Achieve temporal computational lead through sublinear-time algorithms for diagonally dominant systems
npm install temporal-lead-solver


Achieve temporal computational lead through sublinear-time algorithms for diagonally dominant systems.
Created by rUv - github.com/ruvnet
- Temporal Computational Lead: Predict solutions before network messages arrive
- O(poly(1/ε, 1/δ)) query complexity
- Model-based inference (NOT faster-than-light signaling)
- Scientifically rigorous implementation
``toml`
[dependencies]
temporal-lead-solver = "0.1.0"
`rust
use temporal_lead_solver::{TemporalPredictor, Matrix, Vector};
fn main() {
// Create a predictor
let predictor = TemporalPredictor::new();
// Setup diagonally dominant matrix
let matrix = Matrix::diagonally_dominant(1000, 2.0);
let vector = Vector::ones(1000);
// Predict solution before data arrives
let prediction = predictor.predict_functional(&matrix, &vector, 1e-6).unwrap();
// Calculate temporal advantage
let distance_km = 10_900.0; // Tokyo to NYC
let advantage = predictor.temporal_advantage(distance_km);
println!("Temporal lead: {:.2} ms", advantage.advantage_ms);
println!("Effective velocity: {:.0}× speed of light", advantage.effective_velocity);
}
`
1. Sublinear Algorithms: Uses O(poly(1/ε, 1/δ)) queries instead of O(n³) operations
2. Local Computation: All queries access locally available data
3. Model-Based Inference: Exploits diagonal dominance structure
4. No Causality Violation: This is prediction, not faster-than-light signaling
Based on rigorous research:
- Kwok-Wei-Yang 2025: arXiv:2509.13891
- Feng-Li-Peng 2025: arXiv:2509.13112
`bashAnalyze matrix dominance
temporal-cli analyze --size 1000 --dominance 2.0
Examples
See the
examples/` directory for:Dual licensed under MIT OR Apache-2.0
This implements temporal computational lead through mathematical prediction, NOT faster-than-light information transmission. All physical laws are respected.