Imported Upstream version 1.72.0

[platform/upstream/boost.git] / libs / multiprecision / example / gauss_laguerre_quadrature.cpp

// Copyright Nick Thompson, 2017
// Copyright John Maddock 2017
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt
// or copy at http://www.boost.org/LICENSE_1_0.txt)

#include <cmath>
#include <cstdint>
#include <functional>
#include <iomanip>
#include <iostream>
#include <numeric>
#include <boost/math/constants/constants.hpp>
#include <boost/math/special_functions/cbrt.hpp>
#include <boost/math/special_functions/factorials.hpp>
#include <boost/math/special_functions/gamma.hpp>
#include <boost/math/tools/roots.hpp>
#include <boost/noncopyable.hpp>

#define CPP_BIN_FLOAT  1
#define CPP_DEC_FLOAT  2
#define CPP_MPFR_FLOAT 3

//#define MP_TYPE CPP_BIN_FLOAT
#define MP_TYPE CPP_DEC_FLOAT
//#define MP_TYPE CPP_MPFR_FLOAT

namespace
{
  struct digits_characteristics
  {
    static const int digits10     = 300;
    static const int guard_digits =   6;
  };
}

#if (MP_TYPE == CPP_BIN_FLOAT)
  #include <boost/multiprecision/cpp_bin_float.hpp>
  namespace mp = boost::multiprecision;
  typedef mp::number<mp::cpp_bin_float<digits_characteristics::digits10 + digits_characteristics::guard_digits>, mp::et_off> mp_type;
#elif (MP_TYPE == CPP_DEC_FLOAT)
  #include <boost/multiprecision/cpp_dec_float.hpp>
  namespace mp = boost::multiprecision;
  typedef mp::number<mp::cpp_dec_float<digits_characteristics::digits10 + digits_characteristics::guard_digits>, mp::et_off> mp_type;
#elif (MP_TYPE == CPP_MPFR_FLOAT)
  #include <boost/multiprecision/mpfr.hpp>
  namespace mp = boost::multiprecision;
  typedef mp::number<mp::mpfr_float_backend<digits_characteristics::digits10 + digits_characteristics::guard_digits>, mp::et_off> mp_type;
#else
#error MP_TYPE is undefined
#endif

template<typename T>
class laguerre_function_object
{
public:
  laguerre_function_object(const int n, const T a) : order(n),
                                                     alpha(a),
                                                     p1   (0),
                                                     d2   (0) { }

  laguerre_function_object(const laguerre_function_object& other) : order(other.order),
                                                                    alpha(other.alpha),
                                                                    p1   (other.p1),
                                                                    d2   (other.d2) { }

  ~laguerre_function_object() { }

  T operator()(const T& x) const
  {
    // Calculate (via forward recursion):
    // * the value of the Laguerre function L(n, alpha, x), called (p2),
    // * the value of the derivative of the Laguerre function (d2),
    // * and the value of the corresponding Laguerre function of
    //   previous order (p1).

    // Return the value of the function (p2) in order to be used as a
    // function object with Boost.Math root-finding. Store the values
    // of the Laguerre function derivative (d2) and the Laguerre function
    // of previous order (p1) in class members for later use.

      p1 = T(0);
    T p2 = T(1);
      d2 = T(0);

    T j_plus_alpha(alpha);
    T two_j_plus_one_plus_alpha_minus_x(1 + alpha - x);

    int j;

    const T my_two(2);

    for(j = 0; j < order; ++j)
    {
      const T p0(p1);

      // Set the value of the previous Laguerre function.
      p1 = p2;

      // Use a recurrence relation to compute the value of the Laguerre function.
      p2 = ((two_j_plus_one_plus_alpha_minus_x * p1) - (j_plus_alpha * p0)) / (j + 1);

      ++j_plus_alpha;
      two_j_plus_one_plus_alpha_minus_x += my_two;
    }

    // Set the value of the derivative of the Laguerre function.
    d2 = ((p2 * j) - (j_plus_alpha * p1)) / x;

    // Return the value of the Laguerre function.
    return p2;
  }

  const T& previous  () const { return p1; }
  const T& derivative() const { return d2; }

  static bool root_tolerance(const T& a, const T& b)
  {
    using std::abs;

    // The relative tolerance here is: ((a - b) * 2) / (a + b).
    return (abs((a - b) * 2) < ((a + b) * boost::math::tools::epsilon<T>()));
  }

private:
  const int order;
  const T   alpha;
  mutable T p1;
  mutable T d2;

  laguerre_function_object();

  const laguerre_function_object& operator=(const laguerre_function_object&);
};

template<typename T>
class guass_laguerre_abscissas_and_weights : private boost::noncopyable
{
public:
  guass_laguerre_abscissas_and_weights(const int n, const T a) : order(n),
                                                                 alpha(a),
                                                                 valid(true),
                                                                 xi   (),
                                                                 wi   ()
  {
    if(alpha < -20.0F)
    {
      // TBD: If we ever boostify this, throw a range error here.
      // If so, then also document it in the docs.
      std::cout << "Range error: the order of the Laguerre function must exceed -20.0." << std::endl;
    }
    else
    {
      calculate();
    }
  }

  virtual ~guass_laguerre_abscissas_and_weights() { }

  const std::vector<T>& abscissas() const { return xi; }
  const std::vector<T>& weights  () const { return wi; }

  bool get_valid() const { return valid; }

private:
  const int order;
  const T   alpha;
  bool      valid;

  std::vector<T> xi;
  std::vector<T> wi;

  void calculate()
  {
    using std::abs;

    std::cout << "finding approximate roots..." << std::endl;

    std::vector<boost::math::tuple<T, T> > root_estimates;

    root_estimates.reserve(static_cast<typename std::vector<boost::math::tuple<T, T> >::size_type>(order));

    const laguerre_function_object<T> laguerre_object(order, alpha);

    // Set the initial values of the step size and the running step
    // to be used for finding the estimate of the first root.
    T step_size  = 0.01F;
    T step       = step_size;

    T first_laguerre_root = 0.0F;

    bool first_laguerre_root_has_been_found = true;

    if(alpha < -1.0F)
    {
      // Iteratively step through the Laguerre function using a
      // small step-size in order to find a rough estimate of
      // the first zero.

      bool this_laguerre_value_is_negative = (laguerre_object(mp_type(0)) < 0);

      static const int j_max = 10000;

      int j;

      for(j = 0; (j < j_max) && (this_laguerre_value_is_negative != (laguerre_object(step) < 0)); ++j)
      {
        // Increment the step size until the sign of the Laguerre function
        // switches. This indicates a zero-crossing, signalling the next root.
        step += step_size;
      }

      if(j >= j_max)
      {
        first_laguerre_root_has_been_found = false;
      }
      else
      {
        // We have found the first zero-crossing. Put a loose bracket around
        // the root using a window. Here, we know that the first root lies
        // between (x - step_size) < root < x.

        // Before storing the approximate root, perform a couple of
        // bisection steps in order to tighten up the root bracket.
        boost::uintmax_t a_couple_of_iterations = 3U;
        const std::pair<T, T>
          first_laguerre_root = boost::math::tools::bisect(laguerre_object,
                                                           step - step_size,
                                                           step,
                                                           laguerre_function_object<T>::root_tolerance,
                                                           a_couple_of_iterations);

        static_cast<void>(a_couple_of_iterations);
      }
    }
    else
    {
      // Calculate an estimate of the 1st root of a generalized Laguerre
      // function using either a Taylor series or an expansion in Bessel
      // function zeros. The Bessel function zeros expansion is from Tricomi.

      // Here, we obtain an estimate of the first zero of J_alpha(x).

      T j_alpha_m1;

      if(alpha < 1.4F)
      {
        // For small alpha, use a short series obtained from Mathematica(R).
        // Series[BesselJZero[v, 1], {v, 0, 3}]
        // N[%, 12]
        j_alpha_m1 = (((          0.09748661784476F
                        * alpha - 0.17549359276115F)
                        * alpha + 1.54288974259931F)
                        * alpha + 2.40482555769577F);
      }
      else
      {
        // For larger alpha, use the first line of Eqs. 10.21.40 in the NIST Handbook.
        const T alpha_pow_third(boost::math::cbrt(alpha));
        const T alpha_pow_minus_two_thirds(T(1) / (alpha_pow_third * alpha_pow_third));

        j_alpha_m1 = alpha * (((((                             + 0.043F
                                  * alpha_pow_minus_two_thirds - 0.0908F)
                                  * alpha_pow_minus_two_thirds - 0.00397F)
                                  * alpha_pow_minus_two_thirds + 1.033150F)
                                  * alpha_pow_minus_two_thirds + 1.8557571F)
                                  * alpha_pow_minus_two_thirds + 1.0F);
      }

      const T vf             = ((order * 4.0F) + (alpha * 2.0F) + 2.0F);
      const T vf2            = vf * vf;
      const T j_alpha_m1_sqr = j_alpha_m1 * j_alpha_m1;

      first_laguerre_root = (j_alpha_m1_sqr * (-0.6666666666667F + ((0.6666666666667F * alpha) * alpha) + (0.3333333333333F * j_alpha_m1_sqr) + vf2)) / (vf2 * vf);
    }

    if(first_laguerre_root_has_been_found)
    {
      bool this_laguerre_value_is_negative = (laguerre_object(mp_type(0)) < 0);

      // Re-set the initial value of the step-size based on the
      // estimate of the first root.
      step_size = first_laguerre_root / 2;
      step      = step_size;

      // Step through the Laguerre function using a step-size
      // of dynamic width in order to find the zero crossings
      // of the Laguerre function, providing rough estimates
      // of the roots. Refine the brackets with a few bisection
      // steps, and store the results as bracketed root estimates.

      while(static_cast<int>(root_estimates.size()) < order)
      {
        // Increment the step size until the sign of the Laguerre function
        // switches. This indicates a zero-crossing, signalling the next root.
        step += step_size;

        if(this_laguerre_value_is_negative != (laguerre_object(step) < 0))
        {
          // We have found the next zero-crossing.

          // Change the running sign of the Laguerre function.
          this_laguerre_value_is_negative = (!this_laguerre_value_is_negative);

          // We have found the first zero-crossing. Put a loose bracket around
          // the root using a window. Here, we know that the first root lies
          // between (x - step_size) < root < x.

          // Before storing the approximate root, perform a couple of
          // bisection steps in order to tighten up the root bracket.
          boost::uintmax_t a_couple_of_iterations = 3U;
          const std::pair<T, T>
            root_estimate_bracket = boost::math::tools::bisect(laguerre_object,
                                                               step - step_size,
                                                               step,
                                                               laguerre_function_object<T>::root_tolerance,
                                                               a_couple_of_iterations);

          static_cast<void>(a_couple_of_iterations);

          // Store the refined root estimate as a bracketed range in a tuple.
          root_estimates.push_back(boost::math::tuple<T, T>(root_estimate_bracket.first,
                                                            root_estimate_bracket.second));

          if(root_estimates.size() >= static_cast<std::size_t>(2U))
          {
            // Determine the next step size. This is based on the distance between
            // the previous two roots, whereby the estimates of the previous roots
            // are computed by taking the average of the lower and upper range of
            // the root-estimate bracket.

            const T r0 = (  boost::math::get<0>(*(root_estimates.rbegin() + 1U))
                          + boost::math::get<1>(*(root_estimates.rbegin() + 1U))) / 2;

            const T r1 = (  boost::math::get<0>(*root_estimates.rbegin())
                          + boost::math::get<1>(*root_estimates.rbegin())) / 2;

            const T distance_between_previous_roots = r1 - r0;

            step_size = distance_between_previous_roots / 3;
          }
        }
      }

      const T norm_g =
        ((alpha == 0) ? T(-1)
                      : -boost::math::tgamma(alpha + order) / boost::math::factorial<T>(order - 1));

      xi.reserve(root_estimates.size());
      wi.reserve(root_estimates.size());

      // Calculate the abscissas and weights to full precision.
      for(std::size_t i = static_cast<std::size_t>(0U); i < root_estimates.size(); ++i)
      {
        std::cout << "calculating abscissa and weight for index: " << i << std::endl;

        // Calculate the abscissas using iterative root-finding.

        // Select the maximum allowed iterations, being at least 20.
        // The determination of the maximum allowed iterations is
        // based on the number of decimal digits in the numerical
        // type T.
        const int my_digits10 = static_cast<int>(static_cast<float>(boost::math::tools::digits<T>()) * 0.301F);
        const boost::uintmax_t number_of_iterations_allowed = (std::max)(20, my_digits10 / 2);

        boost::uintmax_t number_of_iterations_used = number_of_iterations_allowed;

        // Perform the root-finding using ACM TOMS 748 from Boost.Math.
        const std::pair<T, T>
          laguerre_root_bracket = boost::math::tools::toms748_solve(laguerre_object,
                                                                    boost::math::get<0>(root_estimates[i]),
                                                                    boost::math::get<1>(root_estimates[i]),
                                                                    laguerre_function_object<T>::root_tolerance,
                                                                    number_of_iterations_used);

        // Based on the result of *each* root-finding operation, re-assess
        // the validity of the Guass-Laguerre abscissas and weights object.
        valid &= (number_of_iterations_used < number_of_iterations_allowed);

        // Compute the Laguerre root as the average of the values from
        // the solved root bracket.
        const T laguerre_root = (  laguerre_root_bracket.first
                                 + laguerre_root_bracket.second) / 2;

        // Calculate the weight for this Laguerre root. Here, we calculate
        // the derivative of the Laguerre function and the value of the
        // previous Laguerre function on the x-axis at the value of this
        // Laguerre root.
        static_cast<void>(laguerre_object(laguerre_root));

        // Store the abscissa and weight for this index.
        xi.push_back(laguerre_root);
        wi.push_back(norm_g / ((laguerre_object.derivative() * order) * laguerre_object.previous()));
      }
    }
  }
};

namespace
{
  template<typename T>
  struct gauss_laguerre_ai
  {
  public:
    gauss_laguerre_ai(const T X) : x(X)
    {
      using std::exp;
      using std::sqrt;

      zeta = ((sqrt(x) * x) * 2) / 3;

      const T zeta_times_48_pow_sixth = sqrt(boost::math::cbrt(zeta * 48));

      factor = 1 / ((sqrt(boost::math::constants::pi<T>()) * zeta_times_48_pow_sixth) * (exp(zeta) * gamma_of_five_sixths()));
    }

    gauss_laguerre_ai(const gauss_laguerre_ai& other) : x     (other.x),
                                                        zeta  (other.zeta),
                                                        factor(other.factor) { }

    T operator()(const T& t) const
    {
      using std::sqrt;

      return factor / sqrt(boost::math::cbrt(2 + (t / zeta)));
    }

  private:
    const T x;
    T zeta;
    T factor;

    static const T& gamma_of_five_sixths()
    {
      static const T value = boost::math::tgamma(T(5) / 6);

      return value;
    }

    const gauss_laguerre_ai& operator=(const gauss_laguerre_ai&);
  };

  template<typename T>
  T gauss_laguerre_airy_ai(const T x)
  {
    static const float digits_factor  = static_cast<float>(std::numeric_limits<mp_type>::digits10) / 300.0F;
    static const int   laguerre_order = static_cast<int>(600.0F * digits_factor);

    static const guass_laguerre_abscissas_and_weights<T> abscissas_and_weights(laguerre_order, -T(1) / 6);

    T airy_ai_result;

    if(abscissas_and_weights.get_valid())
    {
      const gauss_laguerre_ai<T> this_gauss_laguerre_ai(x);

      airy_ai_result =
        std::inner_product(abscissas_and_weights.abscissas().begin(),
                           abscissas_and_weights.abscissas().end(),
                           abscissas_and_weights.weights().begin(),
                           T(0),
                           std::plus<T>(),
                           [&this_gauss_laguerre_ai](const T& this_abscissa, const T& this_weight) -> T
                           {
                             return this_gauss_laguerre_ai(this_abscissa) * this_weight;
                           });
    }
    else
    {
      // TBD: Consider an error message.
      airy_ai_result = T(0);
    }

    return airy_ai_result;
  }
}

int main()
{
  // Use Gauss-Laguerre integration to compute airy_ai(120 / 7).

  // 9 digits
  // 3.89904210e-22

  // 10 digits
  // 3.899042098e-22

  // 50 digits.
  // 3.8990420982303275013276114626640705170145070824318e-22

  // 100 digits.
  // 3.899042098230327501327611462664070517014507082431797677146153303523108862015228
  // 864136051942933142648e-22

  // 200 digits.
  // 3.899042098230327501327611462664070517014507082431797677146153303523108862015228
  // 86413605194293314264788265460938200890998546786740097437064263800719644346113699
  // 77010905030516409847054404055843899790277e-22

  // 300 digits.
  // 3.899042098230327501327611462664070517014507082431797677146153303523108862015228
  // 86413605194293314264788265460938200890998546786740097437064263800719644346113699
  // 77010905030516409847054404055843899790277083960877617919088116211775232728792242
  // 9346416823281460245814808276654088201413901972239996130752528e-22

  // 500 digits.
  // 3.899042098230327501327611462664070517014507082431797677146153303523108862015228
  // 86413605194293314264788265460938200890998546786740097437064263800719644346113699
  // 77010905030516409847054404055843899790277083960877617919088116211775232728792242
  // 93464168232814602458148082766540882014139019722399961307525276722937464859521685
  // 42826483602153339361960948844649799257455597165900957281659632186012043089610827
  // 78871305322190941528281744734605934497977375094921646511687434038062987482900167
  // 45127557400365419545e-22

  // Mathematica(R) or Wolfram's Alpha:
  // N[AiryAi[120 / 7], 300]
  std::cout << std::setprecision(digits_characteristics::digits10)
            << gauss_laguerre_airy_ai(mp_type(120) / 7)
            << std::endl;
}