除了Elias的答案，当使用有符号整数实现时，会导致未定义行为，当使用无符号整数实现时，会导致高输入的不正确值，

下面是平方求幂的修改版本，它也适用于有符号整数类型，并且不会给出错误的值:

#include <stdint.h>

#define SQRT_INT64_MAX (INT64_C(0xB504F333))

int64_t alx_pow_s64 (int64_t base, uint8_t exp)
{
    int_fast64_t    base_;
    int_fast64_t    result;

    base_   = base;

    if (base_ == 1)
        return  1;
    if (!exp)
        return  1;
    if (!base_)
        return  0;

    result  = 1;
    if (exp & 1)
        result *= base_;
    exp >>= 1;
    while (exp) {
        if (base_ > SQRT_INT64_MAX)
            return  0;
        base_ *= base_;
        if (exp & 1)
            result *= base_;
        exp >>= 1;
    }

    return  result;
}

使用该函数的注意事项:

(1 ** N) == 1
(N ** 0) == 1
(0 ** 0) == 1
(0 ** N) == 0

如果将发生任何溢出或换行，则返回0;

I used int64_t, but any width (signed or unsigned) can be used with little modification. However, if you need to use a non-fixed-width integer type, you will need to change SQRT_INT64_MAX by (int)sqrt(INT_MAX) (in the case of using int) or something similar, which should be optimized, but it is uglier, and not a C constant expression. Also casting the result of sqrt() to an int is not very good because of floating point precission in case of a perfect square, but as I don't know of any implementation where INT_MAX -or the maximum of any type- is a perfect square, you can live with that.

迟到的人:

下面是一个尽可能处理y < 0的解。

It uses a result of intmax_t for maximum range. There is no provision for answers that do not fit in intmax_t. powjii(0, 0) --> 1 which is a common result for this case. pow(0,negative), another undefined result, returns INTMAX_MAX intmax_t powjii(int x, int y) { if (y < 0) { switch (x) { case 0: return INTMAX_MAX; case 1: return 1; case -1: return y % 2 ? -1 : 1; } return 0; } intmax_t z = 1; intmax_t base = x; for (;;) { if (y % 2) { z *= base; } y /= 2; if (y == 0) { break; } base *= base; } return z; }

这段代码使用了一个永久循环for(;;)，以避免在其他循环解决方案中常见的最终基数*=基数。这个乘法是1)不需要的，2)可能是int*int溢出，也就是UB。

我已经实现了记忆所有计算权力的算法，然后在需要时使用它们。比如x^13等于(x^2)^2^2 * x^2 * x其中x^2^2是从表中取出来的而不是再计算一次。这基本上是@Pramod answer的实现(但在c#中)。 需要的乘法数是Ceil(Log n)

public static int Power(int base, int exp)
{
    int tab[] = new int[exp + 1];
    tab[0] = 1;
    tab[1] = base;
    return Power(base, exp, tab);
}

public static int Power(int base, int exp, int tab[])
    {
         if(exp == 0) return 1;
         if(exp == 1) return base;
         int i = 1;
         while(i < exp/2)
         {  
            if(tab[2 * i] <= 0)
                tab[2 * i] = tab[i] * tab[i];
            i = i << 1;
          }
    if(exp <=  i)
        return tab[i];
     else return tab[i] * Power(base, exp - i, tab);
}

平方求幂。

int ipow(int base, int exp)
{
    int result = 1;
    for (;;)
    {
        if (exp & 1)
            result *= base;
        exp >>= 1;
        if (!exp)
            break;
        base *= base;
    }

    return result;
}

这是在非对称密码学中对大数进行模求幂的标准方法。

下面是一个计算x ** y的O(1)算法，灵感来自这条评论。它适用于32位有符号int。

对于较小的y值，它使用平方求幂。对于较大的y值，只有少数x值的结果不会溢出。这个实现使用一个查找表来读取结果而不进行计算。

对于溢出，C标准允许任何行为，包括崩溃。但是，我决定对LUT索引进行边界检查，以防止内存访问违反，这可能是令人惊讶和不受欢迎的。

伪代码:

If `x` is between -2 and 2, use special-case formulas.
Otherwise, if `y` is between 0 and 8, use special-case formulas.
Otherwise:
    Set x = abs(x); remember if x was negative
    If x <= 10 and y <= 19:
        Load precomputed result from a lookup table
    Otherwise:
        Set result to 0 (overflow)
    If x was negative and y is odd, negate the result

C代码:

#define POW9(x) x * x * x * x * x * x * x * x * x
#define POW10(x) POW9(x) * x
#define POW11(x) POW10(x) * x
#define POW12(x) POW11(x) * x
#define POW13(x) POW12(x) * x
#define POW14(x) POW13(x) * x
#define POW15(x) POW14(x) * x
#define POW16(x) POW15(x) * x
#define POW17(x) POW16(x) * x
#define POW18(x) POW17(x) * x
#define POW19(x) POW18(x) * x

int mypow(int x, unsigned y)
{
    static int table[8][11] = {
        {POW9(3), POW10(3), POW11(3), POW12(3), POW13(3), POW14(3), POW15(3), POW16(3), POW17(3), POW18(3), POW19(3)},
        {POW9(4), POW10(4), POW11(4), POW12(4), POW13(4), POW14(4), POW15(4), 0, 0, 0, 0},
        {POW9(5), POW10(5), POW11(5), POW12(5), POW13(5), 0, 0, 0, 0, 0, 0},
        {POW9(6), POW10(6), POW11(6), 0, 0, 0, 0, 0, 0, 0, 0},
        {POW9(7), POW10(7), POW11(7), 0, 0, 0, 0, 0, 0, 0, 0},
        {POW9(8), POW10(8), 0, 0, 0, 0, 0, 0, 0, 0, 0},
        {POW9(9), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0},
        {POW9(10), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
    };

    int is_neg;
    int r;

    switch (x)
    {
    case 0:
        return y == 0 ? 1 : 0;
    case 1:
        return 1;
    case -1:
        return y % 2 == 0 ? 1 : -1;
    case 2:
        return 1 << y;
    case -2:
        return (y % 2 == 0 ? 1 : -1) << y;
    default:
        switch (y)
        {
        case 0:
            return 1;
        case 1:
            return x;
        case 2:
            return x * x;
        case 3:
            return x * x * x;
        case 4:
            r = x * x;
            return r * r;
        case 5:
            r = x * x;
            return r * r * x;
        case 6:
            r = x * x;
            return r * r * r;
        case 7:
            r = x * x;
            return r * r * r * x;
        case 8:
            r = x * x;
            r = r * r;
            return r * r;
        default:
            is_neg = x < 0;
            if (is_neg)
                x = -x;
            if (x <= 10 && y <= 19)
                r = table[x - 3][y - 9];
            else
                r = 0;
            if (is_neg && y % 2 == 1)
                r = -r;
            return r;
        }
    }
}

请注意，平方求幂并不是最优的方法。这可能是一种适用于所有指数值的通用方法，但对于特定的指数值，可能有更好的序列，需要更少的乘法。

例如，如果你想计算x^15，用平方求幂的方法会给你:

x^15 = (x^7)*(x^7)*x 
x^7 = (x^3)*(x^3)*x 
x^3 = x*x*x

这一共有6次乘法。

事实证明，这可以通过“仅仅”5次加法链幂运算来完成。

n*n = n^2
n^2*n = n^3
n^3*n^3 = n^6
n^6*n^6 = n^12
n^12*n^3 = n^15

没有有效的算法来找到这个最优的乘法序列。从维基百科:

The problem of finding the shortest addition chain cannot be solved by dynamic programming, because it does not satisfy the assumption of optimal substructure. That is, it is not sufficient to decompose the power into smaller powers, each of which is computed minimally, since the addition chains for the smaller powers may be related (to share computations). For example, in the shortest addition chain for a¹⁵ above, the subproblem for a⁶ must be computed as (a³)² since a³ is re-used (as opposed to, say, a⁶ = a²(a²)², which also requires three multiplies).