Create Pmll_NP_Solver.c

Signed-off-by: Josef Edwards <joed6834@colorado.edu>

Pmll_NP_Solver.c

@@ -0,0 +1,210 @@
+#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;
+}