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#### Root-finding using Boost.Multiprecision

The apocryphally astute reader might, by now, be asking "How do we know if this computes the 'right' answer?".

For most values, there is, sadly, no 'right' answer. This is because values can only rarely be exactly represented by C++ floating-point types. What we do want is the 'best' representation - one that is the nearest representable value. (For more about how numbers are represented see Floating point).

Of course, we might start with finding an external reference source like Wolfram Alpha, as above, but this is not always possible.

Another way to reassure is to compute 'reference' values at higher precision with which to compare the results of our iterative computations using built-in like `double`. They should agree within the tolerance that was set.

The result of `static_cast`ing to `double` from a higher-precision type like `cpp_bin_float_50` is guaranteed to be the nearest representable `double` value.

For example, the cube root functions in our example for `cbrt(28.)` compute

`std::cbrt<double>(28.) = 3.0365889718756627`

WolframAlpha says `3.036588971875662519420809578505669635581453977248111123242141...`

`static_cast<double>(3.03658897187566251942080957850) = 3.0365889718756627`

This example `cbrt(28.) = 3.0365889718756627`

| Tip
To ensure that all potentially significant decimal digits are displayed use `std::numeric_limits<T>::max_digits10` (or if not available on older platforms or compilers use `2+std::numeric_limits<double>::digits*3010/10000`). Ideally, values should agree to `std::numeric-limits<T>::digits10` decimal digits. This also means that a 'reference' value to be input or `static_cast` should have at least `max_digits10` decimal digits (17 for 64-bit `double`).

If we wish to compute higher-precision values then, on some platforms, we may be able to use `long double` with a higher precision than `double` to compare with the very common `double` and/or a more efficient built-in quad floating-point type like `__float128`.

Almost all platforms can easily use Boost.Multiprecision, for example, cpp_dec_float or a binary type cpp_bin_float types, to compute values at very much higher precision.

| Note
With multiprecision types, it is debatable whether to use the type `T` for computing the initial guesses. Type `double` is like to be accurate enough for the method used in these examples. This would limit the exponent range of possible values to that of `double`. There is also the cost of conversion to and from type `T` to consider. In these examples, `double` is used via `typedef double guess_type`.

Since the functors and functions used above are templated on the value type, we can very simply use them with any of the Boost.Multiprecision types. As a reminder, here's our toy cube root function using 2 derivatives and C++11 lambda functions to find the root:

    template <class T>
    T cbrt_2deriv_lambda(T x)
    {
       // return cube root of x using 1st and 2nd derivatives and Halley.
       //using namespace std;  // Help ADL of std functions.
       using namespace boost::math::tools;
       int exponent;
       frexp(x, &exponent);                                // Get exponent of z (ignore mantissa).
       T guess = ldexp(1., exponent / 3);                    // Rough guess is to divide the exponent by three.
       T min = ldexp(0.5, exponent / 3);                     // Minimum possible value is half our guess.
       T max = ldexp(2., exponent / 3);                      // Maximum possible value is twice our guess.
       const int digits = std::numeric_limits<T>::digits;  // Maximum possible binary digits accuracy for type T.
       // digits used to control how accurate to try to make the result.
       int get_digits = static_cast<int>(digits * 0.4);    // Accuracy triples with each step, so stop when just
       // over one third of the digits are correct.
       boost::uintmax_t maxit = 20;
       T result = halley_iterate(
          // lambda function:
          [x](const T& g){ return std::make_tuple(g * g * g - x, 3 * g * g, 6 * g); },
          guess, min, max, get_digits, maxit);
       return result;
    }

Some examples below are 50 decimal digit decimal and binary types (and on some platforms a much faster `float128` or `quad_float` type ) that we can use with these includes:

    #include <boost/multiprecision/cpp_bin_float.hpp> // For cpp_bin_float_50.
    #include <boost/multiprecision/cpp_dec_float.hpp> // For cpp_dec_float_50.
    #ifndef _MSC_VER  // float128 is not yet supported by Microsoft compiler at 2013.
    #  include <boost/multiprecision/float128.hpp> // Requires libquadmath.
    #endif

Some using statements simplify their use:

      using boost::multiprecision::cpp_dec_float_50; // decimal.
      using boost::multiprecision::cpp_bin_float_50; // binary.
    #ifndef _MSC_VER  // Not supported by Microsoft compiler.
      using boost::multiprecision::float128;
    #endif

They can be used thus:

    std::cout.precision(std::numeric_limits<cpp_dec_float_50>::digits10);

    cpp_dec_float_50 two = 2; //
    cpp_dec_float_50  r = cbrt_2deriv(two);
    std::cout << "cbrt(" << two << ") = " << r << std::endl;

    r = cbrt_2deriv(2.); // Passing a double, so ADL will compute a double precision result.
    std::cout << "cbrt(" << two << ") = " << r << std::endl;
    // cbrt(2) = 1.2599210498948731906665443602832965552806854248047 'wrong' from digits 17 onwards!
    r = cbrt_2deriv(static_cast<cpp_dec_float_50>(2.)); // Passing a cpp_dec_float_50,
    // so will compute a cpp_dec_float_50 precision result.
    std::cout << "cbrt(" << two << ") = " << r << std::endl;
    r = cbrt_2deriv<cpp_dec_float_50>(2.); // Explictly a cpp_dec_float_50, so will compute a cpp_dec_float_50 precision result.
    std::cout << "cbrt(" << two << ") = " << r << std::endl;
    // cpp_dec_float_50 1.2599210498948731647672106072782283505702514647015

A reference value computed by Wolfram Alpha is

    N[2^(1/3), 50]  1.2599210498948731647672106072782283505702514647015

which agrees exactly.

To show values to their full precision, it is necessary to adjust the `std::ostream` `precision` to suit the type, for example:

    template <typename T>
    T show_cube_root(T value)
    { // Demonstrate by printing the root using all definitely significant digits.
      std::cout.precision(std::numeric_limits<T>::digits10);
      T r = cbrt_2deriv(value);
      std::cout << "value = " << value << ", cube root =" << r << std::endl;
      return r;
    }

    show_cube_root(2.);
    show_cube_root(2.L);
    show_cube_root(two);

which outputs:

    cbrt(2) = 1.2599210498948731647672106072782283505702514647015

    value = 2, cube root =1.25992104989487
    value = 2, cube root =1.25992104989487
    value = 2, cube root =1.2599210498948731647672106072782283505702514647015

| Tip
Be very careful about the floating-point type `T` that is passed to the root-finding function. Carelessly passing a integer by writing `cpp_dec_float_50 r = cbrt_2deriv(2);` or `show_cube_root(2);` will provoke many warnings and compile errors. Even `show_cube_root(2.F);` will produce warnings because `typedef double guess_type` defines the type used to compute the guess and bracket values as `double`. Even more treacherous is passing a `double` as in `cpp_dec_float_50 r = cbrt_2deriv(2.);` which silently gives the 'wrong' result, computing a `double` result and then converting to `cpp_dec_float_50`! All digits beyond `max_digits10` will be incorrect. Making the `cbrt` type explicit with `cbrt_2deriv<cpp_dec_float_50>(2.);` will give you the desired 50 decimal digit precision result.

Full code of this example is at root_finding_multiprecision_example.cpp.