diff --git a/numerical_methods/qr_eigen_values.cpp b/numerical_methods/qr_eigen_values.cpp
new file mode 100644
index 000000000..95ace03a7
--- /dev/null
+++ b/numerical_methods/qr_eigen_values.cpp
@@ -0,0 +1,267 @@
+/**
+ * @file
+ * \brief Compute real eigen values and eigen vectors of a symmetric matrix
+ * using [QR decomposition](https://en.wikipedia.org/wiki/QR_decomposition)
+ * method.
+ * \author [Krishna Vedala](https://github.com/kvedala)
+ */
+#include <cassert>
+#include <cmath>
+#include <cstdlib>
+#include <ctime>
+#include <iostream>
+#ifdef _OPENMP
+#include <omp.h>
+#endif
+
+#include "./qr_decompose.h"
+using qr_algorithm::operator<<;
+
+#define LIMS 9 /**< limit of range of matrix values */
+
+/**
+ * create a symmetric square matrix of given size with random elements. A
+ * symmetric square matrix will *always* have real eigen values.
+ *
+ * \param[out] A matrix to create (must be pre-allocated in memory)
+ */
+void create_matrix(std::valarray<std::valarray<double>> *A) {
+    int i, j, tmp, lim2 = LIMS >> 1;
+    int N = A->size();
+    std::srand(std::time(nullptr));
+
+#ifdef _OPENMP
+#pragma omp for
+#endif
+    for (i = 0; i < N; i++) {
+        A[0][i][i] = (std::rand() % LIMS) - lim2;
+        for (j = i + 1; j < N; j++) {
+            tmp = (std::rand() % LIMS) - lim2;
+            A[0][i][j] = tmp;  // summetrically distribute random values
+            A[0][j][i] = tmp;
+        }
+    }
+}
+
+/**
+ * Perform multiplication of two matrices.
+ * * R2 must be equal to C1
+ * * Resultant matrix size should be R1xC2
+ * \param[in] A first matrix to multiply
+ * \param[in] B second matrix to multiply
+ * \param[out] OUT output matrix (must be pre-allocated)
+ * \returns pointer to resultant matrix
+ */
+void mat_mul(const std::valarray<std::valarray<double>> &A,
+             const std::valarray<std::valarray<double>> &B,
+             std::valarray<std::valarray<double>> *OUT) {
+    int R1 = A.size();
+    int C1 = A[0].size();
+    int R2 = B.size();
+    int C2 = B[0].size();
+    if (C1 != R2) {
+        perror("Matrix dimensions mismatch!");
+        return;
+    }
+
+    for (int i = 0; i < R1; i++) {
+        for (int j = 0; j < C2; j++) {
+            OUT[0][i][j] = 0.f;
+            for (int k = 0; k < C1; k++) {
+                OUT[0][i][j] += A[i][k] * B[k][j];
+            }
+        }
+    }
+}
+
+namespace qr_algorithm {
+/** Compute eigen values
+ * \param[in,out] A matric to compute eigen values for \note This matrix gets
+ * modified
+ * \param[in] print_intermediates (optional) whether to print intermediate A, Q
+ * and R matrices (default = `false`)
+ */
+std::valarray<double> eigen_values(std::valarray<std::valarray<double>> *A,
+                                   bool print_intermediates = false) {
+    int rows = A->size();
+    int columns = rows;
+    int counter = 0, num_eigs = rows - 1;
+    double last_eig = 0;
+
+    std::valarray<std::valarray<double>> Q(rows);
+    std::valarray<std::valarray<double>> R(columns);
+
+    /* number of eigen values = matrix size */
+    std::valarray<double> eigen_vals(rows);
+    for (int i = 0; i < rows; i++) {
+        Q[i] = std::valarray<double>(columns);
+        R[i] = std::valarray<double>(columns);
+    }
+
+    /* continue till all eigen values are found */
+    while (num_eigs > 0) {
+        /* iterate with QR decomposition */
+        while (std::abs(A[0][num_eigs][num_eigs - 1]) >
+               std::numeric_limits<double>::epsilon()) {
+            // initial approximation = last diagonal element
+            last_eig = A[0][num_eigs][num_eigs];
+            for (int i = 0; i < rows; i++) {
+                A[0][i][i] -= last_eig; /* A - cI */
+            }
+
+            qr_decompose(*A, &Q, &R);
+
+            if (print_intermediates) {
+                std::cout << *A << "\n";
+                std::cout << Q << "\n";
+                std::cout << R << "\n";
+                printf("-------------------- %d ---------------------\n",
+                       ++counter);
+            }
+
+            // new approximation A' = R * Q
+            mat_mul(R, Q, A);
+
+            for (int i = 0; i < rows; i++) {
+                A[0][i][i] += last_eig; /* A + cI */
+            }
+        }
+
+        /* store the converged eigen value */
+        eigen_vals[num_eigs] = last_eig;
+        // A[0][num_eigs][num_eigs];
+        if (print_intermediates) {
+            std::cout << "========================\n";
+            std::cout << "Eigen value: " << last_eig << ",\n";
+            std::cout << "========================\n";
+        }
+
+        num_eigs--;
+        rows--;
+        columns--;
+    }
+    eigen_vals[0] = A[0][0][0];
+
+    if (print_intermediates) {
+        std::cout << Q << "\n";
+        std::cout << R << "\n";
+    }
+
+    return eigen_vals;
+}
+
+}  // namespace qr_algorithm
+
+/**
+ * test function to compute eigen values of a 2x2 matrix
+ * \f[\begin{bmatrix}
+ * 5 & 7\\
+ * 7 & 11
+ * \end{bmatrix}\f]
+ * which are approximately, {15.56158, 0.384227}
+ */
+void test1() {
+    std::valarray<std::valarray<double>> X = {{5, 7}, {7, 11}};
+    double y[] = {15.56158, 0.384227};  // corresponding y-values
+
+    std::cout << "------- Test 1 -------" << std::endl;
+    std::valarray<double> eig_vals = qr_algorithm::eigen_values(&X);
+
+    for (int i = 0; i < 2; i++) {
+        std::cout << i + 1 << "/2 Checking for " << y[i] << " --> ";
+        bool result = false;
+        for (int j = 0; j < 2 && !result; j++) {
+            if (std::abs(y[i] - eig_vals[j]) < 0.1) {
+                result = true;
+                std::cout << "(" << eig_vals[j] << ") ";
+            }
+        }
+        assert(result);  // ensure that i^th expected eigen value was computed
+        std::cout << "found\n";
+    }
+    std::cout << "Test 1 Passed\n\n";
+}
+
+/**
+ * test function to compute eigen values of a 2x2 matrix
+ * \f[\begin{bmatrix}
+ * -4& 4& 2& 0& -3\\
+ * 4& -4& 4& -3& -1\\
+ * 2& 4& 4& 3& -3\\
+ * 0& -3& 3& -1&-1\\
+ * -3& -1& -3& -3& 0
+ * \end{bmatrix}\f]
+ * which are approximately, {9.27648, -9.26948, 2.0181, -1.03516, -5.98994}
+ */
+void test2() {
+    std::valarray<std::valarray<double>> X = {{-4, 4, 2, 0, -3},
+                                              {4, -4, 4, -3, -1},
+                                              {2, 4, 4, 3, -3},
+                                              {0, -3, 3, -1, -3},
+                                              {-3, -1, -3, -3, 0}};
+    double y[] = {9.27648, -9.26948, 2.0181, -1.03516,
+                  -5.98994};  // corresponding y-values
+
+    std::cout << "------- Test 2 -------" << std::endl;
+    std::valarray<double> eig_vals = qr_algorithm::eigen_values(&X);
+
+    std::cout << X << "\n"
+              << "Eigen values: " << eig_vals << "\n";
+
+    for (int i = 0; i < 5; i++) {
+        std::cout << i + 1 << "/5 Checking for " << y[i] << " --> ";
+        bool result = false;
+        for (int j = 0; j < 5 && !result; j++) {
+            if (std::abs(y[i] - eig_vals[j]) < 0.1) {
+                result = true;
+                std::cout << "(" << eig_vals[j] << ") ";
+            }
+        }
+        assert(result);  // ensure that i^th expected eigen value was computed
+        std::cout << "found\n";
+    }
+    std::cout << "Test 2 Passed\n\n";
+}
+
+/**
+ * main function
+ */
+int main(int argc, char **argv) {
+    int mat_size = 5;
+    if (argc == 2)
+        mat_size = atoi(argv[1]);
+    else {  // if invalid input argument is given run tests
+        test1();
+        test2();
+        std::cout << "Usage: ./qr_eigen_values [mat_size]\n";
+        return 0;
+    }
+
+    if (mat_size < 2) {
+        fprintf(stderr, "Matrix size should be > 2\n");
+        return -1;
+    }
+
+    int i, rows = mat_size, columns = mat_size;
+
+    std::valarray<std::valarray<double>> A(rows);
+
+    for (int i = 0; i < rows; i++) {
+        A[i] = std::valarray<double>(columns);
+    }
+
+    /* create a random matrix */
+    create_matrix(&A);
+
+    std::cout << A << "\n";
+
+    clock_t t1 = clock();
+    std::valarray<double> eigen_vals = qr_algorithm::eigen_values(&A);
+    double dtime = static_cast<double>(clock() - t1) / CLOCKS_PER_SEC;
+
+    std::cout << "Eigen vals: ";
+    for (i = 0; i < mat_size; i++) std::cout << eigen_vals[i] << "\t";
+    std::cout << "\nTime taken to compute: " << dtime << " sec\n";
+
+    return 0;
+}