durand_kerner_roots.cpp File Reference

Compute all possible approximate roots of any given polynomial using Durand Kerner algorithm More...

#include <algorithm>
#include <cassert>
#include <cmath>
#include <complex>
#include <cstdlib>
#include <ctime>
#include <fstream>
#include <iostream>
#include <valarray>

Include dependency graph for durand_kerner_roots.cpp:

Macros
#define	ACCURACY   1e-10
#define	MAX_BUFF_SIZE   50

Functions
std::complex< double >	poly_function (const std::valarray< double > &coeffs, std::complex< double > x)
const char *	complex_str (const std::complex< double > &x)
bool	check_termination (long double delta)
std::pair< uint32_t, double >	durand_kerner_algo (const std::valarray< double > &coeffs, std::valarray< std::complex< double >> *roots, bool write_log=false)
void	test1 ()
void	test2 ()
int	main (int argc, char **argv)

Detailed Description

Compute all possible approximate roots of any given polynomial using Durand Kerner algorithm

Author

Krishna Vedala

Test the algorithm online: https://gist.github.com/kvedala/27f1b0b6502af935f6917673ec43bcd7

Try the highly unstable Wilkinson's polynomial:

./numerical_methods/durand_kerner_roots 1 -210 20615 -1256850 53327946
-1672280820 40171771630 -756111184500 11310276995381 -135585182899530
1307535010540395 -10142299865511450 63030812099294896 -311333643161390640
1206647803780373360 -3599979517947607200 8037811822645051776
-12870931245150988800 13803759753640704000 -8752948036761600000
2432902008176640000

Sample implementation results to compute approximate roots of the equation \(x^4-1=0\):

Macro Definition Documentation

ACCURACY

#define ACCURACY   1e-10

maximum accuracy limit

Function Documentation

check_termination()

bool check_termination

(

long double

delta

)

check for termination condition

Parameters

[in]

delta

point at which to evaluate the polynomial

Returns

false if termination not reached

true if termination reached

                                          {
    static long double past_delta = INFINITY;
    if (std::abs(past_delta - delta) <= ACCURACY || delta < ACCURACY)
        return true;
    past_delta = delta;
    return false;
}

complex_str()

const char* complex_str

(

const std::complex< double > &

x

)

create a textual form of complex number

Parameters

[in]

x

point at which to evaluate the polynomial

Returns

pointer to converted string

                                                     {
#define MAX_BUFF_SIZE 50
    static char msg[MAX_BUFF_SIZE];
 
    std::snprintf(msg, MAX_BUFF_SIZE, "% 7.04g%+7.04gj", x.real(), x.imag());
 
    return msg;
}

durand_kerner_algo()

std::pair<uint32_t, double> durand_kerner_algo	(	const std::valarray< double > &	coeffs,
		std::valarray< std::complex< double >> *	roots,
		bool	write_log = false
	)

Implements Durand Kerner iterative algorithm to compute all roots of a polynomial.

Parameters

[in]	coeffs	coefficients of the polynomial
[out]	roots	the computed roots of the polynomial
[in]	write_log	flag whether to save the log file (default = false)

Returns

pair of values - number of iterations taken and final accuracy achieved

                                                                    {
    long double tol_condition = 1;
    uint32_t iter = 0;
    int n;
    std::ofstream log_file;
 
    if (write_log) {
        /*
         * store intermediate values to a CSV file
         */
        log_file.open("durand_kerner.log.csv");
        if (!log_file.is_open()) {
            perror("Unable to create a storage log file!");
            std::exit(EXIT_FAILURE);
        }
        log_file << "iter#,";
 
        for (n = 0; n < roots->size(); n++) log_file << "root_" << n << ",";
 
        log_file << "avg. correction";
        log_file << "\n0,";
        for (n = 0; n < roots->size(); n++)
            log_file << complex_str((*roots)[n]) << ",";
    }
 
    bool break_loop = false;
    while (!check_termination(tol_condition) && iter < INT16_MAX &&
           !break_loop) {
        tol_condition = 0;
        iter++;
        break_loop = false;
 
        if (log_file.is_open())
            log_file << "\n" << iter << ",";
 
#ifdef _OPENMP
#pragma omp parallel for shared(break_loop, tol_condition)
#endif
        for (n = 0; n < roots->size(); n++) {
            if (break_loop)
                continue;
 
            std::complex<double> numerator, denominator;
            numerator = poly_function(coeffs, (*roots)[n]);
            denominator = 1.0;
            for (int i = 0; i < roots->size(); i++)
                if (i != n)
                    denominator *= (*roots)[n] - (*roots)[i];
 
            std::complex<long double> delta = numerator / denominator;
 
            if (std::isnan(std::abs(delta)) || std::isinf(std::abs(delta))) {
                std::cerr << "\n\nOverflow/underrun error - got value = "
                          << std::abs(delta) << "\n";
                // return std::pair<uint32_t, double>(iter, tol_condition);
                break_loop = true;
            }
 
            (*roots)[n] -= delta;
 
#ifdef _OPENMP
#pragma omp critical
#endif
            tol_condition = std::max(tol_condition, std::abs(std::abs(delta)));
        }
        // tol_condition /= (degree - 1);
 
        if (break_loop)
            break;
 
        if (log_file.is_open()) {
            for (n = 0; n < roots->size(); n++)
                log_file << complex_str((*roots)[n]) << ",";
        }
 
#if defined(DEBUG) || !defined(NDEBUG)
        if (iter % 500 == 0) {
            std::cout << "Iter: " << iter << "\t";
            for (n = 0; n < roots->size(); n++)
                std::cout << "\t" << complex_str((*roots)[n]);
            std::cout << "\t\tabsolute average change: " << tol_condition
                      << "\n";
        }
#endif
 
        if (log_file.is_open())
            log_file << tol_condition;
    }
 
    return std::pair<uint32_t, long double>(iter, tol_condition);
}

Here is the call graph for this function:

poly_function()

std::complex<double> poly_function	(	const std::valarray< double > &	coeffs,
		std::complex< double >	x
	)

Evaluate the value of a polynomial with given coefficients

Parameters

[in]	coeffs	coefficients of the polynomial
[in]	x	point at which to evaluate the polynomial

Returns

\(f(x)\)

                                                         {
    double real = 0.f, imag = 0.f;
    int n;
 
    // #ifdef _OPENMP
    // #pragma omp target teams distribute reduction(+ : real, imag)
    // #endif
    for (n = 0; n < coeffs.size(); n++) {
        std::complex<double> tmp =
            coeffs[n] * std::pow(x, coeffs.size() - n - 1);
        real += tmp.real();
        imag += tmp.imag();
    }
 
    return std::complex<double>(real, imag);
}

Here is the call graph for this function:

test1()

void test1

(

)

Self test the algorithm by checking the roots for \(x^2+4=0\) to which the roots are \(0 \pm 2i\)

             {
    const std::valarray<double> coeffs = {1, 0, 4};  // x^2 - 2 = 0
    std::valarray<std::complex<double>> roots(2);
    std::valarray<std::complex<double>> expected = {
        std::complex<double>(0., 2.),
        std::complex<double>(0., -2.)  // known expected roots
    };
 
    /* initialize root approximations with random values */
    for (int n = 0; n < roots.size(); n++) {
        roots[n] = std::complex<double>(std::rand() % 100, std::rand() % 100);
        roots[n] -= 50.f;
        roots[n] /= 25.f;
    }
 
    auto result = durand_kerner_algo(coeffs, &roots, false);
 
    for (int i = 0; i < roots.size(); i++) {
        // check if approximations are have < 0.1% error with one of the
        // expected roots
        bool err1 = false;
        for (int j = 0; j < roots.size(); j++)
            err1 |= std::abs(std::abs(roots[i] - expected[j])) < 1e-3;
        assert(err1);
    }
 
    std::cout << "Test 1 passed! - " << result.first << " iterations, "
              << result.second << " accuracy"
              << "\n";
}

Here is the call graph for this function:

test2()

void test2

(

)

Self test the algorithm by checking the roots for \(0.015625x^3-1=0\) to which the roots are \((4+0i),\,(-2\pm3.464i)\)

             {
    const std::valarray<double> coeffs = {// 0.015625 x^3 - 1 = 0
                                          1. / 64., 0., 0., -1.};
    std::valarray<std::complex<double>> roots(3);
    const std::valarray<std::complex<double>> expected = {
        std::complex<double>(4., 0.), std::complex<double>(-2., 3.46410162),
        std::complex<double>(-2., -3.46410162)  // known expected roots
    };
 
    /* initialize root approximations with random values */
    for (int n = 0; n < roots.size(); n++) {
        roots[n] = std::complex<double>(std::rand() % 100, std::rand() % 100);
        roots[n] -= 50.f;
        roots[n] /= 25.f;
    }
 
    auto result = durand_kerner_algo(coeffs, &roots, false);
 
    for (int i = 0; i < roots.size(); i++) {
        // check if approximations are have < 0.1% error with one of the
        // expected roots
        bool err1 = false;
        for (int j = 0; j < roots.size(); j++)
            err1 |= std::abs(std::abs(roots[i] - expected[j])) < 1e-3;
        assert(err1);
    }
 
    std::cout << "Test 2 passed! - " << result.first << " iterations, "
              << result.second << " accuracy"
              << "\n";
}

Here is the call graph for this function: