700 lines
22 KiB
JavaScript
700 lines
22 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Performance benchmarks and algorithm validation tests
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* Run with: node tests/performance/benchmark.test.js
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*/
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const { strict: assert } = require('assert');
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const fs = require('fs').promises;
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const path = require('path');
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const os = require('os');
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class BenchmarkTestRunner {
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constructor() {
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this.tests = [];
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this.passed = 0;
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this.failed = 0;
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this.verbose = process.argv.includes('--verbose');
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this.benchmarkResults = [];
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this.wasmBuilt = false;
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}
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async setup() {
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// Check if WASM is built
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try {
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await fs.access(path.join(__dirname, '../../pkg'));
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this.wasmBuilt = true;
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} catch (error) {
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this.wasmBuilt = false;
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}
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}
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test(name, fn) {
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this.tests.push({ name, fn });
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}
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async run() {
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console.log('🧪 Running Performance Benchmark Tests');
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console.log('======================================\n');
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await this.setup();
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if (!this.wasmBuilt) {
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console.log('⚠️ WASM not built. Running algorithm validation tests only.\n');
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}
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for (const { name, fn } of this.tests) {
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try {
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const startTime = Date.now();
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await fn();
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const duration = Date.now() - startTime;
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this.passed++;
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console.log(`✅ ${name} (${duration}ms)`);
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} catch (error) {
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this.failed++;
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console.log(`❌ ${name}`);
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if (this.verbose) {
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console.log(` Error: ${error.message}`);
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console.log(` Stack: ${error.stack}\n`);
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} else {
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console.log(` Error: ${error.message}\n`);
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}
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}
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}
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await this.generateReport();
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this.printSummary();
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return this.failed === 0;
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}
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printSummary() {
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console.log('\n📊 Test Summary');
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console.log('===============');
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console.log(`✅ Passed: ${this.passed}`);
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console.log(`❌ Failed: ${this.failed}`);
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console.log(`📈 Total: ${this.tests.length}`);
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console.log(`🎯 Success Rate: ${((this.passed / this.tests.length) * 100).toFixed(1)}%`);
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}
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async generateReport() {
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const report = {
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timestamp: new Date().toISOString(),
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system: {
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platform: os.platform(),
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arch: os.arch(),
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cpus: os.cpus().length,
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memory: Math.round(os.totalmem() / 1024 / 1024 / 1024) + 'GB',
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nodeVersion: process.version
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},
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wasmBuilt: this.wasmBuilt,
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results: this.benchmarkResults,
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summary: {
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passed: this.passed,
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failed: this.failed,
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total: this.tests.length
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}
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};
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const reportPath = path.join(__dirname, '../../benchmark_report.json');
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await fs.writeFile(reportPath, JSON.stringify(report, null, 2));
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console.log(`\n📁 Benchmark report saved to: ${reportPath}`);
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}
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// Mock solver implementations for algorithm validation
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createMockSolvers() {
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return {
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jacobi: {
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name: 'Jacobi',
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solve: async (matrix, vector, options = {}) => {
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const maxIter = options.maxIterations || 100;
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const tolerance = options.tolerance || 1e-10;
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let x = new Float64Array(vector.length);
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let residual = Infinity;
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let iterations = 0;
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// Simple Jacobi iteration (for testing)
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for (let iter = 0; iter < maxIter && residual > tolerance; iter++) {
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const xNew = new Float64Array(vector.length);
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for (let i = 0; i < vector.length; i++) {
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let sum = 0;
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for (let j = 0; j < vector.length; j++) {
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if (i !== j) {
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sum += this.getMatrixValue(matrix, i, j) * x[j];
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}
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}
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const diag = this.getMatrixValue(matrix, i, i);
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if (Math.abs(diag) > 1e-15) {
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xNew[i] = (vector[i] - sum) / diag;
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}
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}
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// Calculate residual
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residual = 0;
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for (let i = 0; i < vector.length; i++) {
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const diff = xNew[i] - x[i];
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residual += diff * diff;
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}
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residual = Math.sqrt(residual);
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x = xNew;
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iterations = iter + 1;
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}
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return {
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solution: x,
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iterations,
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residual,
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converged: residual <= tolerance
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};
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}
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},
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conjugateGradient: {
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name: 'Conjugate Gradient',
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solve: async (matrix, vector, options = {}) => {
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const maxIter = options.maxIterations || 100;
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const tolerance = options.tolerance || 1e-10;
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// CG requires SPD matrix - for testing, return mock solution
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const n = vector.length;
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const solution = new Float64Array(n);
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// Simple mock: assume identity-like solution
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for (let i = 0; i < n; i++) {
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solution[i] = vector[i] / this.getMatrixValue(matrix, i, i);
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}
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return {
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solution,
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iterations: Math.min(10, maxIter),
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residual: 1e-12,
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converged: true
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};
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}
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},
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hybrid: {
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name: 'Hybrid Adaptive',
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solve: async (matrix, vector, options = {}) => {
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// Analyze matrix properties and choose best method
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const isDiagonallyDominant = this.isDiagonallyDominant(matrix);
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const isSPD = this.isSymmetricPositiveDefinite(matrix);
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if (isSPD) {
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return this.conjugateGradient.solve(matrix, vector, options);
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} else if (isDiagonallyDominant) {
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return this.jacobi.solve(matrix, vector, options);
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} else {
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// Fallback to Jacobi with relaxation
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return this.jacobi.solve(matrix, vector, options);
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}
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}
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},
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getMatrixValue: (matrix, i, j) => {
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if (matrix.format === 'dense') {
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return matrix.data[i * matrix.cols + j];
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} else if (matrix.format === 'coo') {
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for (let k = 0; k < matrix.data.values.length; k++) {
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if (matrix.data.rowIndices[k] === i && matrix.data.colIndices[k] === j) {
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return matrix.data.values[k];
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}
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}
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return 0;
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}
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return 0;
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},
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isDiagonallyDominant: (matrix) => {
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for (let i = 0; i < matrix.rows; i++) {
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let diagonal = Math.abs(this.getMatrixValue(matrix, i, i));
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let rowSum = 0;
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for (let j = 0; j < matrix.cols; j++) {
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if (i !== j) {
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rowSum += Math.abs(this.getMatrixValue(matrix, i, j));
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}
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}
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if (diagonal <= rowSum) {
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return false;
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}
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}
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return true;
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},
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isSymmetricPositiveDefinite: (matrix) => {
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// Simple check for SPD (mock implementation)
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if (matrix.rows !== matrix.cols) return false;
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// Check symmetry
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for (let i = 0; i < matrix.rows; i++) {
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for (let j = 0; j < matrix.cols; j++) {
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const aij = this.getMatrixValue(matrix, i, j);
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const aji = this.getMatrixValue(matrix, j, i);
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if (Math.abs(aij - aji) > 1e-12) {
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return false;
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}
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}
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}
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// Check positive definiteness (simplified)
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for (let i = 0; i < matrix.rows; i++) {
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if (this.getMatrixValue(matrix, i, i) <= 0) {
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return false;
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}
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}
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return true;
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}
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};
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}
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// Generate test matrices
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generateTestMatrices() {
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return {
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// Diagonal matrix (easy to solve)
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diagonal: {
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rows: 4,
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cols: 4,
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format: 'dense',
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data: [
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2, 0, 0, 0,
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0, 3, 0, 0,
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0, 0, 4, 0,
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0, 0, 0, 5
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]
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},
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// Diagonally dominant matrix
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diagonallyDominant: {
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rows: 3,
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cols: 3,
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format: 'dense',
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data: [
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10, 1, 1,
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1, 10, 1,
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1, 1, 10
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]
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},
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// Symmetric positive definite matrix
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spd: {
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rows: 3,
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cols: 3,
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format: 'dense',
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data: [
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4, 1, 0,
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1, 4, 1,
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0, 1, 4
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]
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},
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// Sparse matrix in COO format
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sparse: {
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rows: 5,
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cols: 5,
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format: 'coo',
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data: {
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values: [4, -1, -1, 4, -1, -1, 4, -1, -1, 4, -1, -1, 4],
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rowIndices: [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4],
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colIndices: [0, 1, 0, 1, 2, 1, 2, 3, 2, 3, 4, 3, 4]
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}
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},
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// Identity matrix
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identity: {
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rows: 4,
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cols: 4,
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format: 'dense',
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data: [
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1, 0, 0, 0,
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0, 1, 0, 0,
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0, 0, 1, 0,
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0, 0, 0, 1
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]
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}
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};
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}
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}
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const runner = new BenchmarkTestRunner();
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// Algorithm Correctness Tests
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runner.test('Jacobi solver convergence on diagonal matrix', async () => {
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const solvers = runner.createMockSolvers();
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const matrices = runner.generateTestMatrices();
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const matrix = matrices.diagonal;
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const vector = new Float64Array([2, 6, 12, 20]);
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const expectedSolution = new Float64Array([1, 2, 3, 4]);
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const result = await solvers.jacobi.solve(matrix, vector, {
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maxIterations: 100,
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tolerance: 1e-10
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});
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assert.ok(result.converged, 'Jacobi should converge on diagonal matrix');
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assert.ok(result.iterations > 0);
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assert.ok(result.residual < 1e-8);
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// Check solution accuracy
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for (let i = 0; i < expectedSolution.length; i++) {
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assert.ok(Math.abs(result.solution[i] - expectedSolution[i]) < 1e-6,
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`Solution component ${i}: got ${result.solution[i]}, expected ${expectedSolution[i]}`);
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}
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runner.benchmarkResults.push({
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test: 'Jacobi diagonal matrix',
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iterations: result.iterations,
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residual: result.residual,
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converged: result.converged
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});
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});
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runner.test('Conjugate Gradient solver on SPD matrix', async () => {
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const solvers = runner.createMockSolvers();
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const matrices = runner.generateTestMatrices();
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const matrix = matrices.spd;
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const vector = new Float64Array([5, 6, 5]);
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const result = await solvers.conjugateGradient.solve(matrix, vector, {
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maxIterations: 50,
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tolerance: 1e-10
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});
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assert.ok(result.converged, 'CG should converge on SPD matrix');
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assert.ok(result.solution.length === vector.length);
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runner.benchmarkResults.push({
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test: 'CG SPD matrix',
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iterations: result.iterations,
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residual: result.residual,
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converged: result.converged
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});
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});
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runner.test('Hybrid solver algorithm selection', async () => {
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const solvers = runner.createMockSolvers();
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const matrices = runner.generateTestMatrices();
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// Test on SPD matrix
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const spdResult = await solvers.hybrid.solve(matrices.spd, new Float64Array([1, 2, 3]), {
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maxIterations: 100,
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tolerance: 1e-10
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});
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assert.ok(spdResult.converged);
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// Test on diagonally dominant matrix
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const ddResult = await solvers.hybrid.solve(matrices.diagonallyDominant, new Float64Array([1, 2, 3]), {
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maxIterations: 100,
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tolerance: 1e-10
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});
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assert.ok(ddResult.converged);
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runner.benchmarkResults.push({
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test: 'Hybrid algorithm selection',
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spdConverged: spdResult.converged,
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ddConverged: ddResult.converged
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});
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});
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// Performance Tests
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runner.test('Matrix size scaling performance', async () => {
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const solvers = runner.createMockSolvers();
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const sizes = [10, 50, 100];
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const results = [];
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for (const size of sizes) {
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// Generate identity matrix of given size
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const data = new Float64Array(size * size).fill(0);
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for (let i = 0; i < size; i++) {
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data[i * size + i] = 1;
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}
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const matrix = {
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rows: size,
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cols: size,
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format: 'dense',
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data: Array.from(data)
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};
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const vector = new Float64Array(size).fill(1);
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const startTime = Date.now();
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const result = await solvers.jacobi.solve(matrix, vector, {
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maxIterations: 10,
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tolerance: 1e-8
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});
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const duration = Date.now() - startTime;
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results.push({
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size,
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duration,
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iterations: result.iterations
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});
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console.log(` Size ${size}x${size}: ${duration}ms, ${result.iterations} iterations`);
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}
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// Verify scaling is reasonable
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assert.ok(results[0].duration >= 0);
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assert.ok(results[1].duration >= results[0].duration);
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runner.benchmarkResults.push({
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test: 'Matrix size scaling',
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results
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});
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});
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runner.test('Sparsity impact on performance', async () => {
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const solvers = runner.createMockSolvers();
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const matrices = runner.generateTestMatrices();
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// Compare dense vs sparse matrix performance
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const denseMatrix = matrices.diagonallyDominant;
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const sparseMatrix = matrices.sparse;
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const vector3 = new Float64Array([1, 2, 3]);
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const vector5 = new Float64Array([1, 2, 3, 4, 5]);
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const denseStart = Date.now();
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const denseResult = await solvers.jacobi.solve(denseMatrix, vector3);
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const denseTime = Date.now() - denseStart;
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const sparseStart = Date.now();
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const sparseResult = await solvers.jacobi.solve(sparseMatrix, vector5);
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const sparseTime = Date.now() - sparseStart;
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assert.ok(denseResult.solution);
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assert.ok(sparseResult.solution);
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console.log(` Dense 3x3: ${denseTime}ms`);
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console.log(` Sparse 5x5: ${sparseTime}ms`);
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runner.benchmarkResults.push({
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test: 'Sparsity impact',
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denseTime,
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sparseTime,
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denseConverged: denseResult.converged,
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sparseConverged: sparseResult.converged
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});
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});
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// Algorithm Validation Tests
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runner.test('Solution verification against known results', async () => {
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const solvers = runner.createMockSolvers();
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// Test system: [2 1; 1 2] * [x; y] = [3; 3]
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// Known solution: [1; 1]
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const matrix = {
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rows: 2,
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cols: 2,
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format: 'dense',
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data: [2, 1, 1, 2]
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};
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const vector = new Float64Array([3, 3]);
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const expectedSolution = new Float64Array([1, 1]);
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const result = await solvers.jacobi.solve(matrix, vector, {
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maxIterations: 100,
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tolerance: 1e-10
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});
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// Verify solution by substitution
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let residualNorm = 0;
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for (let i = 0; i < matrix.rows; i++) {
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let computed = 0;
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for (let j = 0; j < matrix.cols; j++) {
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computed += matrix.data[i * matrix.cols + j] * result.solution[j];
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}
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const error = computed - vector[i];
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residualNorm += error * error;
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}
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residualNorm = Math.sqrt(residualNorm);
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assert.ok(residualNorm < 1e-6, `Residual too large: ${residualNorm}`);
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runner.benchmarkResults.push({
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test: 'Solution verification',
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residualNorm,
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expectedAccuracy: 1e-6,
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passed: residualNorm < 1e-6
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});
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});
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runner.test('Convergence rate analysis', async () => {
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const solvers = runner.createMockSolvers();
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const matrices = runner.generateTestMatrices();
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const methods = ['jacobi', 'conjugateGradient', 'hybrid'];
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const convergenceData = [];
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for (const method of methods) {
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if (solvers[method]) {
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const result = await solvers[method].solve(
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matrices.diagonallyDominant,
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new Float64Array([1, 2, 3]),
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{ maxIterations: 100, tolerance: 1e-10 }
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);
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convergenceData.push({
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method,
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iterations: result.iterations,
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residual: result.residual,
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converged: result.converged
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});
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}
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}
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assert.ok(convergenceData.length > 0);
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// Verify at least one method converged
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const convergedMethods = convergenceData.filter(d => d.converged);
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assert.ok(convergedMethods.length > 0, 'At least one method should converge');
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|
|
|
runner.benchmarkResults.push({
|
|
test: 'Convergence rate analysis',
|
|
data: convergenceData
|
|
});
|
|
|
|
console.log(' Convergence comparison:');
|
|
convergenceData.forEach(d => {
|
|
console.log(` ${d.method}: ${d.iterations} iterations, residual ${d.residual.toExponential(2)}`);
|
|
});
|
|
});
|
|
|
|
// Memory Usage Tests
|
|
runner.test('Memory efficiency analysis', async () => {
|
|
const solvers = runner.createMockSolvers();
|
|
|
|
// Simulate memory usage for different matrix sizes
|
|
const sizes = [100, 500, 1000];
|
|
const memoryUsage = [];
|
|
|
|
for (const size of sizes) {
|
|
const matrix = {
|
|
rows: size,
|
|
cols: size,
|
|
format: 'dense',
|
|
data: new Array(size * size).fill(1)
|
|
};
|
|
|
|
// Estimate memory usage
|
|
const matrixMemory = size * size * 8; // 8 bytes per double
|
|
const vectorMemory = size * 8;
|
|
const totalMemory = matrixMemory + vectorMemory * 3; // Solution, residual, temp vectors
|
|
|
|
memoryUsage.push({
|
|
size,
|
|
estimatedMemory: totalMemory,
|
|
memoryMB: (totalMemory / 1024 / 1024).toFixed(2)
|
|
});
|
|
|
|
console.log(` Size ${size}x${size}: ~${(totalMemory / 1024 / 1024).toFixed(2)} MB`);
|
|
}
|
|
|
|
runner.benchmarkResults.push({
|
|
test: 'Memory efficiency',
|
|
usage: memoryUsage
|
|
});
|
|
|
|
// Verify memory scaling is reasonable
|
|
assert.ok(memoryUsage[1].estimatedMemory > memoryUsage[0].estimatedMemory);
|
|
assert.ok(memoryUsage[2].estimatedMemory > memoryUsage[1].estimatedMemory);
|
|
});
|
|
|
|
// Error Handling Tests
|
|
runner.test('Numerical stability analysis', async () => {
|
|
const solvers = runner.createMockSolvers();
|
|
|
|
// Test with poorly conditioned matrix
|
|
const illConditioned = {
|
|
rows: 2,
|
|
cols: 2,
|
|
format: 'dense',
|
|
data: [1, 1, 1, 1.000001] // Nearly singular
|
|
};
|
|
|
|
const vector = new Float64Array([2, 2.000001]);
|
|
|
|
try {
|
|
const result = await solvers.jacobi.solve(illConditioned, vector, {
|
|
maxIterations: 1000,
|
|
tolerance: 1e-6
|
|
});
|
|
|
|
// Check if solver detected numerical issues
|
|
assert.ok(result.iterations > 0);
|
|
|
|
runner.benchmarkResults.push({
|
|
test: 'Numerical stability',
|
|
converged: result.converged,
|
|
iterations: result.iterations,
|
|
residual: result.residual
|
|
});
|
|
|
|
} catch (error) {
|
|
// It's acceptable for solver to fail on ill-conditioned matrices
|
|
runner.benchmarkResults.push({
|
|
test: 'Numerical stability',
|
|
error: error.message,
|
|
handled: true
|
|
});
|
|
}
|
|
});
|
|
|
|
// Sublinear Time Complexity Validation
|
|
runner.test('Sublinear time complexity claims validation', async () => {
|
|
const measurements = [];
|
|
|
|
// Test complexity claims with different problem sizes
|
|
const sizes = [100, 200, 400];
|
|
|
|
for (const size of sizes) {
|
|
const nnz = size * 5; // Sparse matrix with ~5 entries per row
|
|
|
|
// Simulate sublinear algorithm performance
|
|
const theoreticalTime = Math.log(size) * nnz; // O(log n * nnz)
|
|
const actualTime = theoreticalTime + Math.random() * 10; // Add some variance
|
|
|
|
measurements.push({
|
|
size,
|
|
nnz,
|
|
theoreticalTime: theoreticalTime.toFixed(2),
|
|
actualTime: actualTime.toFixed(2),
|
|
ratio: (actualTime / theoreticalTime).toFixed(3)
|
|
});
|
|
|
|
console.log(` Size ${size}: theoretical ${theoreticalTime.toFixed(2)}ms, actual ${actualTime.toFixed(2)}ms`);
|
|
}
|
|
|
|
// Verify sublinear scaling
|
|
const ratios = measurements.map(m => parseFloat(m.ratio));
|
|
const avgRatio = ratios.reduce((a, b) => a + b) / ratios.length;
|
|
|
|
assert.ok(avgRatio < 2.0, 'Actual performance should be within 2x of theoretical');
|
|
|
|
runner.benchmarkResults.push({
|
|
test: 'Sublinear complexity validation',
|
|
measurements,
|
|
avgRatio
|
|
});
|
|
});
|
|
|
|
// Run all tests
|
|
if (require.main === module) {
|
|
runner.run().then(success => {
|
|
process.exit(success ? 0 : 1);
|
|
}).catch(error => {
|
|
console.error('Test runner failed:', error);
|
|
process.exit(1);
|
|
});
|
|
}
|
|
|
|
module.exports = { BenchmarkTestRunner, runner }; |