feat:node-modules

node_modules/mathjs/lib/esm/function/algebra/solver/usolve.js

@@ -0,0 +1,161 @@
+import { factory } from '../../../utils/factory.js';
+import { createSolveValidation } from './utils/solveValidation.js';
+var name = 'usolve';
+var dependencies = ['typed', 'matrix', 'divideScalar', 'multiplyScalar', 'subtractScalar', 'equalScalar', 'DenseMatrix'];
+export var createUsolve = /* #__PURE__ */factory(name, dependencies, _ref => {
+  var {
+    typed,
+    matrix,
+    divideScalar,
+    multiplyScalar,
+    subtractScalar,
+    equalScalar,
+    DenseMatrix
+  } = _ref;
+  var solveValidation = createSolveValidation({
+    DenseMatrix
+  });
+
+  /**
+   * Finds one solution of a linear equation system by backward substitution. Matrix must be an upper triangular matrix. Throws an error if there's no solution.
+   *
+   * `U * x = b`
+   *
+   * Syntax:
+   *
+   *    math.usolve(U, b)
+   *
+   * Examples:
+   *
+   *    const a = [[-2, 3], [2, 1]]
+   *    const b = [11, 9]
+   *    const x = usolve(a, b)  // [[8], [9]]
+   *
+   * See also:
+   *
+   *    usolveAll, lup, slu, usolve, lusolve
+   *
+   * @param {Matrix, Array} U       A N x N matrix or array (U)
+   * @param {Matrix, Array} b       A column vector with the b values
+   *
+   * @return {DenseMatrix | Array}  A column vector with the linear system solution (x)
+   */
+  return typed(name, {
+    'SparseMatrix, Array | Matrix': function SparseMatrix_Array__Matrix(m, b) {
+      return _sparseBackwardSubstitution(m, b);
+    },
+    'DenseMatrix, Array | Matrix': function DenseMatrix_Array__Matrix(m, b) {
+      return _denseBackwardSubstitution(m, b);
+    },
+    'Array, Array | Matrix': function Array_Array__Matrix(a, b) {
+      var m = matrix(a);
+      var r = _denseBackwardSubstitution(m, b);
+      return r.valueOf();
+    }
+  });
+  function _denseBackwardSubstitution(m, b) {
+    // make b into a column vector
+    b = solveValidation(m, b, true);
+    var bdata = b._data;
+    var rows = m._size[0];
+    var columns = m._size[1];
+
+    // result
+    var x = [];
+    var mdata = m._data;
+    // loop columns backwards
+    for (var j = columns - 1; j >= 0; j--) {
+      // b[j]
+      var bj = bdata[j][0] || 0;
+      // x[j]
+      var xj = void 0;
+      if (!equalScalar(bj, 0)) {
+        // value at [j, j]
+        var vjj = mdata[j][j];
+        if (equalScalar(vjj, 0)) {
+          // system cannot be solved
+          throw new Error('Linear system cannot be solved since matrix is singular');
+        }
+        xj = divideScalar(bj, vjj);
+
+        // loop rows
+        for (var i = j - 1; i >= 0; i--) {
+          // update copy of b
+          bdata[i] = [subtractScalar(bdata[i][0] || 0, multiplyScalar(xj, mdata[i][j]))];
+        }
+      } else {
+        // zero value at j
+        xj = 0;
+      }
+      // update x
+      x[j] = [xj];
+    }
+    return new DenseMatrix({
+      data: x,
+      size: [rows, 1]
+    });
+  }
+  function _sparseBackwardSubstitution(m, b) {
+    // make b into a column vector
+    b = solveValidation(m, b, true);
+    var bdata = b._data;
+    var rows = m._size[0];
+    var columns = m._size[1];
+    var values = m._values;
+    var index = m._index;
+    var ptr = m._ptr;
+
+    // result
+    var x = [];
+
+    // loop columns backwards
+    for (var j = columns - 1; j >= 0; j--) {
+      var bj = bdata[j][0] || 0;
+      if (!equalScalar(bj, 0)) {
+        // non-degenerate row, find solution
+
+        var vjj = 0;
+
+        // upper triangular matrix values & index (column j)
+        var jValues = [];
+        var jIndices = [];
+
+        // first & last indeces in column
+        var firstIndex = ptr[j];
+        var lastIndex = ptr[j + 1];
+
+        // values in column, find value at [j, j], loop backwards
+        for (var k = lastIndex - 1; k >= firstIndex; k--) {
+          var i = index[k];
+
+          // check row (rows are not sorted!)
+          if (i === j) {
+            vjj = values[k];
+          } else if (i < j) {
+            // store upper triangular
+            jValues.push(values[k]);
+            jIndices.push(i);
+          }
+        }
+
+        // at this point we must have a value in vjj
+        if (equalScalar(vjj, 0)) {
+          throw new Error('Linear system cannot be solved since matrix is singular');
+        }
+        var xj = divideScalar(bj, vjj);
+        for (var _k = 0, _lastIndex = jIndices.length; _k < _lastIndex; _k++) {
+          var _i = jIndices[_k];
+          bdata[_i] = [subtractScalar(bdata[_i][0], multiplyScalar(xj, jValues[_k]))];
+        }
+        x[j] = [xj];
+      } else {
+        // degenerate row, we can choose any value
+        x[j] = [0];
+      }
+    }
+    return new DenseMatrix({
+      data: x,
+      size: [rows, 1]
+    });
+  }
+});