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150 lines
4.0 KiB
C++
150 lines
4.0 KiB
C++
/**
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* \file
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* \brief Find extrema of a univariate real function in a given interval using
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* [golden section search
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* algorithm](https://en.wikipedia.org/wiki/Golden-section_search).
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*
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* \author [Krishna Vedala](https://github.com/kvedala)
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*/
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#define _USE_MATH_DEFINES //< required for MS Visual C++
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#include <cassert>
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#include <cmath>
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#include <functional>
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#include <iostream>
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#include <limits>
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#define EPSILON 1e-7 ///< solution accuracy limit
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/**
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* @brief Get the minima of a function in the given interval. To get the maxima,
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* simply negate the function. The golden ratio used here is:\f[
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* k=\frac{3-\sqrt{5}}{2} \approx 0.381966\ldots\f]
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*
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* @param f function to get minima for
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* @param lim_a lower limit of search window
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* @param lim_b upper limit of search window
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* @return local minima found in the interval
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*/
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double get_minima(const std::function<double(double)> &f, double lim_a,
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double lim_b) {
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uint32_t iters = 0;
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double c, d;
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double prev_mean, mean = std::numeric_limits<double>::infinity();
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// golden ratio value
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const double M_GOLDEN_RATIO = (1.f + std::sqrt(5.f)) / 2.f;
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// ensure that lim_a < lim_b
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if (lim_a > lim_b) {
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std::swap(lim_a, lim_b);
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} else if (std::abs(lim_a - lim_b) <= EPSILON) {
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std::cerr << "Search range must be greater than " << EPSILON << "\n";
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return lim_a;
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}
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do {
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prev_mean = mean;
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// compute the section ratio width
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double ratio = (lim_b - lim_a) / M_GOLDEN_RATIO;
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c = lim_b - ratio; // right-side section start
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d = lim_a + ratio; // left-side section end
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if (f(c) < f(d)) {
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// select left section
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lim_b = d;
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} else {
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// selct right section
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lim_a = c;
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}
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mean = (lim_a + lim_b) / 2.f;
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iters++;
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// continue till the interval width is greater than sqrt(system epsilon)
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} while (std::abs(lim_a - lim_b) > EPSILON);
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std::cout << " (iters: " << iters << ") ";
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return prev_mean;
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}
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/**
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* @brief Test function to find minima for the function
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* \f$f(x)= (x-2)^2\f$
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* in the interval \f$[1,5]\f$
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* \n Expected result = 2
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*/
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void test1() {
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// define the function to minimize as a lambda function
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std::function<double(double)> f1 = [](double x) {
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return (x - 2) * (x - 2);
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};
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std::cout << "Test 1.... ";
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double minima = get_minima(f1, 1, 5);
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std::cout << minima << "...";
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assert(std::abs(minima - 2) < EPSILON);
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std::cout << "passed\n";
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}
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/**
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* @brief Test function to find *maxima* for the function
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* \f$f(x)= x^{\frac{1}{x}}\f$
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* in the interval \f$[-2,10]\f$
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* \n Expected result: \f$e\approx 2.71828182845904509\f$
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*/
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void test2() {
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// define the function to maximize as a lambda function
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// since we are maximixing, we negated the function return value
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std::function<double(double)> func = [](double x) {
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return -std::pow(x, 1.f / x);
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};
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std::cout << "Test 2.... ";
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double minima = get_minima(func, -2, 10);
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std::cout << minima << " (" << M_E << ")...";
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assert(std::abs(minima - M_E) < EPSILON);
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std::cout << "passed\n";
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}
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/**
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* @brief Test function to find *maxima* for the function
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* \f$f(x)= \cos x\f$
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* in the interval \f$[0,12]\f$
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* \n Expected result: \f$\pi\approx 3.14159265358979312\f$
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*/
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void test3() {
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// define the function to maximize as a lambda function
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// since we are maximixing, we negated the function return value
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std::function<double(double)> func = [](double x) { return std::cos(x); };
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std::cout << "Test 3.... ";
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double minima = get_minima(func, -4, 12);
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std::cout << minima << " (" << M_PI << ")...";
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assert(std::abs(minima - M_PI) < EPSILON);
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std::cout << "passed\n";
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}
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/** Main function */
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int main() {
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std::cout.precision(9);
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std::cout << "Computations performed with machine epsilon: " << EPSILON
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<< "\n";
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test1();
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test2();
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test3();
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return 0;
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}
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