feat:node-modules

node_modules/mathjs/lib/esm/function/algebra/decomposition/slu.js

@@ -0,0 +1,101 @@
+import { isInteger } from '../../../utils/number.js';
+import { factory } from '../../../utils/factory.js';
+import { createCsSqr } from '../sparse/csSqr.js';
+import { createCsLu } from '../sparse/csLu.js';
+var name = 'slu';
+var dependencies = ['typed', 'abs', 'add', 'multiply', 'transpose', 'divideScalar', 'subtract', 'larger', 'largerEq', 'SparseMatrix'];
+export var createSlu = /* #__PURE__ */factory(name, dependencies, _ref => {
+  var {
+    typed,
+    abs,
+    add,
+    multiply,
+    transpose,
+    divideScalar,
+    subtract,
+    larger,
+    largerEq,
+    SparseMatrix
+  } = _ref;
+  var csSqr = createCsSqr({
+    add,
+    multiply,
+    transpose
+  });
+  var csLu = createCsLu({
+    abs,
+    divideScalar,
+    multiply,
+    subtract,
+    larger,
+    largerEq,
+    SparseMatrix
+  });
+
+  /**
+   * Calculate the Sparse Matrix LU decomposition with full pivoting. Sparse Matrix `A` is decomposed in two matrices (`L`, `U`) and two permutation vectors (`pinv`, `q`) where
+   *
+   * `P * A * Q = L * U`
+   *
+   * Syntax:
+   *
+   *    math.slu(A, order, threshold)
+   *
+   * Examples:
+   *
+   *    const A = math.sparse([[4,3], [6, 3]])
+   *    math.slu(A, 1, 0.001)
+   *    // returns:
+   *    // {
+   *    //   L: [[1, 0], [1.5, 1]]
+   *    //   U: [[4, 3], [0, -1.5]]
+   *    //   p: [0, 1]
+   *    //   q: [0, 1]
+   *    // }
+   *
+   * See also:
+   *
+   *    lup, lsolve, usolve, lusolve
+   *
+   * @param {SparseMatrix} A              A two dimensional sparse matrix for which to get the LU decomposition.
+   * @param {Number}       order          The Symbolic Ordering and Analysis order:
+   *                                       0 - Natural ordering, no permutation vector q is returned
+   *                                       1 - Matrix must be square, symbolic ordering and analisis is performed on M = A + A'
+   *                                       2 - Symbolic ordering and analisis is performed on M = A' * A. Dense columns from A' are dropped, A recreated from A'.
+   *                                           This is appropriatefor LU factorization of unsymmetric matrices.
+   *                                       3 - Symbolic ordering and analisis is performed on M = A' * A. This is best used for LU factorization is matrix M has no dense rows.
+   *                                           A dense row is a row with more than 10*sqr(columns) entries.
+   * @param {Number}       threshold       Partial pivoting threshold (1 for partial pivoting)
+   *
+   * @return {Object} The lower triangular matrix, the upper triangular matrix and the permutation vectors.
+   */
+  return typed(name, {
+    'SparseMatrix, number, number': function SparseMatrix_number_number(a, order, threshold) {
+      // verify order
+      if (!isInteger(order) || order < 0 || order > 3) {
+        throw new Error('Symbolic Ordering and Analysis order must be an integer number in the interval [0, 3]');
+      }
+      // verify threshold
+      if (threshold < 0 || threshold > 1) {
+        throw new Error('Partial pivoting threshold must be a number from 0 to 1');
+      }
+
+      // perform symbolic ordering and analysis
+      var s = csSqr(order, a, false);
+
+      // perform lu decomposition
+      var f = csLu(a, s, threshold);
+
+      // return decomposition
+      return {
+        L: f.L,
+        U: f.U,
+        p: f.pinv,
+        q: s.q,
+        toString: function toString() {
+          return 'L: ' + this.L.toString() + '\nU: ' + this.U.toString() + '\np: ' + this.p.toString() + (this.q ? '\nq: ' + this.q.toString() : '') + '\n';
+        }
+      };
+    }
+  });
+});