Table of Contents

Conjugate Gradient Method

Routine Name: conjugate_gradient

Author: Philip Nelson

Language: C++

Description

conjugate_gradient is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is symmetric and positive-definite. conjugate_gradient is implemented as an iterative algorithm, applicable to sparse systems that are too large to be handled by a direct implementation. 1

Input

std::array<T, N> conjugate_gradient(Matrix<T, N, N>& A,
                                    std::array<T, N> const& b,
                                    unsigned int const& MAX_ITERATIONS = 1000u)

requires:

• Matrix<T, N, N>& A - an NxN matrix of type T
• std::array<T, N> const& b - the b vector
• unsigned int const& MAX_ITERATIONS - the maximum iterations (default 1000)

Output

The solution vector \(x\).

Code

template <typename T, std::size_t N>
std::array<T, N> conjugate_gradient(Matrix<T, N, N>& A,
                                    std::array<T, N> const& b,
                                    unsigned int const& MAX_ITERATIONS = 1000u)
{
  auto ct = 0u;
  auto tol = maceps<T>().maceps;
  std::array<T, N> x_k, x_k1;
  x_k.fill(0);
  x_k1.fill(0);
  auto r_k = b;
  auto r_k1 = r_k, r_k2 = r_k;
  auto p_k = r_k, p_k1 = r_k;
  for (auto k = 0u; k < MAX_ITERATIONS; ++k)
  {
    ++ct;
    if (k != 0)
    {
      auto b_k = (r_k1 * r_k1) / (r_k2 * r_k2);
      p_k = r_k1 + b_k * p_k1;
    }
    auto s_k = A * p_k;
    auto a_k = r_k1 * r_k1 / (p_k * s_k);
    x_k = x_k1 + a_k * p_k;
    r_k = r_k1 - a_k * s_k;

    if (allclose(x_k, x_k1, tol))
    {
      std::cout << "Conjugate Gradient completed in " << ct << " iterations\n";
      return x_k;
    }

    r_k2 = r_k1;
    r_k1 = r_k;
    x_k1 = x_k;
    p_k1 = p_k;
  }
  return x_k;
}

Example

int main()
{
  Matrix<double, 2, 2> A({
      {16,   3},
      { 7, -11}
      });
  std::array<double, 2> b({
      {11, 13}
      });
  std::cout << "A\n" << A << "b\n" << b << '\n';
  std::cout << gauss_sidel(A, b, 100u) << "\n";
  std::cout << A.jacobiIteration(b, 100u) << "\n";
  std::cout << conjugate_gradient(A, b, 10000u) << "\n";

  Matrix<double, 4, 4> A1({
      {10, -1,  2,  0},
      {-1, 11, -1,  3},
      { 2, -1, 10, -1},
      { 0,  3, -1,  8}
      });
  std::array<double, 4> b1({
      {6, 25, -11, 15}
      });
  std::cout << "A\n" << A1 << "b\n" << b1 << '\n';
  std::cout << gauss_sidel(A1, b1, 100u) << "\n";
  std::cout << A1.jacobiIteration(b1, 100u) << "\n";
  std::cout << conjugate_gradient(A1, b1, 1000u) << "\n";
}

Result

As opposed to Gauss Sidel, Conjugate Gradient converges differently on the solution depending on the topology of the region around the solution.

A
|         16         3 |
|          7       -11 |
b
[         11        13 ]

Gauss Sidel completed in 18 iterations
[      0.812    -0.665 ]

Jacobi Iteration completed in 35 iterations
[      0.812    -0.665 ]

Conjugate Gradient completed in 75 iterations
[      0.812    -0.665 ]

A
|         10        -1         2         0 |
|         -1        11        -1         3 |
|          2        -1        10        -1 |
|          0         3        -1         8 |
b
[          6        25       -11        15 ]

Gauss Sidel completed in 17 iterations
[          1         2        -1         1 ]

Jacobi Iteration completed in 43 iterations
[          1         2        -1         1 ]

Conjugate Gradient completed in 5 iterations
[          1         2        -1         1 ]

Last Modification date: 21 March 2018