#6219: Add Monte Carlo Integral Approximation

Author: MuhammadEzzatHBK

src/main/java/com/thealgorithms/randomized/MonteCarloIntegration.java

@@ -0,0 +1,72 @@
+package com.thealgorithms.randomized;
+
+import java.util.Random;
+import java.util.function.Function;
+
+/**
+ * A demonstration of the Monte Carlo integration algorithm in Java.
+ *
+ * <p>This class estimates the value of definite integrals using randomized sampling,
+ * also known as the Monte Carlo method. It is particularly effective for:
+ * <ul>
+ *   <li>Functions that are difficult or impossible to integrate analytically</li>
+ *   <li>High-dimensional integrals where traditional methods are inefficient</li>
+ *   <li>Simulation and probabilistic analysis tasks</li>
+ * </ul>
+ *
+ * <p>The core idea is to sample random points uniformly from the integration domain,
+ * evaluate the function at those points, and compute the scaled average to estimate the integral.
+ *
+ * <p>For a one-dimensional integral over [a, b], the approximation is the function range (b-a),
+ * multiplied by the function average result for a random sample.
+ * See more: <a href="https://en.wikipedia.org/wiki/Monte_Carlo_integration">Monte Carlo Integration</a>
+ *
+ * @author: MuhammadEzzatHBK
+ */
+
+public final class MonteCarloIntegration {
+
+    private MonteCarloIntegration() {
+    }
+
+    /**
+     * Approximates the definite integral of a given function over a specified
+     * interval using the Monte Carlo method with a fixed random seed for
+     * reproducibility.
+     *
+     * @param fx    the function to integrate
+     * @param a     the lower bound of the interval
+     * @param b     the upper bound of the interval
+     * @param n     the number of random samples to use
+     * @param seed  the seed for the random number generator
+     * @return      the approximate value of the integral
+     */
+    public static double approximate(Function<Double, Double> fx, double a, double b, int n, long seed) {
+        return doApproximate(fx, a, b, n, new Random(seed));
+    }
+
+    /**
+     * Approximates the definite integral of a given function over a specified
+     * interval using the Monte Carlo method with a random seed based on the
+     * current system time for more randomness.
+     *
+     * @param fx    the function to integrate
+     * @param a     the lower bound of the interval
+     * @param b     the upper bound of the interval
+     * @param n     the number of random samples to use
+     * @return      the approximate value of the integral
+     */
+    public static double approximate(Function<Double, Double> fx, double a, double b, int n) {
+        return doApproximate(fx, a, b, n, new Random(System.currentTimeMillis()));
+    }
+
+    private static double doApproximate(Function<Double, Double> fx, double a, double b, int n, Random generator) {
+        double totalArea = 0.0;
+        double range = b - a;
+        for (int i = 0; i < n; i++) {
+            double x = a + generator.nextDouble() * range;
+            totalArea += fx.apply(x);
+        }
+        return range * totalArea / n;
+    }
+}

src/test/java/com/thealgorithms/randomized/MonteCarloIntegrationTest.java

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+package com.thealgorithms.randomized;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+import java.util.function.Function;
+import org.junit.jupiter.api.Test;
+
+class MonteCarloIntegrationTest {
+
+    private static final double EPSILON = 0.03; // Allow 3% error margin
+
+    @Test
+    void testConstantFunction() {
+        // Integral of f(x) = 2 from 0 to 1 is 2
+        Function<Double, Double> constant = x -> 2.0;
+        double result = MonteCarloIntegration.approximate(constant, 0, 1, 10000);
+        assertEquals(2.0, result, EPSILON);
+    }
+
+    @Test
+    void testLinearFunction() {
+        // Integral of f(x) = x from 0 to 1 is 0.5
+        Function<Double, Double> linear = x -> x;
+        double result = MonteCarloIntegration.approximate(linear, 0, 1, 10000);
+        assertEquals(0.5, result, EPSILON);
+    }
+
+    @Test
+    void testQuadraticFunction() {
+        // Integral of f(x) = x^2 from 0 to 1 is 1/3
+        Function<Double, Double> quadratic = x -> x * x;
+        double result = MonteCarloIntegration.approximate(quadratic, 0, 1, 10000);
+        assertEquals(1.0 / 3.0, result, EPSILON);
+    }
+
+    @Test
+    void testLargeSampleSize() {
+        // Integral of f(x) = x^2 from 0 to 1 is 1/3
+        Function<Double, Double> quadratic = x -> x * x;
+        double result = MonteCarloIntegration.approximate(quadratic, 0, 1, 50000000);
+        assertEquals(1.0 / 3.0, result, EPSILON / 2); // Larger sample size, smaller error margin
+    }
+
+    @Test
+    void testReproducibility() {
+        Function<Double, Double> linear = x -> x;
+        double result1 = MonteCarloIntegration.approximate(linear, 0, 1, 10000, 42L);
+        double result2 = MonteCarloIntegration.approximate(linear, 0, 1, 10000, 42L);
+        assertEquals(result1, result2, 0.0); // Exactly equal
+    }
+
+    @Test
+    void testNegativeInterval() {
+        // Integral of f(x) = x from -1 to 1 is 0
+        Function<Double, Double> linear = x -> x;
+        double result = MonteCarloIntegration.approximate(linear, -1, 1, 10000);
+        assertEquals(0.0, result, EPSILON);
+    }
+}