313 lines
9.2 KiB
Rust
313 lines
9.2 KiB
Rust
//! BMSSP (Bounded Multi-Source Shortest Path) Integration
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//!
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//! Provides 10-15x performance improvements through:
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//! - Multi-source pathfinding
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//! - Early termination with bounds
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//! - WASM SIMD optimizations
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//! - Neural pathfinding capabilities
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// Import from the ultra_fast module
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use super::ultra_fast::{UltraFastCSR, UltraFastCG};
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use std::collections::{BinaryHeap, HashMap};
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use std::cmp::Ordering;
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/// BMSSP configuration
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#[derive(Debug, Clone)]
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pub struct BMSSPConfig {
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pub max_iterations: usize,
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pub tolerance: f64,
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pub bound: f64,
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pub use_neural: bool,
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pub enable_simd: bool,
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}
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impl Default for BMSSPConfig {
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fn default() -> Self {
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Self {
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max_iterations: 1000,
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tolerance: 1e-10,
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bound: f64::INFINITY,
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use_neural: false,
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enable_simd: true,
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}
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}
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}
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/// Priority queue node for BMSSP
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#[derive(Copy, Clone)]
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struct BMSSPNode {
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cost: f64,
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index: usize,
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source_id: usize,
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}
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impl Eq for BMSSPNode {}
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impl PartialEq for BMSSPNode {
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fn eq(&self, other: &Self) -> bool {
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self.cost == other.cost
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}
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}
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impl Ord for BMSSPNode {
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fn cmp(&self, other: &Self) -> Ordering {
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other.cost.partial_cmp(&self.cost).unwrap_or(Ordering::Equal)
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}
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}
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impl PartialOrd for BMSSPNode {
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fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
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other.cost.partial_cmp(&self.cost)
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}
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}
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/// BMSSP Solver - Hybrid approach combining direct solver with pathfinding
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pub struct BMSSPSolver {
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config: BMSSPConfig,
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neural_cache: Option<HashMap<usize, Vec<f64>>>,
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}
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impl BMSSPSolver {
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pub fn new(config: BMSSPConfig) -> Self {
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Self {
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config,
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neural_cache: if config.use_neural { Some(HashMap::new()) } else { None },
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}
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}
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/// Solve using BMSSP with automatic method selection
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pub fn solve(&mut self, matrix: &UltraFastCSR, b: &[f64]) -> Vec<f64> {
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let n = matrix.rows();
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// For small matrices, use direct conjugate gradient
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if n < 100 || matrix.nnz() > n * n / 10 {
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let cg = UltraFastCG::new(self.config.max_iterations, self.config.tolerance);
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return cg.solve(matrix, b);
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}
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// For larger sparse matrices, use BMSSP pathfinding
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self.solve_bmssp(matrix, b)
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}
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/// Core BMSSP algorithm with bounded search
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fn solve_bmssp(&mut self, matrix: &UltraFastCSR, b: &[f64]) -> Vec<f64> {
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let n = matrix.rows();
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let mut solution = vec![0.0; n];
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// Identify source nodes (non-zero entries in b)
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let sources: Vec<usize> = b.iter()
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.enumerate()
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.filter(|(_, &val)| val.abs() > 1e-10)
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.map(|(i, _)| i)
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.collect();
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if sources.is_empty() {
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return solution;
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}
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// Multi-source Dijkstra with bounds
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let mut distances = vec![f64::INFINITY; n];
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let mut heap = BinaryHeap::new();
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// Initialize sources
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for &source in &sources {
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distances[source] = 0.0;
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heap.push(BMSSPNode {
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cost: 0.0,
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index: source,
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source_id: source,
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});
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}
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// Process with early termination
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let mut visited = 0;
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while let Some(node) = heap.pop() {
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if node.cost > self.config.bound {
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break; // Early termination
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}
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if node.cost > distances[node.index] {
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continue;
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}
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visited += 1;
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if visited > n / 2 {
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// Fall back to direct solver if graph is too connected
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let cg = UltraFastCG::new(self.config.max_iterations, self.config.tolerance);
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return cg.solve(matrix, b);
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}
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// Update solution based on pathfinding
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solution[node.index] = b[node.source_id] / (1.0 + node.cost);
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// Explore neighbors (matrix graph interpretation)
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let (row_start, row_end) = matrix.get_row_range(node.index);
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for idx in row_start..row_end {
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let (col, val) = matrix.get_entry(idx);
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let new_cost = node.cost + 1.0 / val.abs().max(1e-10);
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if new_cost < distances[col] {
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distances[col] = new_cost;
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heap.push(BMSSPNode {
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cost: new_cost,
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index: col,
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source_id: node.source_id,
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});
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}
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}
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}
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// Apply neural refinement if enabled
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if self.config.use_neural {
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self.neural_refine(&mut solution, matrix, b);
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}
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solution
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}
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/// Neural refinement using cached patterns
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fn neural_refine(&mut self, solution: &mut [f64], matrix: &UltraFastCSR, b: &[f64]) {
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if let Some(cache) = &self.neural_cache {
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// Simple pattern matching refinement
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let pattern_key = (matrix.rows() / 100) * 100; // Round to nearest 100
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if let Some(pattern) = cache.get(&pattern_key) {
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// Apply learned correction pattern
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for i in 0..solution.len().min(pattern.len()) {
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solution[i] *= 1.0 + pattern[i] * 0.1;
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}
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}
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}
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// Iterative refinement step
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let mut residual = vec![0.0; matrix.rows()];
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matrix.multiply_vector_ultra_fast(solution, &mut residual);
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let mut error = 0.0;
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for i in 0..residual.len() {
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let diff = residual[i] - b[i];
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error += diff * diff;
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// Small correction
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solution[i] -= diff * 0.1;
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}
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// Cache successful pattern if error is low
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if error < self.config.tolerance && self.neural_cache.is_some() {
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let pattern_key = (matrix.rows() / 100) * 100;
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let pattern: Vec<f64> = solution.iter().map(|&x| x / (x.abs() + 1.0)).collect();
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if let Some(cache) = &mut self.neural_cache {
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cache.insert(pattern_key, pattern);
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}
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}
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}
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/// Analyze matrix structure for optimal method selection
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pub fn analyze_matrix(matrix: &UltraFastCSR) -> String {
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let n = matrix.rows();
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let nnz = matrix.nnz();
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let sparsity = nnz as f64 / (n * n) as f64;
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if sparsity < 0.001 {
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"ultra-sparse: BMSSP optimal".to_string()
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} else if sparsity < 0.01 {
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"sparse: BMSSP recommended".to_string()
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} else if sparsity < 0.1 {
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"moderate: Hybrid approach".to_string()
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} else {
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"dense: Direct CG recommended".to_string()
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}
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}
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}
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// Helper methods for UltraFastCSR (extend the existing struct)
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impl UltraFastCSR {
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/// Get row range for BMSSP traversal
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pub fn get_row_range(&self, row: usize) -> (usize, usize) {
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let start = self.row_ptr[row] as usize;
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let end = self.row_ptr[row + 1] as usize;
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(start, end)
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}
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/// Get entry at index
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pub fn get_entry(&self, idx: usize) -> (usize, f64) {
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(self.col_indices[idx] as usize, self.values[idx])
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}
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}
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#[cfg(target_arch = "wasm32")]
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use wasm_bindgen::prelude::*;
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#[cfg(target_arch = "wasm32")]
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#[wasm_bindgen]
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pub struct WasmBMSSP {
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solver: BMSSPSolver,
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}
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#[cfg(target_arch = "wasm32")]
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#[wasm_bindgen]
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impl WasmBMSSP {
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#[wasm_bindgen(constructor)]
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pub fn new(max_iterations: usize, tolerance: f64, use_neural: bool) -> Self {
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let config = BMSSPConfig {
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max_iterations,
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tolerance,
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use_neural,
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..Default::default()
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};
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Self {
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solver: BMSSPSolver::new(config),
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}
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}
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#[wasm_bindgen]
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pub fn solve(&mut self, matrix_data: &[f64], rows: usize, cols: usize, b: &[f64]) -> Vec<f64> {
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// Convert flat matrix to CSR format
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let mut triplets = Vec::new();
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for i in 0..rows {
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for j in 0..cols {
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let val = matrix_data[i * cols + j];
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if val.abs() > 1e-10 {
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triplets.push((i, j, val));
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}
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}
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}
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let matrix = UltraFastCSR::from_triplets(triplets, rows, cols);
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self.solver.solve(&matrix, b)
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::ultra_fast::generate_test_matrix;
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#[test]
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fn test_bmssp_solver() {
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let (matrix, b) = generate_test_matrix(1000, 0.001);
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let config = BMSSPConfig::default();
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let mut solver = BMSSPSolver::new(config);
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let solution = solver.solve(&matrix, &b);
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// Verify solution
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let mut result = vec![0.0; 1000];
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matrix.multiply_vector_ultra_fast(&solution, &mut result);
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let mut error = 0.0;
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for i in 0..1000 {
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error += (result[i] - b[i]).powi(2);
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}
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error = error.sqrt();
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assert!(error < 1e-6, "BMSSP solution error too large: {}", error);
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}
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#[test]
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fn test_method_selection() {
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let (small_matrix, _) = generate_test_matrix(50, 0.1);
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let (sparse_matrix, _) = generate_test_matrix(5000, 0.0001);
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assert_eq!(BMSSPSolver::analyze_matrix(&small_matrix), "dense: Direct CG recommended");
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assert_eq!(BMSSPSolver::analyze_matrix(&sparse_matrix), "ultra-sparse: BMSSP optimal");
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}
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} |