100 lines
2.9 KiB
JavaScript
100 lines
2.9 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.createCsSymperm = void 0;
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var _csCumsum = require("./csCumsum.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 = 'csSymperm';
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const dependencies = ['conj', 'SparseMatrix'];
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const createCsSymperm = exports.createCsSymperm = /* #__PURE__ */(0, _factory.factory)(name, dependencies, _ref => {
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let {
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conj,
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SparseMatrix
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} = _ref;
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/**
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* Computes the symmetric permutation of matrix A accessing only
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* the upper triangular part of A.
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*
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* C = P * A * P'
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*
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* @param {Matrix} a The A matrix
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* @param {Array} pinv The inverse of permutation vector
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* @param {boolean} values Process matrix values (true)
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*
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* @return {Matrix} The C matrix, C = P * A * P'
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*/
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return function csSymperm(a, pinv, values) {
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// A matrix arrays
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const avalues = a._values;
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const aindex = a._index;
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const aptr = a._ptr;
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const asize = a._size;
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// columns
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const n = asize[1];
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// C matrix arrays
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const cvalues = values && avalues ? [] : null;
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const cindex = []; // (nz)
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const cptr = []; // (n + 1)
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// variables
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let i, i2, j, j2, p, p0, p1;
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// create workspace vector
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const w = []; // (n)
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// count entries in each column of C
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for (j = 0; j < n; j++) {
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// column j of A is column j2 of C
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j2 = pinv ? pinv[j] : j;
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// loop values in column j
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for (p0 = aptr[j], p1 = aptr[j + 1], p = p0; p < p1; p++) {
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// row
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i = aindex[p];
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// skip lower triangular part of A
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if (i > j) {
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continue;
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}
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// row i of A is row i2 of C
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i2 = pinv ? pinv[i] : i;
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// column count of C
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w[Math.max(i2, j2)]++;
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}
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}
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// compute column pointers of C
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(0, _csCumsum.csCumsum)(cptr, w, n);
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// loop columns
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for (j = 0; j < n; j++) {
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// column j of A is column j2 of C
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j2 = pinv ? pinv[j] : j;
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// loop values in column j
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for (p0 = aptr[j], p1 = aptr[j + 1], p = p0; p < p1; p++) {
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// row
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i = aindex[p];
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// skip lower triangular part of A
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if (i > j) {
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continue;
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}
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// row i of A is row i2 of C
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i2 = pinv ? pinv[i] : i;
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// C index for column j2
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const q = w[Math.max(i2, j2)]++;
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// update C index for entry q
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cindex[q] = Math.min(i2, j2);
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// check we need to process values
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if (cvalues) {
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cvalues[q] = i2 <= j2 ? avalues[p] : conj(avalues[p]);
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}
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}
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}
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// return C matrix
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return new SparseMatrix({
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values: cvalues,
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index: cindex,
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ptr: cptr,
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size: [n, n]
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});
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};
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});
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