mirror of
https://github.com/ruvnet/RuView
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feat: vendor midstream and sublinear-time-solver libraries (#109)
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/.
This commit is contained in:
rUv
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# 🦀 Sublinear-Time-Solver Rust Crate
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[](https://crates.io/crates/sublinear-time-solver)
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[](https://docs.rs/sublinear-time-solver)
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[](https://opensource.org/licenses/MIT)
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://www.rust-lang.org/)
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[](https://github.com/your-org/sublinear-time-solver/actions)
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> High-performance Rust implementation of sublinear-time algorithms for solving asymmetric diagonally dominant linear systems with O(log^k n) complexity
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## 🚀 Quick Start
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Add this to your `Cargo.toml`:
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```toml
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[dependencies]
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sublinear-time-solver = "0.1.0"
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```
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## 📖 Basic Usage
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```rust
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use sublinear_solver::{Solver, SolverMethod, Matrix, Vector};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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// Create a simple 3x3 diagonally dominant system
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// 4x + y = 5
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// x + 3y - z = 4
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// -y + 2z = 3
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let matrix = Matrix::from_dense(vec![
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vec![4.0, 1.0, 0.0],
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vec![1.0, 3.0, -1.0],
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vec![0.0, -1.0, 2.0],
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])?;
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let rhs = Vector::from_slice(&[5.0, 4.0, 3.0]);
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// Create solver and solve
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let solver = Solver::new();
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let solution = solver.solve(&matrix, &rhs, SolverMethod::ConjugateGradient)?;
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println!("Solution: {:?}", solution.vector());
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// Output: Solution: [1.0, 1.0, 2.0]
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Ok(())
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}
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```
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## ⚡ High-Performance Features
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### Sparse Matrix Support
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```rust
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use sublinear_solver::{SparseMatrix, CooMatrix};
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// Efficient sparse matrix representation
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let mut matrix = SparseMatrix::new(1000, 1000);
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matrix.insert(0, 0, 4.0);
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matrix.insert(0, 1, 1.0);
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matrix.insert(1, 0, 1.0);
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matrix.insert(1, 1, 3.0);
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// Or use COO format for bulk construction
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let coo = CooMatrix::from_triplets(
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vec![0, 0, 1, 1], // row indices
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vec![0, 1, 0, 1], // column indices
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vec![4.0, 1.0, 1.0, 3.0], // values
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1000, 1000
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);
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```
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### Streaming Solutions
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```rust
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use sublinear_solver::{StreamingSolver, ConvergenceOptions};
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let streaming_solver = StreamingSolver::new(ConvergenceOptions {
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tolerance: 1e-8,
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max_iterations: 1000,
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convergence_check_interval: 10,
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});
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// Get intermediate results as solver converges
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for step in streaming_solver.solve_stream(&matrix, &rhs, SolverMethod::Jacobi)? {
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println!("Iteration {}: residual = {:.2e}", step.iteration, step.residual);
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if step.iteration % 100 == 0 {
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println!("Intermediate solution: {:?}", step.current_solution);
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}
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}
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```
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### Parallel Processing
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```rust
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use sublinear_solver::{ParallelSolver, ThreadPoolConfig};
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// Enable parallel processing with custom thread pool
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let parallel_solver = ParallelSolver::new(ThreadPoolConfig {
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num_threads: 8,
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chunk_size: 1000,
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enable_simd: true,
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});
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let solution = parallel_solver.solve_parallel(&matrix, &rhs, SolverMethod::Hybrid)?;
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```
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## 🧮 Algorithm Selection
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### Available Methods
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```rust
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use sublinear_solver::SolverMethod;
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// Choose based on your matrix properties
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let method = match matrix_properties {
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// Symmetric positive definite matrices
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MatrixType::SymmetricPD => SolverMethod::ConjugateGradient,
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// Large sparse matrices with fast convergence needs
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MatrixType::SparseLarge => SolverMethod::Neumann,
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// When you only need specific solution entries
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MatrixType::LocalizedSolution => SolverMethod::ForwardPush,
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// Graph-like matrices (PageRank, network flow)
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MatrixType::GraphBased => SolverMethod::BackwardPush,
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// General case - automatically selects best method
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_ => SolverMethod::Hybrid,
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};
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```
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### Method Comparison
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| Method | Time Complexity | Memory | Best For |
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|--------|----------------|---------|----------|
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| `Neumann` | O(log n) | O(nnz) | Well-conditioned sparse |
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| `ForwardPush` | O(1/ε) | O(nnz) | Localized solutions |
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| `BackwardPush` | O(1/ε) | O(nnz) | Graph problems |
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| `ConjugateGradient` | O(√n log n) | O(nnz) | Symmetric matrices |
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| `Jacobi` | O(log n) | O(n) | Simple iteration |
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| `GaussSeidel` | O(log n) | O(n) | Sequential updates |
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| `Hybrid` | O(log n) | O(nnz) | Automatic selection |
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## 🎯 Advanced Usage
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### Custom Convergence Criteria
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```rust
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use sublinear_solver::{ConvergenceOptions, ResidualType};
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let options = ConvergenceOptions::builder()
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.tolerance(1e-10)
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.max_iterations(5000)
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.residual_type(ResidualType::Relative)
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.convergence_history(true)
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.early_stopping_patience(50)
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.build();
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let solution = solver.solve_with_options(&matrix, &rhs, SolverMethod::Hybrid, options)?;
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// Access convergence information
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println!("Converged in {} iterations", solution.iterations());
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println!("Final residual: {:.2e}", solution.residual());
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println!("Convergence history: {:?}", solution.convergence_history());
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```
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### Matrix Analysis
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```rust
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use sublinear_solver::analysis::{MatrixAnalyzer, ConditionEstimator};
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let analyzer = MatrixAnalyzer::new(&matrix);
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// Check matrix properties
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println!("Is diagonally dominant: {}", analyzer.is_diagonally_dominant());
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println!("Sparsity: {:.2}%", analyzer.sparsity() * 100.0);
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println!("Condition number estimate: {:.2e}", analyzer.condition_estimate());
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// Get recommendations
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let recommendation = analyzer.recommend_method();
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println!("Recommended method: {:?}", recommendation.method);
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println!("Expected convergence: {} iterations", recommendation.estimated_iterations);
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```
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### Error Handling and Diagnostics
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```rust
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use sublinear_solver::{SolverError, DiagnosticInfo};
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match solver.solve(&matrix, &rhs, SolverMethod::ConjugateGradient) {
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Ok(solution) => {
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println!("Solution: {:?}", solution.vector());
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// Check solution quality
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let residual = matrix.residual(&solution.vector(), &rhs);
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println!("Solution residual: {:.2e}", residual);
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}
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Err(SolverError::ConvergenceFailure { iterations, residual, diagnostic }) => {
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eprintln!("Failed to converge after {} iterations", iterations);
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eprintln!("Final residual: {:.2e}", residual);
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if let Some(info) = diagnostic {
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eprintln!("Diagnostic: {}", info.message());
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eprintln!("Suggested action: {}", info.suggestion());
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}
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}
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Err(SolverError::InvalidMatrix { reason }) => {
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eprintln!("Matrix validation failed: {}", reason);
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}
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Err(e) => {
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eprintln!("Solver error: {}", e);
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}
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}
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```
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## 🌐 WebAssembly Integration
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Enable WASM features for browser/Node.js deployment:
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```toml
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[dependencies]
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sublinear-time-solver = { version = "0.1.0", features = ["wasm"] }
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```
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```rust
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#[cfg(feature = "wasm")]
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use wasm_bindgen::prelude::*;
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#[cfg(feature = "wasm")]
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#[wasm_bindgen]
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pub struct WasmSolver {
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inner: Solver,
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}
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#[cfg(feature = "wasm")]
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#[wasm_bindgen]
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impl WasmSolver {
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#[wasm_bindgen(constructor)]
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pub fn new() -> WasmSolver {
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WasmSolver {
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inner: Solver::new(),
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}
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}
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#[wasm_bindgen]
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pub fn solve_dense(&self, matrix: &[f64], rhs: &[f64], size: usize) -> Vec<f64> {
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// Implementation for WASM interface
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// ...
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}
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}
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```
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## 🔧 Feature Flags
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```toml
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[dependencies]
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sublinear-time-solver = { version = "0.1.0", features = [
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"wasm", # WebAssembly support
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"parallel", # Multi-threading with rayon
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"simd", # SIMD optimizations
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"serde", # Serialization support
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"cli" # Command-line interface
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] }
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```
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### Available Features
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- **`default`**: Standard library support, basic serialization
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- **`std`**: Full standard library (enabled by default)
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- **`wasm`**: WebAssembly bindings and browser compatibility
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- **`parallel`**: Multi-threaded operations with rayon
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- **`simd`**: SIMD vectorization for numerical operations
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- **`serde`**: Serialize/deserialize matrices and solutions
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- **`cli`**: Command-line interface tools
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## 📊 Performance Benchmarks
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### Rust vs Other Implementations
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```
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Matrix Size: 100,000 × 100,000 (0.1% sparsity)
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Hardware: AMD Ryzen 9 5950X, 32GB RAM
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Implementation Time Memory Accuracy
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─────────────────────────────────────────────────────
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sublinear-solver (Rust) 145ms 58MB 1.2e-8
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NumPy (Python) 8.2s 2.1GB 1.1e-8
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SciPy sparse 2.1s 340MB 1.3e-8
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Eigen (C++) 890ms 120MB 1.1e-8
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```
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### Scaling Performance
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```rust
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// Benchmark different matrix sizes
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use criterion::{black_box, criterion_group, criterion_main, Criterion};
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fn benchmark_scaling(c: &mut Criterion) {
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let sizes = vec![1000, 10000, 100000, 1000000];
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for &size in &sizes {
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let matrix = generate_sparse_dd_matrix(size, 0.001); // 0.1% sparsity
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let rhs = Vector::random(size);
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c.bench_function(&format!("solve_{}", size), |b| {
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b.iter(|| {
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let solver = Solver::new();
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solver.solve(black_box(&matrix), black_box(&rhs), SolverMethod::Hybrid)
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})
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});
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}
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}
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criterion_group!(benches, benchmark_scaling);
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criterion_main!(benches);
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```
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## 🤖 Integration Examples
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### Multi-Agent Systems
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```rust
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use sublinear_solver::{Solver, SparseMatrix};
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struct SwarmCoordinator {
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solver: Solver,
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communication_matrix: SparseMatrix,
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agent_states: Vec<f64>,
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}
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impl SwarmCoordinator {
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pub fn new(num_agents: usize) -> Self {
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let comm_matrix = build_communication_graph(num_agents);
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Self {
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solver: Solver::new(),
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communication_matrix: comm_matrix,
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agent_states: vec![0.0; num_agents],
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}
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}
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pub fn coordinate(&mut self, target_states: &[f64]) -> Result<Vec<f64>, SolverError> {
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// Solve consensus problem: L * x = target_states
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// where L is the graph Laplacian
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let laplacian = self.communication_matrix.to_laplacian();
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let solution = self.solver.solve(
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&laplacian,
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&Vector::from_slice(target_states),
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SolverMethod::ForwardPush
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)?;
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self.agent_states = solution.vector().to_vec();
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Ok(self.agent_states.clone())
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}
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}
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```
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### Machine Learning Integration
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```rust
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use sublinear_solver::{Solver, Matrix, Vector, SolverMethod};
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struct OnlineLinearRegression {
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solver: Solver,
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feature_matrix: Matrix,
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targets: Vector,
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weights: Option<Vector>,
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regularization: f64,
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}
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impl OnlineLinearRegression {
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pub fn new(regularization: f64) -> Self {
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Self {
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solver: Solver::new(),
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feature_matrix: Matrix::empty(),
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targets: Vector::empty(),
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weights: None,
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regularization,
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}
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}
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pub fn update(&mut self, features: &[f64], target: f64) -> Result<(), SolverError> {
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// Add new sample to dataset
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self.feature_matrix.add_row(features);
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self.targets.push(target);
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// Solve regularized normal equations: (X^T X + λI) w = X^T y
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let xtx = self.feature_matrix.transpose().multiply(&self.feature_matrix);
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let regularized = xtx.add_diagonal(self.regularization);
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let xty = self.feature_matrix.transpose().multiply_vector(&self.targets);
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let solution = self.solver.solve(®ularized, &xty, SolverMethod::ConjugateGradient)?;
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self.weights = Some(solution.vector().clone());
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Ok(())
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}
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pub fn predict(&self, features: &[f64]) -> Option<f64> {
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self.weights.as_ref().map(|w| {
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features.iter().zip(w.iter()).map(|(f, w)| f * w).sum()
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})
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}
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}
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```
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## 🧪 Testing
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```bash
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# Run all tests
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cargo test
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# Run with specific features
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cargo test --features "parallel,simd"
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# Run benchmarks
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cargo bench
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# Test WASM build
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wasm-pack test --node
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# Property-based testing
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cargo test --features "proptest"
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```
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## 📚 API Documentation
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Generate and view complete API documentation:
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```bash
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cargo doc --open --features "parallel,simd,wasm"
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```
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## 🔗 Integration with Other Crates
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### nalgebra
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```rust
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use nalgebra::{DMatrix, DVector};
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use sublinear_solver::Solver;
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// Convert from nalgebra types
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let nalgebra_matrix = DMatrix::from_fn(3, 3, |i, j| if i == j { 4.0 } else { 1.0 });
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let nalgebra_vector = DVector::from_vec(vec![1.0, 2.0, 3.0]);
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let matrix = Matrix::from_nalgebra(&nalgebra_matrix);
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let rhs = Vector::from_nalgebra(&nalgebra_vector);
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let solution = solver.solve(&matrix, &rhs, SolverMethod::ConjugateGradient)?;
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let result_nalgebra = solution.to_nalgebra();
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```
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### ndarray
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```rust
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use ndarray::{Array2, Array1};
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let ndarray_matrix = Array2::eye(1000) * 4.0 + Array2::ones((1000, 1000));
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let ndarray_rhs = Array1::ones(1000);
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let matrix = Matrix::from_ndarray(&ndarray_matrix);
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let rhs = Vector::from_ndarray(&ndarray_rhs);
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```
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## 🛠️ Development
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### Building from Source
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```bash
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# Clone the repository
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git clone https://github.com/your-org/sublinear-time-solver
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cd sublinear-time-solver
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# Build with all features
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cargo build --release --all-features
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# Build for WASM
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wasm-pack build --target nodejs --out-dir js/pkg
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# Run benchmarks
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cargo bench --features "parallel,simd"
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```
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### Project Structure
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```
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src/
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├── lib.rs # Main library entry point
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├── solver/ # Core solver implementations
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│ ├── mod.rs
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│ ├── neumann.rs # Neumann series method
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│ ├── push.rs # Forward/backward push methods
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│ ├── jacobi.rs # Jacobi iteration
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│ ├── cg.rs # Conjugate gradient
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│ └── hybrid.rs # Hybrid method selection
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├── matrix/ # Matrix representations and operations
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│ ├── mod.rs
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│ ├── sparse.rs # Sparse matrix formats
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│ ├── dense.rs # Dense matrix operations
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│ └── analysis.rs # Matrix analysis tools
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├── vector/ # Vector operations
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├── error.rs # Error types and handling
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├── streaming.rs # Streaming solver interface
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├── parallel.rs # Parallel processing utilities
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└── wasm.rs # WebAssembly bindings
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```
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## 🤝 Contributing
|
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|
||||
Contributions are welcome! Please see our [Contributing Guide](../CONTRIBUTING.md) for details.
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|
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### Development Checklist
|
||||
|
||||
- [ ] Add tests for new functionality
|
||||
- [ ] Update documentation
|
||||
- [ ] Run `cargo fmt` and `cargo clippy`
|
||||
- [ ] Ensure all feature combinations compile
|
||||
- [ ] Add benchmarks for performance-critical code
|
||||
- [ ] Test WASM compatibility if applicable
|
||||
|
||||
## 📄 License
|
||||
|
||||
This project is dual-licensed under MIT OR Apache-2.0. See [LICENSE-MIT](../LICENSE-MIT) and [LICENSE-APACHE](../LICENSE-APACHE) for details.
|
||||
|
||||
## 🏆 Citation
|
||||
|
||||
If you use this solver in academic work, please cite:
|
||||
|
||||
```bibtex
|
||||
@software{sublinear_solver_2024,
|
||||
title = {Sublinear-Time Solver: High-Performance Algorithms for Large Sparse Linear Systems},
|
||||
author = {rUv},
|
||||
year = {2024},
|
||||
url = {https://github.com/your-org/sublinear-time-solver},
|
||||
version = {0.1.0}
|
||||
}
|
||||
```
|
||||
|
||||
## 🔗 Links
|
||||
|
||||
- [Crates.io](https://crates.io/crates/sublinear-time-solver)
|
||||
- [Documentation](https://docs.rs/sublinear-time-solver)
|
||||
- [GitHub Repository](https://github.com/your-org/sublinear-time-solver)
|
||||
- [npm Package](https://www.npmjs.com/package/sublinear-time-solver)
|
||||
- [Research Paper](https://arxiv.org/html/2509.13891v1)
|
||||
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
Made with ❤️ by rUv
|
||||
</div>
|
||||
Reference in New Issue
Block a user