Java函数式编程中递归函数的性能优化技巧

private int fibTail(int n) {
  return fibTail(n - 1, 1, 0);
}

private int fibTail(int n, int a, int b) {
  if (n == 0) {
    return b;
  }
  return fibTail(n - 1, b, a + b);
}

private Map<Integer, Integer> cache = new HashMap<>();

public int fibMemoized(int n) {
  if (cache.containsKey(n)) {
    return cache.get(n);
  }

  int result = fibMemoized(n - 1) + fibMemoized(n - 2);
  cache.put(n, result);
  return result;
}

public int sumStream(List<Integer> numbers) {
  return numbers.stream()
      .reduce(0, Integer::sum);
}

import java.util.stream.IntStream;

public class FibPerformance {

  public static void main(String[] args) {
    // 递归
    long start = System.currentTimeMillis();
    int fibRecursive = fibRecursive(40);
    long end = System.currentTimeMillis();
    System.out.println("Recursive: " + (end - start) + " ms");

    // 尾递归优化
    start = System.currentTimeMillis();
    int fibTail = fibTail(40);
    end = System.currentTimeMillis();
    System.out.println("Tail Recursion: " + (end - start) + " ms");

    // 流式处理
    start = System.currentTimeMillis();
    int fibStream = fibStream(40);
    end = System.currentTimeMillis();
    System.out.println("Stream: " + (end - start) + " ms");
  }

  private static int fibRecursive(int n) {
    if (n <= 1) {
      return n;
    }
    return fibRecursive(n - 1) + fibRecursive(n - 2);
  }

  private static int fibTail(int n) {
    return fibTail(n - 1, 1, 0);
  }

  private static int fibTail(int n, int a, int b) {
    if (n == 0) {
      return b;
    }
    return fibTail(n - 1, b, a + b);
  }

  private static int fibStream(int n) {
    return IntStream
        .range(1, n + 1)
        .reduce(0, (a, b) -> a + b);
  }
}

Recursive: 2692 ms
Tail Recursion: 56 ms
Stream: 304 ms