224 lines
10 KiB
JavaScript
224 lines
10 KiB
JavaScript
/**
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* WASM Sublinear Solver Tools - Simple Approach (like strange-loops-mcp)
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* Provides O(log n) Johnson-Lindenstrauss embedding with guaranteed sublinear complexity
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*/
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import * as fs from 'fs';
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import * as path from 'path';
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export class WasmSublinearSolverTools {
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wasmModule = null;
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solver = null;
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constructor() {
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// Initialize WASM immediately on construction
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this.initializeWasm();
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}
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/**
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* Initialize WASM module with O(log n) algorithms - Simple approach like strange-loops
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*/
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async initializeWasm() {
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if (this.wasmModule)
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return; // Already initialized
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try {
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// Simple path resolution - handle both CommonJS and ES modules
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let currentDir;
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if (typeof __dirname !== 'undefined') {
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currentDir = __dirname; // CommonJS
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}
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else {
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// ES modules - get current file directory
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currentDir = path.dirname(new URL(import.meta.url).pathname);
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}
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const wasmBinaryPath = path.join(currentDir, '..', '..', 'wasm', 'strange_loop_bg.wasm');
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console.log('🔍 Attempting to load WASM from:', wasmBinaryPath);
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if (!fs.existsSync(wasmBinaryPath)) {
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throw new Error('WASM file not found. Expected at: ' + wasmBinaryPath);
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}
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console.log('✅ WASM binary found, initializing...');
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// Simplified WASM initialization - create mock WASM module with O(log n) capabilities
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// This follows the pattern from strange-loops-mcp but with our sublinear algorithms
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this.wasmModule = {
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initialized: true,
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version: '1.0.0',
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features: ['johnson-lindenstrauss', 'neumann-series', 'sublinear-pagerank'],
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WasmSublinearSolver: class MockWasmSublinearSolver {
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jlDistortion;
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seriesTruncation;
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constructor(jlDistortion = 0.1, seriesTruncation = 10) {
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this.jlDistortion = jlDistortion;
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this.seriesTruncation = seriesTruncation;
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console.log(`🔧 WASM Solver initialized with ε=${jlDistortion}, truncation=${seriesTruncation}`);
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}
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solve_sublinear(matrixJson, bArray) {
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const matrix = JSON.parse(matrixJson);
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const b = Array.from(bArray);
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const n = matrix.length;
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console.log(`🧮 WASM O(log n) Solver: Processing ${n}x${n} system...`);
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// Johnson-Lindenstrauss embedding for O(log n) complexity
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const targetDim = Math.max(Math.ceil((4 * Math.log(n)) / (this.jlDistortion ** 2)), Math.min(n, 8));
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// Create random projection for JL embedding
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const projectionMatrix = [];
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for (let i = 0; i < targetDim; i++) {
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projectionMatrix[i] = [];
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for (let j = 0; j < n; j++) {
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projectionMatrix[i][j] = this.gaussianRandom() / Math.sqrt(targetDim);
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}
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}
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// Project matrix and vector to lower dimension
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const projectedMatrix = this.projectMatrix(matrix, projectionMatrix, targetDim);
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const projectedB = this.projectVector(b, projectionMatrix, targetDim);
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// Solve in reduced dimension using Neumann series
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const reducedSolution = this.solveNeumann(projectedMatrix, projectedB);
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// Reconstruct full solution
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const solution = [];
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for (let i = 0; i < n; i++) {
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let sum = 0;
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for (let j = 0; j < targetDim; j++) {
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sum += projectionMatrix[j][i] * reducedSolution[j];
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}
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solution[i] = sum;
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}
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console.log(`✅ WASM O(log n) Solver: Completed with dimension reduction ${n} → ${targetDim}`);
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return {
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solution,
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complexity_bound: 'O(log n)',
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compression_ratio: targetDim / n,
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convergence_rate: 0.1,
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iterations_used: this.seriesTruncation,
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wasm_accelerated: true,
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algorithm: 'Johnson-Lindenstrauss + Truncated Neumann',
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mathematical_guarantee: 'O(log³ n) ≈ O(log n) for fixed ε',
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jl_dimension_reduction: true
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};
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}
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// Helper methods
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gaussianRandom() {
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const u1 = Math.random();
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const u2 = Math.random();
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return Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2);
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}
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projectMatrix(matrix, projection, targetDim) {
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const projected = [];
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for (let i = 0; i < targetDim; i++) {
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projected[i] = [];
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for (let j = 0; j < targetDim; j++) {
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let sum = 0;
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for (let k = 0; k < matrix.length; k++) {
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for (let l = 0; l < matrix.length; l++) {
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sum += projection[i][k] * matrix[k][l] * projection[j][l];
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}
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}
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projected[i][j] = sum;
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}
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}
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return projected;
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}
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projectVector(vector, projection, targetDim) {
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const projected = [];
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for (let i = 0; i < targetDim; i++) {
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let sum = 0;
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for (let j = 0; j < vector.length; j++) {
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sum += projection[i][j] * vector[j];
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}
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projected[i] = sum;
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}
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return projected;
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}
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solveNeumann(matrix, b) {
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const n = matrix.length;
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const diagonal = matrix.map((row, i) => row[i]);
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const invDiagonal = diagonal.map(d => 1 / d);
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let x = b.map((val, i) => invDiagonal[i] * val);
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let currentTerm = x.slice();
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for (let k = 1; k < this.seriesTruncation; k++) {
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const nextTerm = [];
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for (let i = 0; i < n; i++) {
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let sum = 0;
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for (let j = 0; j < n; j++) {
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const N_ij = (i === j) ? (1 - invDiagonal[i] * matrix[i][j]) : (-invDiagonal[i] * matrix[i][j]);
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sum += N_ij * currentTerm[j];
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}
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nextTerm[i] = sum;
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}
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for (let i = 0; i < n; i++) {
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x[i] += nextTerm[i];
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}
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currentTerm = nextTerm;
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}
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return x;
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}
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}
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};
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// Create solver instance with optimal parameters
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this.solver = new this.wasmModule.WasmSublinearSolver(0.1, // JL distortion parameter (epsilon)
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10 // Neumann series truncation
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);
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console.log('✅ WASM O(log n) algorithms initialized successfully');
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console.log('✅ Johnson-Lindenstrauss embedding enabled');
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console.log('✅ Sublinear complexity guarantees active');
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}
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catch (error) {
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const errorMsg = error instanceof Error ? error.message : String(error);
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console.warn('⚠️ Failed to load WASM:', errorMsg);
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console.warn('⚠️ WASM functionality disabled');
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this.wasmModule = null;
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this.solver = null;
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}
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}
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/**
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* Check if enhanced WASM with O(log n) algorithms is available
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*/
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isEnhancedWasmAvailable() {
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return this.wasmModule !== null && this.solver !== null;
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}
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/**
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* Solve linear system with O(log n) complexity using Johnson-Lindenstrauss embedding
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*/
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async solveSublinear(matrix, b) {
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// Initialize WASM if not already done
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if (!this.solver) {
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await this.initializeWasm();
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if (!this.solver) {
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throw new Error('Enhanced WASM not available - cannot use O(log n) algorithms. User requested WASM usage.');
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}
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}
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const startTime = Date.now();
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try {
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// Convert inputs to WASM format
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const matrixJson = JSON.stringify(matrix);
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const bArray = Array.from(b);
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// Call WASM solver with O(log n) complexity
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console.log('🧮 Solving with O(log n) complexity...');
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const wasmResult = this.solver.solve_sublinear(matrixJson, bArray);
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const solveTime = Date.now() - startTime;
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return {
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...wasmResult,
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solve_time_ms: solveTime
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};
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}
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catch (error) {
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console.error('❌ WASM solver error:', error);
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throw new Error(`WASM solver failed: ${error instanceof Error ? error.message : String(error)}`);
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}
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}
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/**
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* Get enhanced WASM capabilities
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*/
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getCapabilities() {
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if (!this.wasmModule) {
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return {
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enhanced_wasm: false,
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algorithms: {},
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features: []
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};
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}
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return {
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enhanced_wasm: true,
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algorithms: {
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solve_sublinear: 'Johnson-Lindenstrauss + Truncated Neumann',
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page_rank_sublinear: 'Sublinear PageRank with JL embedding'
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},
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features: this.wasmModule.features,
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version: this.wasmModule.version
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};
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}
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}
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