91 lines
3.1 KiB
JavaScript
91 lines
3.1 KiB
JavaScript
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"use strict";
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Object.defineProperty(exports, "__esModule", {
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value: true
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});
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exports.createCsSpsolve = void 0;
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var _csReach = require("./csReach.js");
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var _factory = require("../../../utils/factory.js");
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// Copyright (c) 2006-2024, Timothy A. Davis, All Rights Reserved.
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// SPDX-License-Identifier: LGPL-2.1+
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// https://github.com/DrTimothyAldenDavis/SuiteSparse/tree/dev/CSparse/Source
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const name = 'csSpsolve';
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const dependencies = ['divideScalar', 'multiply', 'subtract'];
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const createCsSpsolve = exports.createCsSpsolve = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => {
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let {
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divideScalar,
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multiply,
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subtract
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} = _ref;
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/**
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* The function csSpsolve() computes the solution to G * x = bk, where bk is the
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* kth column of B. When lo is true, the function assumes G = L is lower triangular with the
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* diagonal entry as the first entry in each column. When lo is true, the function assumes G = U
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* is upper triangular with the diagonal entry as the last entry in each column.
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*
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* @param {Matrix} g The G matrix
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* @param {Matrix} b The B matrix
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* @param {Number} k The kth column in B
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* @param {Array} xi The nonzero pattern xi[top] .. xi[n - 1], an array of size = 2 * n
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* The first n entries is the nonzero pattern, the last n entries is the stack
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* @param {Array} x The soluton to the linear system G * x = b
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* @param {Array} pinv The inverse row permutation vector, must be null for L * x = b
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* @param {boolean} lo The lower (true) upper triangular (false) flag
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*
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* @return {Number} The index for the nonzero pattern
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*/
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return function csSpsolve(g, b, k, xi, x, pinv, lo) {
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// g arrays
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const gvalues = g._values;
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const gindex = g._index;
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const gptr = g._ptr;
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const gsize = g._size;
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// columns
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const n = gsize[1];
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// b arrays
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const bvalues = b._values;
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const bindex = b._index;
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const bptr = b._ptr;
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// vars
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let p, p0, p1, q;
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// xi[top..n-1] = csReach(B(:,k))
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const top = (0, _csReach.csReach)(g, b, k, xi, pinv);
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// clear x
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for (p = top; p < n; p++) {
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x[xi[p]] = 0;
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}
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// scatter b
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for (p0 = bptr[k], p1 = bptr[k + 1], p = p0; p < p1; p++) {
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x[bindex[p]] = bvalues[p];
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}
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// loop columns
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for (let px = top; px < n; px++) {
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// x array index for px
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const j = xi[px];
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// apply permutation vector (U x = b), j maps to column J of G
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const J = pinv ? pinv[j] : j;
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// check column J is empty
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if (J < 0) {
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continue;
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}
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// column value indeces in G, p0 <= p < p1
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p0 = gptr[J];
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p1 = gptr[J + 1];
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// x(j) /= G(j,j)
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x[j] = divideScalar(x[j], gvalues[lo ? p0 : p1 - 1]);
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// first entry L(j,j)
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p = lo ? p0 + 1 : p0;
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q = lo ? p1 : p1 - 1;
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// loop
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for (; p < q; p++) {
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// row
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const i = gindex[p];
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// x(i) -= G(i,j) * x(j)
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x[i] = subtract(x[i], multiply(gvalues[p], x[j]));
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}
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}
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// return top of stack
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return top;
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};
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});
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