feat: vendor midstream and sublinear-time-solver libraries

Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/. Co-Authored-By: claude-flow <ruv@ruv.net>
2026-08-11 20:41:44 +00:00 · 2026-03-02 23:32:45 -05:00
parent 14902e6b4e
commit e91bb8a1d5
1600 changed files with 1852646 additions and 0 deletions
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+//! Demo of temporal computational lead
+
+fn main() {
+    println!("Temporal Computational Lead Demonstration");
+    println!("=========================================\n");
+
+    // Tokyo to NYC scenario
+    let distance_km = 10_900.0;
+    let light_time_ms = distance_km / 299_792.458; // Speed of light in km/s
+
+    println!("Scenario: Tokyo → NYC Financial Trading");
+    println!("Distance: {} km", distance_km);
+    println!("Light travel time: {:.1} ms", light_time_ms);
+
+    // Sublinear solver performance
+    let matrix_size: u32 = 1000;
+    let queries = ((matrix_size as f64).log2() * 100.0) as usize;
+    let computation_time_us = queries as f64 * 0.001; // μs per query
+
+    println!("\nMatrix: {}×{} diagonally dominant", matrix_size, matrix_size);
+    println!("Queries (sublinear): {}", queries);
+    println!("Computation time: {:.3} μs", computation_time_us);
+
+    // Temporal advantage
+    let advantage_ms = light_time_ms - (computation_time_us / 1000.0);
+    let effective_velocity = light_time_ms / (computation_time_us / 1000.0);
+
+    println!("\nResults:");
+    println!("✓ Temporal computational lead: {:.1} ms", advantage_ms);
+    println!("✓ Effective velocity: {:.0}× speed of light", effective_velocity);
+
+    println!("\nKey insight:");
+    println!("We compute t^T x* using local model structure in O(poly(1/ε, 1/δ))");
+    println!("This is prediction from local data, NOT faster-than-light signaling");
+
+    // Show complexity table
+    println!("\nComplexity Comparison:");
+    println!("Traditional O(n³): {} operations", matrix_size.pow(3));
+    println!("Sublinear O(log n): {} queries", queries);
+    println!("Speedup: {}×", matrix_size.pow(3) / queries as u32);
+}