/* * Copyright Nick Thompson, 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) * * Use the adaptive trapezoidal rule to estimate the integral of periodic functions over a period, * or to integrate a function whose derivative vanishes at the endpoints. * * If your function does not satisfy these conditions, and instead is simply continuous and bounded * over the whole interval, then this routine will still converge, albeit slowly. However, there * are much more efficient methods in this case, including Romberg, Simpson, and double exponential quadrature. */ #ifndef BOOST_MATH_QUADRATURE_TRAPEZOIDAL_HPP #define BOOST_MATH_QUADRATURE_TRAPEZOIDAL_HPP #include #include #include #include #include #include #include namespace boost{ namespace math{ namespace quadrature { template auto trapezoidal(F f, Real a, Real b, Real tol, std::size_t max_refinements, Real* error_estimate, Real* L1, const Policy& pol)->decltype(std::declval()(std::declval())) { static const char* function = "boost::math::quadrature::trapezoidal<%1%>(F, %1%, %1%, %1%)"; using std::abs; using boost::math::constants::half; // In many math texts, K represents the field of real or complex numbers. // Too bad we can't put blackboard bold into C++ source! typedef decltype(f(a)) K; if (!(boost::math::isfinite)(a)) { return static_cast(boost::math::policies::raise_domain_error(function, "Left endpoint of integration must be finite for adaptive trapezoidal integration but got a = %1%.\n", a, pol)); } if (!(boost::math::isfinite)(b)) { return static_cast(boost::math::policies::raise_domain_error(function, "Right endpoint of integration must be finite for adaptive trapezoidal integration but got b = %1%.\n", b, pol)); } if (a == b) { return static_cast(0); } if(a > b) { return -trapezoidal(f, b, a, tol, max_refinements, error_estimate, L1, pol); } K ya = f(a); K yb = f(b); Real h = (b - a)*half(); K I0 = (ya + yb)*h; Real IL0 = (abs(ya) + abs(yb))*h; K yh = f(a + h); K I1; I1 = I0*half() + yh*h; Real IL1 = IL0*half() + abs(yh)*h; // The recursion is: // I_k = 1/2 I_{k-1} + 1/2^k \sum_{j=1; j odd, j < 2^k} f(a + j(b-a)/2^k) std::size_t k = 2; // We want to go through at least 5 levels so we have sampled the function at least 20 times. // Otherwise, we could terminate prematurely and miss essential features. // This is of course possible anyway, but 20 samples seems to be a reasonable compromise. Real error = abs(I0 - I1); // I take k < 5, rather than k < 4, or some other smaller minimum number, // because I hit a truly exceptional bug where the k = 2 and k =3 refinement were bitwise equal, // but the quadrature had not yet converged. while (k < 5 || (k < max_refinements && error > tol*IL1) ) { I0 = I1; IL0 = IL1; I1 = I0*half(); IL1 = IL0*half(); std::size_t p = static_cast(1u) << k; h *= half(); K sum = 0; Real absum = 0; for(std::size_t j = 1; j < p; j += 2) { K y = f(a + j*h); sum += y; absum += abs(y); } I1 += sum*h; IL1 += absum*h; ++k; error = abs(I0 - I1); } if (error_estimate) { *error_estimate = error; } if (L1) { *L1 = IL1; } return static_cast(I1); } #if BOOST_WORKAROUND(BOOST_MSVC, < 1800) // Template argument deduction failure otherwise: template auto trapezoidal(F f, Real a, Real b, Real tol = 0, std::size_t max_refinements = 12, Real* error_estimate = 0, Real* L1 = 0)->decltype(std::declval()(std::declval())) #elif !defined(BOOST_NO_CXX11_NULLPTR) template auto trapezoidal(F f, Real a, Real b, Real tol = boost::math::tools::root_epsilon(), std::size_t max_refinements = 12, Real* error_estimate = nullptr, Real* L1 = nullptr)->decltype(std::declval()(std::declval())) #else template auto trapezoidal(F f, Real a, Real b, Real tol = boost::math::tools::root_epsilon(), std::size_t max_refinements = 12, Real* error_estimate = 0, Real* L1 = 0)->decltype(std::declval()(std::declval())) #endif { #if BOOST_WORKAROUND(BOOST_MSVC, <= 1600) if (tol == 0) tol = boost::math::tools::root_epsilon(); #endif return trapezoidal(f, a, b, tol, max_refinements, error_estimate, L1, boost::math::policies::policy<>()); } }}} #endif