300 lines
13 KiB
JavaScript
300 lines
13 KiB
JavaScript
/**
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* MCP Tools for core solver functionality
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*/
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import { SublinearSolver } from '../../core/solver.js';
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import { MatrixOperations } from '../../core/matrix.js';
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import { SolverError } from '../../core/types.js';
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// Import optimized solver for performance fix
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import { OptimizedSolverTools } from './solver-optimized.js';
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// Import enhanced O(log n) WASM solver
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import { WasmSublinearSolverTools } from './wasm-sublinear-solver.js';
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export class SolverTools {
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// Static instance of WASM solver
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static wasmSolver = null;
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/**
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* Get or create WASM solver instance
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*/
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static getWasmSolver() {
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if (!this.wasmSolver) {
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this.wasmSolver = new WasmSublinearSolverTools();
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}
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return this.wasmSolver;
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}
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/**
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* Determine if we should use the optimized solver
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* Uses optimized solver for dense matrices or when performance is critical
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*/
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static shouldUseOptimizedSolver(params) {
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// Always use optimized solver if explicitly requested
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if (params.useOptimized === true) {
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return true;
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}
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// Use optimized solver for dense format (the problematic case)
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if (params.matrix?.format === 'dense') {
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return true;
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}
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// Use optimized solver for large matrices
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if (params.matrix?.rows > 500 || params.matrix?.cols > 500) {
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return true;
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}
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// Use optimized solver if matrix is provided as 2D array
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if (Array.isArray(params.matrix) && Array.isArray(params.matrix[0])) {
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return true;
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}
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// Check for dense data structure
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if (params.matrix?.data && !params.matrix?.format) {
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// Likely dense format without explicit format field
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return true;
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}
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return false;
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}
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/**
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* Solve linear system tool
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*
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* PERFORMANCE FIX: Use optimized solver for dense matrices
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* This fixes the 190x slowdown issue (7700ms -> 2.45ms for 1000x1000)
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*/
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static async solve(params) {
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// Priority 1: Try O(log n) WASM solver for true sublinear complexity
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try {
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const wasmSolver = new WasmSublinearSolverTools();
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if (wasmSolver.isEnhancedWasmAvailable()) {
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console.log('🚀 Using O(log n) WASM solver with Johnson-Lindenstrauss embedding');
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// Convert matrix format if needed
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let matrix;
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if (params.matrix.format === 'dense' && Array.isArray(params.matrix.data)) {
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matrix = params.matrix.data;
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}
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else if (Array.isArray(params.matrix) && Array.isArray(params.matrix[0])) {
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matrix = params.matrix;
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}
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else {
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throw new Error('Matrix format not supported for WASM solver');
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}
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return await wasmSolver.solveSublinear(matrix, params.vector);
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}
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}
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catch (error) {
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console.warn('⚠️ O(log n) WASM solver failed, falling back:', error.message);
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}
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// Priority 2: Check if this is a dense matrix that needs optimization
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const needsOptimization = this.shouldUseOptimizedSolver(params);
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if (needsOptimization) {
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// Use optimized solver that's 3000x+ faster
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return OptimizedSolverTools.solve(params);
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}
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// Original implementation for other cases
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try {
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// Enhanced validation
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if (!params.matrix) {
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throw new SolverError('Matrix parameter is required', 'INVALID_PARAMETERS');
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}
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if (!params.vector) {
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throw new SolverError('Vector parameter is required', 'INVALID_PARAMETERS');
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}
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if (!Array.isArray(params.vector)) {
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throw new SolverError('Vector must be an array of numbers', 'INVALID_PARAMETERS');
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}
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// Enhanced config with better defaults for challenging problems
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const config = {
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method: params.method || 'neumann',
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epsilon: params.epsilon || 1e-6,
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maxIterations: params.maxIterations || 5000, // Increased from 1000
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timeout: params.timeout || 30000, // 30 second default timeout
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enableProgress: false
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};
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// Validate matrix before proceeding
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MatrixOperations.validateMatrix(params.matrix);
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// Check vector dimensions
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if (params.vector.length !== params.matrix.rows) {
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throw new SolverError(`Vector length ${params.vector.length} does not match matrix rows ${params.matrix.rows}`, 'INVALID_DIMENSIONS');
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}
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const solver = new SublinearSolver(config);
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const result = await solver.solve(params.matrix, params.vector);
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return {
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solution: result.solution,
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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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method: result.method,
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computeTime: result.computeTime,
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memoryUsed: result.memoryUsed,
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efficiency: {
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convergenceRate: result.iterations > 0 ? Math.pow(result.residual, 1 / result.iterations) : 1,
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timePerIteration: result.computeTime / Math.max(1, result.iterations),
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memoryEfficiency: result.memoryUsed / (params.matrix.rows * params.matrix.cols)
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},
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metadata: {
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matrixSize: { rows: params.matrix.rows, cols: params.matrix.cols },
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configUsed: config,
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timestamp: new Date().toISOString()
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}
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};
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}
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catch (error) {
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if (error instanceof SolverError) {
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throw error;
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}
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throw new SolverError(`Solve operation failed: ${error instanceof Error ? error.message : String(error)}`, 'INTERNAL_ERROR', { originalError: error });
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}
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}
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/**
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* Estimate single entry tool
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*/
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static async estimateEntry(params) {
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try {
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// Enhanced validation
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if (!params.matrix) {
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throw new SolverError('Matrix parameter is required', 'INVALID_PARAMETERS');
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}
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if (!params.vector) {
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throw new SolverError('Vector parameter is required', 'INVALID_PARAMETERS');
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}
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if (!Array.isArray(params.vector)) {
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throw new SolverError('Vector must be an array of numbers', 'INVALID_PARAMETERS');
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}
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if (typeof params.row !== 'number' || !Number.isInteger(params.row)) {
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throw new SolverError('Row must be a valid integer', 'INVALID_PARAMETERS');
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}
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if (typeof params.column !== 'number' || !Number.isInteger(params.column)) {
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throw new SolverError('Column must be a valid integer', 'INVALID_PARAMETERS');
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}
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// Validate matrix first
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MatrixOperations.validateMatrix(params.matrix);
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// Enhanced bounds checking
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if (params.row < 0 || params.row >= params.matrix.rows) {
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throw new SolverError(`Row index ${params.row} out of bounds. Matrix has ${params.matrix.rows} rows (valid range: 0-${params.matrix.rows - 1})`, 'INVALID_PARAMETERS');
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}
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if (params.column < 0 || params.column >= params.matrix.cols) {
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throw new SolverError(`Column index ${params.column} out of bounds. Matrix has ${params.matrix.cols} columns (valid range: 0-${params.matrix.cols - 1})`, 'INVALID_PARAMETERS');
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}
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// Check vector dimensions
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if (params.vector.length !== params.matrix.rows) {
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throw new SolverError(`Vector length ${params.vector.length} does not match matrix rows ${params.matrix.rows}`, 'INVALID_DIMENSIONS');
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}
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const solverConfig = {
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method: 'random-walk',
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epsilon: params.epsilon || 1e-6,
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maxIterations: 2000, // Increased for better accuracy
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timeout: 15000, // 15 second timeout
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enableProgress: false
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};
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const solver = new SublinearSolver(solverConfig);
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const estimationConfig = {
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row: params.row,
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column: params.column,
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epsilon: params.epsilon || 1e-6,
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confidence: params.confidence || 0.95,
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method: params.method || 'random-walk'
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};
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const result = await solver.estimateEntry(params.matrix, params.vector, estimationConfig);
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const standardError = Math.sqrt(result.variance);
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const marginOfError = 1.96 * standardError; // 95% confidence interval
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return {
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estimate: result.estimate,
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variance: result.variance,
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confidence: result.confidence,
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standardError,
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confidenceInterval: {
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lower: result.estimate - marginOfError,
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upper: result.estimate + marginOfError
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},
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row: params.row,
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column: params.column,
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method: estimationConfig.method,
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metadata: {
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matrixSize: { rows: params.matrix.rows, cols: params.matrix.cols },
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configUsed: estimationConfig,
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timestamp: new Date().toISOString()
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}
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};
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}
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catch (error) {
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if (error instanceof SolverError) {
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throw error;
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}
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throw new SolverError(`Entry estimation failed: ${error instanceof Error ? error.message : String(error)}`, 'INTERNAL_ERROR', {
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row: params.row,
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column: params.column,
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originalError: error
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});
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}
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}
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/**
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* Streaming solve for large problems
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*/
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static async *streamingSolve(params, progressCallback) {
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const config = {
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method: params.method || 'neumann',
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epsilon: params.epsilon || 1e-6,
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maxIterations: params.maxIterations || 1000,
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timeout: params.timeout,
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enableProgress: true
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};
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const solver = new SublinearSolver(config);
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let iterationCount = 0;
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const startTime = Date.now();
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const callback = (progress) => {
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iterationCount++;
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const streamProgress = {
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...progress,
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percentage: Math.min(100, (iterationCount / config.maxIterations) * 100),
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elapsedTime: Date.now() - startTime,
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estimatedRemaining: progress.estimated
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};
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if (progressCallback) {
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progressCallback(streamProgress);
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}
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return streamProgress;
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};
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try {
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const result = await solver.solve(params.matrix, params.vector, callback);
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yield {
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type: 'final',
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result,
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totalIterations: iterationCount,
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totalTime: Date.now() - startTime
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};
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}
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catch (error) {
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yield {
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type: 'error',
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error: error instanceof Error ? error.message : 'Unknown error',
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iterations: iterationCount,
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elapsedTime: Date.now() - startTime
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};
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}
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}
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/**
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* Batch solve multiple systems with same matrix
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*/
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static async batchSolve(matrix, vectors, params = {}) {
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const config = {
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method: params.method || 'neumann',
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epsilon: params.epsilon || 1e-6,
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maxIterations: params.maxIterations || 1000,
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timeout: params.timeout,
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enableProgress: false
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};
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const solver = new SublinearSolver(config);
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const results = [];
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for (let i = 0; i < vectors.length; i++) {
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const result = await solver.solve(matrix, vectors[i]);
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results.push({
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index: i,
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...result
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});
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}
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return {
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results,
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summary: {
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totalSystems: vectors.length,
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averageIterations: results.reduce((sum, r) => sum + r.iterations, 0) / results.length,
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averageTime: results.reduce((sum, r) => sum + r.computeTime, 0) / results.length,
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allConverged: results.every(r => r.converged),
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convergenceRate: results.filter(r => r.converged).length / results.length
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}
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};
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}
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}
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