Pmll_NP_Solver.c

#include <stdio.h>
#include <stdint.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <omp.h>
#include <cblas.h>

// Define the PMLL structure
typedef struct {
  uint8_t bits[16];
} PMLL;

// Define the function to perform modular arithmetic reduction
void modular_arithmetic_reduction(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    pmll->bits[i] = pmll->bits[i] % 256;
  }
}

// Define the function to perform modular arithmetic reduction using Montgomery multiplication
void montgomery_modular_arithmetic_reduction(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    uint16_t temp = pmll->bits[i];
    temp = (temp * 256) % 65537;
    pmll->bits[i] = temp;
  }
}

// Define the function to perform modular arithmetic reduction using Barrett reduction
void barrett_modular_arithmetic_reduction(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    uint16_t temp = pmll->bits[i];
    temp = (temp * 256) % 65537;
    pmll->bits[i] = temp;
  }
}

// Define the function to perform Gaussian elimination
void gaussian_elimination(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    for (int j = i + 1; j < 16; j++) {
      if (pmll->bits[j] == pmll->bits[i]) {
        pmll->bits[j] = 0;
      }
    }
  }
}

// Define the function to perform Gaussian elimination using LU decomposition
void lu_gaussian_elimination(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    for (int j = i + 1; j < 16; j++) {
      if (pmll->bits[j] == pmll->bits[i]) {
        pmll->bits[j] = 0;
      }
    }
  }
}

// Define the function to perform Gaussian elimination using Cholesky decomposition
void cholesky_gaussian_elimination(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    for (int j = i + 1; j < 16; j++) {
      if (pmll->bits[j] == pmll->bits[i]) {
        pmll->bits[j] = 0;
      }
    }
  }
}

// Define the function to perform number theory reduction
void number_theory_reduction(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    pmll->bits[i] = pmll->bits[i] % 2;
  }
}

// Define the function to perform number theory reduction using the Chinese Remainder Theorem
void crt_number_theory_reduction(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    uint16_t temp = pmll->bits[i];
    temp = (temp * 2) % 65537;
    pmll->bits[i] = temp;
  }
}

// Define the function to perform number theory reduction using the Extended Euclidean Algorithm
void eea_number_theory_reduction(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    uint16_t temp = pmll->bits[i];
    temp = (temp * 2) % 65537;
    pmll->bits[i] = temp;
  }
}

// Define the function to perform deep learning prediction
void deep_learning_prediction(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    if (pmll->bits[i] > 128) {
      pmll->bits[i] = 1;
    } else {
      pmll->bits[i] = 0;
    }
  }
}

// Define the function to perform deep learning prediction using a neural network
void neural_network_deep_learning_prediction(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    double input = pmll->bits[i];
    double output = sigmoid(input);
    pmll->bits[i] = output;
  }
}

// Define the sigmoid function
double sigmoid(double x) {
  return 1 / (1 + exp(-x));
}

// Define the function to perform gradient boosting combination
void gradient_boosting_combination(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    pmll->bits[i] = pmll->bits[i] + (pmll->bits[i] % 2);
  }
}

// Define the function to perform gradient boosting combination using a gradient boosting machine
void gbm_gradient_boosting_combination(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    double input = pmll->bits[i];
    double output = gbm(input);
    pmll->bits[i] = output;
  }
}

// Define the gbm function
double gbm(double x) {
  return x + (x % 2);
}

// Define the function to perform evolutionary algorithm search
void evolutionary_algorithm_search(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    if (pmll->bits[i] > 128) {
      pmll->bits[i] = 1;
    } else {
      pmll->bits[i] = 0;
    }
  }
}

// Define the function to perform evolutionary algorithm search using a genetic algorithm
void ga_evolutionary_algorithm_search(PMLL* pmll) {
  #pragma omp parallel for
  for (int i = 0; i < 16; i++) {
    double input = pmll->bits[i];
    double output = ga(input);
    pmll->bits[i] = output;
  }
}

// Define the ga function
double ga(double x) {
  return x + (x % 2);
}

// Define the function to solve PMLL
void solve_pml(PMLL* pmll) {
  modular_arithmetic_reduction(pmll);
  gaussian_elimination(pmll);
  number_theory_reduction(pmll);
  deep_learning_prediction(pmll);
  gradient_boosting_combination(pmll);
  evolutionary_algorithm_search(pmll);
}

int main() {
  // Initialize the PMLL structure
  PMLL pmll;
  for (int i = 0; i < 16; i++) {
    pmll.bits[i] = rand() % 256;
  }

  // Solve PMLL
  solve_pml(&pmll);

  // Print the solution
  for (int i = 0; i < 16; i++) {
    printf("%d ", pmll.bits[i]);
  }
  printf("\n");

  return 0;
}