;
var _is = require("../../../utils/is.js");
var _factory = require("../../../utils/factory.js");
var _solveValidation = require("./utils/solveValidation.js");
var _csIpvec = require("../sparse/csIpvec.js");
var name = 'lusolve';
var dependencies = ['typed', 'matrix', 'lup', 'slu', 'usolve', 'lsolve', 'DenseMatrix'];
var createLusolve = /* #__PURE__ */(0, _factory.factory)(name, dependencies, function (_ref) {
var typed = _ref.typed,
matrix = _ref.matrix,
lup = _ref.lup,
slu = _ref.slu,
usolve = _ref.usolve,
lsolve = _ref.lsolve,
DenseMatrix = _ref.DenseMatrix;
var solveValidation = (0, _solveValidation.createSolveValidation)({
DenseMatrix: DenseMatrix
});
/**
* Solves the linear system `A * x = b` where `A` is an [n x n] matrix and `b` is a [n] column vector.
*
* Syntax:
*
* math.lusolve(A, b) // returns column vector with the solution to the linear system A * x = b
* math.lusolve(lup, b) // returns column vector with the solution to the linear system A * x = b, lup = math.lup(A)
*
* Examples:
*
* const m = [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]
*
* const x = math.lusolve(m, [-1, -1, -1, -1]) // x = [[-1], [-0.5], [-1/3], [-0.25]]
*
* const f = math.lup(m)
* const x1 = math.lusolve(f, [-1, -1, -1, -1]) // x1 = [[-1], [-0.5], [-1/3], [-0.25]]
* const x2 = math.lusolve(f, [1, 2, 1, -1]) // x2 = [[1], [1], [1/3], [-0.25]]
*
* const a = [[-2, 3], [2, 1]]
* const b = [11, 9]
* const x = math.lusolve(a, b) // [[2], [5]]
*
* See also:
*
* lup, slu, lsolve, usolve
*
* @param {Matrix | Array | Object} A Invertible Matrix or the Matrix LU decomposition
* @param {Matrix | Array} b Column Vector
* @param {number} [order] The Symbolic Ordering and Analysis order, see slu for details. Matrix must be a SparseMatrix
* @param {Number} [threshold] Partial pivoting threshold (1 for partial pivoting), see slu for details. Matrix must be a SparseMatrix.
*
* @return {DenseMatrix | Array} Column vector with the solution to the linear system A * x = b
*/
return typed(name, {
'Array, Array | Matrix': function ArrayArrayMatrix(a, b) {
a = matrix(a);
var d = lup(a);
var x = _lusolve(d.L, d.U, d.p, null, b);
return x.valueOf();
},
'DenseMatrix, Array | Matrix': function DenseMatrixArrayMatrix(a, b) {
var d = lup(a);
return _lusolve(d.L, d.U, d.p, null, b);
},
'SparseMatrix, Array | Matrix': function SparseMatrixArrayMatrix(a, b) {
var d = lup(a);
return _lusolve(d.L, d.U, d.p, null, b);
},
'SparseMatrix, Array | Matrix, number, number': function SparseMatrixArrayMatrixNumberNumber(a, b, order, threshold) {
var d = slu(a, order, threshold);
return _lusolve(d.L, d.U, d.p, d.q, b);
},
'Object, Array | Matrix': function ObjectArrayMatrix(d, b) {
return _lusolve(d.L, d.U, d.p, d.q, b);
}
});
function _toMatrix(a) {
if ((0, _is.isMatrix)(a)) {
return a;
}
if ((0, _is.isArray)(a)) {
return matrix(a);
}
throw new TypeError('Invalid Matrix LU decomposition');
}
function _lusolve(l, u, p, q, b) {
// verify decomposition
l = _toMatrix(l);
u = _toMatrix(u); // apply row permutations if needed (b is a DenseMatrix)
if (p) {
b = solveValidation(l, b, true);
b._data = (0, _csIpvec.csIpvec)(p, b._data);
} // use forward substitution to resolve L * y = b
var y = lsolve(l, b); // use backward substitution to resolve U * x = y
var x = usolve(u, y); // apply column permutations if needed (x is a DenseMatrix)
if (q) {
x._data = (0, _csIpvec.csIpvec)(q, x._data);
}
return x;
}
});
exports.createLusolve = createLusolve;