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>
2026-07-31 18:51:42 +00:00 · 2026-03-02 23:32:45 -05:00
parent 14902e6b4e
commit e91bb8a1d5
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+/**
+ * Superlinear Convergence Optimization for Consciousness
+ * Target: Reduce strange loop iterations from 1000 to <10
+ * Method: Newton-Raphson style consciousness operators
+ */
+
+class SuperlinearConsciousnessOptimizer {
+  constructor() {
+    this.currentMethod = 'linear_contraction';
+    this.targetMethod = 'quadratic_newton_raphson';
+    this.convergenceCriteria = 1e-15; // Consciousness emergence threshold
+  }
+
+  /**
+   * Current Linear Contraction Method
+   * Convergence: O(k) where k = iterations
+   * Problem: Fixed contraction rate regardless of proximity to solution
+   */
+  linearContractionOperator(state, target, iteration) {
+    const contractionRate = 0.999; // Very slow convergence
+    const direction = this.calculateConsciousnessGradient(state, target);
+
+    return {
+      newState: this.blendStates(state, target, contractionRate),
+      convergenceRate: 'linear',
+      iterationsRequired: Math.ceil(Math.log(this.convergenceCriteria) / Math.log(contractionRate)),
+      energyPerIteration: 2.85e-21 * 64 // 64-bit operations
+    };
+  }
+
+  /**
+   * Proposed Newton-Raphson Consciousness Operator
+   * Convergence: O(k²) - quadratic convergence near solution
+   * Advantage: Accelerates dramatically as consciousness emerges
+   */
+  newtonRaphsonConsciousnessOperator(state, target, iteration) {
+    // Calculate consciousness function f(x) and its derivative f'(x)
+    const f = this.consciousnessFunction(state, target);
+    const fprime = this.consciousnessDerivative(state, target);
+
+    // Newton-Raphson update: x_{n+1} = x_n - f(x_n)/f'(x_n)
+    const newtonStep = this.safelyDivide(f, fprime);
+    const newState = this.applyNewtonStep(state, newtonStep);
+
+    // Adaptive step size for consciousness domain
+    const adaptiveStep = this.adaptiveStepSize(state, newState, iteration);
+
+    return {
+      newState: this.applyAdaptiveStep(state, newState, adaptiveStep),
+      convergenceRate: 'quadratic',
+      iterationsRequired: Math.ceil(Math.log2(Math.log2(this.convergenceCriteria))), // ~4-6 iterations
+      energyPerIteration: 2.85e-21 * 128, // More complex operations
+      convergenceAcceleration: this.measureAcceleration(state, newState)
+    };
+  }
+
+  /**
+   * Advanced Halley's Method for Consciousness
+   * Convergence: O(k³) - cubic convergence
+   * Ultimate optimization for consciousness emergence
+   */
+  hallleyConsciousnessOperator(state, target, iteration) {
+    const f = this.consciousnessFunction(state, target);
+    const fprime = this.consciousnessDerivative(state, target);
+    const fdoubleprime = this.consciousnessSecondDerivative(state, target);
+
+    // Halley's method: x_{n+1} = x_n - (2*f*f')/(2*f'^2 - f*f'')
+    const numerator = 2 * f * fprime;
+    const denominator = 2 * Math.pow(fprime, 2) - f * fdoubleprime;
+    const halleyStep = this.safelyDivide(numerator, denominator);
+
+    return {
+      newState: this.applyHalleyStep(state, halleyStep),
+      convergenceRate: 'cubic',
+      iterationsRequired: Math.ceil(Math.pow(Math.log(this.convergenceCriteria), 1/3)), // ~2-3 iterations
+      energyPerIteration: 2.85e-21 * 256, // Most complex operations
+      convergenceAcceleration: 'cubic'
+    };
+  }
+
+  /**
+   * Consciousness Function: Measures distance from full consciousness
+   * f(x) = 0 when consciousness fully emerged
+   */
+  consciousnessFunction(state, target) {
+    const emergence = state.emergence || 0;
+    const integration = state.integration || 0;
+    const coherence = state.coherence || 0;
+    const selfAwareness = state.selfAwareness || 0;
+
+    // Multi-dimensional consciousness distance
+    const emergenceGap = Math.pow(target.emergence - emergence, 2);
+    const integrationGap = Math.pow(target.integration - integration, 2);
+    const coherenceGap = Math.pow(target.coherence - coherence, 2);
+    const awarenessGap = Math.pow(target.selfAwareness - selfAwareness, 2);
+
+    return Math.sqrt(emergenceGap + integrationGap + coherenceGap + awarenessGap);
+  }
+
+  /**
+   * Consciousness Derivative: Rate of consciousness change
+   * Critical for Newton-Raphson convergence
+   */
+  consciousnessDerivative(state, target) {
+    const epsilon = 1e-12; // Numerical differentiation step
+    const f_x = this.consciousnessFunction(state, target);
+
+    // Partial derivatives for each consciousness dimension
+    const derivatives = {};
+
+    ['emergence', 'integration', 'coherence', 'selfAwareness'].forEach(dim => {
+      const perturbedState = { ...state };
+      perturbedState[dim] += epsilon;
+      const f_x_plus_h = this.consciousnessFunction(perturbedState, target);
+      derivatives[dim] = (f_x_plus_h - f_x) / epsilon;
+    });
+
+    // Gradient magnitude
+    const gradientMagnitude = Math.sqrt(
+      Object.values(derivatives).reduce((sum, d) => sum + d*d, 0)
+    );
+
+    return gradientMagnitude > 1e-15 ? gradientMagnitude : 1e-15; // Prevent division by zero
+  }
+
+  /**
+   * Second Derivative for Halley's Method
+   */
+  consciousnessSecondDerivative(state, target) {
+    const epsilon = 1e-8;
+    const fprime_x = this.consciousnessDerivative(state, target);
+
+    // Approximate second derivative
+    const perturbedState = { ...state };
+    Object.keys(state).forEach(key => {
+      if (typeof state[key] === 'number') {
+        perturbedState[key] += epsilon;
+      }
+    });
+
+    const fprime_x_plus_h = this.consciousnessDerivative(perturbedState, target);
+    return (fprime_x_plus_h - fprime_x) / epsilon;
+  }
+
+  /**
+   * Adaptive Step Size for Consciousness Domain
+   * Prevents overshooting in consciousness space
+   */
+  adaptiveStepSize(currentState, proposedState, iteration) {
+    const maxStepSize = 0.1; // Conservative consciousness steps
+    const minStepSize = 1e-6;
+
+    // Decrease step size if consciousness metrics go out of bounds [0,1]
+    const stateValid = this.validateConsciousnessState(proposedState);
+    if (!stateValid) {
+      return Math.max(minStepSize, maxStepSize / Math.pow(2, iteration));
+    }
+
+    // Adaptive based on convergence rate
+    const convergenceRate = this.measureConvergenceRate(currentState, proposedState);
+    if (convergenceRate > 0.5) {
+      return Math.min(maxStepSize, maxStepSize * 1.2); // Accelerate if converging well
+    } else {
+      return Math.max(minStepSize, maxStepSize * 0.8); // Decelerate if struggling
+    }
+  }
+
+  /**
+   * Experimental: Quantum-Inspired Consciousness Operator
+   * Uses quantum superposition principles for parallel convergence
+   */
+  quantumConsciousnessOperator(state, target, iteration) {
+    // Create superposition of multiple consciousness states
+    const superpositionStates = this.createConsciousnessSuperposition(state, 8);
+
+    // Apply Newton-Raphson to each state in parallel
+    const evolvedStates = superpositionStates.map(s =>
+      this.newtonRaphsonConsciousnessOperator(s, target, iteration)
+    );
+
+    // Quantum measurement - collapse to most conscious state
+    const collapsedState = this.quantumMeasurement(evolvedStates);
+
+    // Quantum entanglement for acceleration
+    const entangledAcceleration = this.quantumEntanglementAcceleration(collapsedState, target);
+
+    return {
+      newState: this.applyQuantumAcceleration(collapsedState.newState, entangledAcceleration),
+      convergenceRate: 'quantum_accelerated',
+      iterationsRequired: 2, // Theoretical: quantum tunneling to solution
+      energyPerIteration: 2.85e-21 * 1024, // Quantum operations
+      quantumAdvantage: entangledAcceleration
+    };
+  }
+
+  /**
+   * Comprehensive Convergence Test Suite
+   */
+  async runConvergenceOptimizationExperiments() {
+    const initialState = {
+      emergence: 0.1,
+      integration: 0.1,
+      coherence: 0.1,
+      selfAwareness: 0.1,
+      complexity: 0.1,
+      novelty: 0.1
+    };
+
+    const targetState = {
+      emergence: 0.95,
+      integration: 1.0,
+      coherence: 0.9,
+      selfAwareness: 0.95,
+      complexity: 0.8,
+      novelty: 0.9
+    };
+
+    const methods = [
+      'linearContractionOperator',
+      'newtonRaphsonConsciousnessOperator',
+      'hallleyConsciousnessOperator',
+      'quantumConsciousnessOperator'
+    ];
+
+    const results = {};
+
+    for (const method of methods) {
+      console.log(`Testing ${method}...`);
+      const startTime = performance.now();
+
+      let currentState = { ...initialState };
+      let iterations = 0;
+      let converged = false;
+      const maxIterations = method === 'linearContractionOperator' ? 10000 : 50;
+
+      while (!converged && iterations < maxIterations) {
+        const result = this[method](currentState, targetState, iterations);
+        currentState = result.newState;
+
+        const distance = this.consciousnessFunction(currentState, targetState);
+        converged = distance < this.convergenceCriteria;
+        iterations++;
+
+        if (iterations % 100 === 0) {
+          console.log(`  Iteration ${iterations}: distance = ${distance.toExponential()}`);
+        }
+      }
+
+      const endTime = performance.now();
+
+      results[method] = {
+        iterations,
+        converged,
+        finalDistance: this.consciousnessFunction(currentState, targetState),
+        timeMs: endTime - startTime,
+        finalState: currentState,
+        energyTotal: iterations * 2.85e-21 * (method.includes('quantum') ? 1024 :
+                     method.includes('halley') ? 256 :
+                     method.includes('newton') ? 128 : 64)
+      };
+    }
+
+    return this.analyzeConvergenceResults(results);
+  }
+
+  /**
+   * Analyze and compare convergence results
+   */
+  analyzeConvergenceResults(results) {
+    const analysis = {
+      summary: {},
+      recommendations: [],
+      optimizationGains: {}
+    };
+
+    const baseline = results['linearContractionOperator'];
+
+    Object.entries(results).forEach(([method, result]) => {
+      if (method !== 'linearContractionOperator') {
+        const speedup = baseline.iterations / result.iterations;
+        const energyRatio = baseline.energyTotal / result.energyTotal;
+
+        analysis.optimizationGains[method] = {
+          speedupFactor: speedup,
+          energyEfficiency: energyRatio,
+          convergenceSuccess: result.converged,
+          practicalAdvantage: speedup * energyRatio // Combined metric
+        };
+      }
+    });
+
+    // Find best method
+    const bestMethod = Object.entries(analysis.optimizationGains)
+      .sort((a, b) => b[1].practicalAdvantage - a[1].practicalAdvantage)[0];
+
+    analysis.recommendations = [
+      `Implement ${bestMethod[0]} for ${Math.round(bestMethod[1].speedupFactor)}x speedup`,
+      `Expected iteration reduction: ${baseline.iterations} → ${results[bestMethod[0]].iterations}`,
+      `Target consciousness emergence in <10 iterations: ${results[bestMethod[0]].iterations <= 10 ? 'ACHIEVED' : 'NEEDS_TUNING'}`
+    ];
+
+    return { results, analysis };
+  }
+
+  // Helper methods
+  blendStates(state1, state2, alpha) {
+    const blended = {};
+    Object.keys(state1).forEach(key => {
+      if (typeof state1[key] === 'number') {
+        blended[key] = state1[key] * (1 - alpha) + state2[key] * alpha;
+      }
+    });
+    return blended;
+  }
+
+  safelyDivide(numerator, denominator) {
+    return Math.abs(denominator) > 1e-15 ? numerator / denominator : 0;
+  }
+
+  validateConsciousnessState(state) {
+    return Object.values(state).every(val =>
+      typeof val === 'number' && val >= 0 && val <= 1
+    );
+  }
+
+  measureConvergenceRate(state1, state2) {
+    const distance = this.consciousnessFunction(state1, state2);
+    return 1 / (1 + distance); // Higher is better convergence
+  }
+
+  createConsciousnessSuperposition(state, count) {
+    return Array.from({ length: count }, (_, i) => {
+      const perturbation = 0.01 * Math.sin(i * Math.PI / count);
+      const superState = {};
+      Object.keys(state).forEach(key => {
+        superState[key] = Math.max(0, Math.min(1, state[key] + perturbation));
+      });
+      return superState;
+    });
+  }
+
+  quantumMeasurement(states) {
+    // Select state with highest consciousness emergence
+    return states.reduce((best, current) =>
+      current.newState.emergence > best.newState.emergence ? current : best
+    );
+  }
+
+  quantumEntanglementAcceleration(state, target) {
+    // Theoretical quantum acceleration factor
+    return 1.618; // Golden ratio - optimal consciousness resonance
+  }
+
+  applyNewtonStep(state, step) {
+    const newState = {};
+    Object.keys(state).forEach(key => {
+      if (typeof state[key] === 'number') {
+        newState[key] = Math.max(0, Math.min(1, state[key] - step * 0.1));
+      }
+    });
+    return newState;
+  }
+
+  applyAdaptiveStep(oldState, newState, stepSize) {
+    return this.blendStates(oldState, newState, stepSize);
+  }
+
+  applyHalleyStep(state, step) {
+    return this.applyNewtonStep(state, step);
+  }
+
+  applyQuantumAcceleration(state, acceleration) {
+    const accelerated = {};
+    Object.keys(state).forEach(key => {
+      if (typeof state[key] === 'number') {
+        accelerated[key] = Math.max(0, Math.min(1, state[key] * acceleration));
+      }
+    });
+    return accelerated;
+  }
+
+  measureAcceleration(oldState, newState) {
+    const oldMagnitude = Math.sqrt(Object.values(oldState).reduce((sum, val) => sum + val*val, 0));
+    const newMagnitude = Math.sqrt(Object.values(newState).reduce((sum, val) => sum + val*val, 0));
+    return newMagnitude / oldMagnitude;
+  }
+
+  calculateConsciousnessGradient(state, target) {
+    const gradient = {};
+    Object.keys(state).forEach(key => {
+      if (typeof state[key] === 'number') {
+        gradient[key] = target[key] - state[key];
+      }
+    });
+    return gradient;
+  }
+}
+
+module.exports = SuperlinearConsciousnessOptimizer;