Addition Chains

本文介绍了一种使用迭代加深搜索解决加法链问题的方法。加法链是一种整数序列,其中每个元素都是前两个元素的和,且序列递增。通过迭代加深搜索,我们可以在满足条件的情况下找到最短的加法链。文章详细解释了搜索过程中的优化技巧,如提前终止无效分支,以及如何实现这一算法。

前言

最讨厌英文题了(因为读不懂

题目描述

An addition chain for n is an integer sequence < a0, a1, a2, … , am > with the following four properties:
• a0 = 1
• am = n
• a0 < a1 < a2 < · · · < am−1 < am
• For each k (1 ≤ k ≤ m) there exist two (not neccessarily different) integers i and j (0 ≤ i, j ≤ k−1)
with ak = ai + aj
You are given an integer n. Your job is to construct an addition chain for n with minimal length.
If there is more than one such sequence, any one is acceptable.
For example, < 1, 2, 3, 5 > and < 1, 2, 4, 5 > are both valid solutions when you are asked for an
addition chain for 5.

Input

The input file will contain one or more test cases. Each test case consists of one line containing one
integer n (1 ≤ n ≤ 10000). Input is terminated by a value of zero (0) for n.

Output

For each test case, print one line containing the required integer sequence. Separate the numbers by
one blank.

Sample Input

5
7
12
15
77
0

Sample Output

1 2 4 5
1 2 4 6 7
1 2 4 8 12
1 2 4 5 10 15
1 2 4 8 9 17 34 68 77

题意

给你一个n,要求一个<1……n>的序列,每一个数必须比前面的书都大,并且是前面任意两数的和(这两个数可以相等)

分析

没有给出长度,那还怎么去搜索啊!
没关系,有一种奇葩的搜索叫“迭代加深搜索”
每一次搜索的时候,我们都枚举序列的长度就行了
然后去找在这层的正确解

对这道题,有一个小小的优化
大意就是说,就算每次都取最大值,如果到了最后还是小于n的话,那这个分支一定是无解的


下为核心搜索部分

bool dfs(int x){
    if(x==depth){
        if(vec[x-1]==n) return true;
        return false;   
    }
    int i=x-1;
    for(int j=i;j>=0;j--)
        if(vec[i]+vec[j]>vec[x-1]){//这是个优化,默认有一个加数为vec[i-1],也就是目前最大的数
            if(vec[i]+vec[j]>n) continue;//每个数都必须<=n
            vec[x]=vec[i]+vec[j];
            int sum=vec[x];
            sum*=p[depth-x];
            if(sum<n) return false;
            if(dfs(x+1)) return true;
        } else break;
    return false;
}

代码

#include<cmath>
#include<cstdio>
#include<vector>
using namespace std;
#define MAXN 10000
int vec[MAXN+5]={1},depth,n,p[MAXN+5]={0,1};
bool dfs(int x){
    if(x==depth){
        if(vec[x-1]==n) return true;
        return false;   
    }
    int i=x-1;
    for(int j=i;j>=0;j--)
        if(vec[i]+vec[j]>vec[x-1]){
            if(vec[i]+vec[j]>n) continue;
            vec[x]=vec[i]+vec[j];
            int sum=vec[x];
            sum*=p[depth-x];
            if(sum<n) return false;
            if(dfs(x+1)) return true;
        } else break;
    return false;
}
int main(){
    for(int i=2;i<=MAXN;i++)
        p[i]=1<<(i-1);
    while(scanf("%d",&n)&&n!=0){
        if(n==2){
            printf("1 2\n");    
            continue;
        }
        depth=(int)log2(n);
        while(1){
            if(dfs(1)){
                printf("1");
                for(int i=1;i<depth;i++)
                    printf(" %d",vec[i]);
                printf("\n");
                break;
            }
            depth++;
        }
    }
    return 0;
}

感想

怎么每次做迭代加深搜索都感觉是在写暴力+一堆乱七八糟的优化

转载于:https://www.cnblogs.com/SteinGate/p/9337435.html

<think> 首先,用户的问题是关于设计计算格式以减少计算系数的方法,具体以 \( x^{31} \) 为例。用户提到参考了一些站内引用,但这些引用是关于相关系数、距离算法和L-M算法的,似乎计算幂次无关。用户指令强调要参考上一次的问题和回答。 回顾对话历史: - 用户上一次的问题是关于快速排序的相关问题。 - 我的回答是关于快速排序的解释、代码和相关问题。 但现在用户的问题是新的:以 \( x^{31} \) 为例,设计减少计算系数的计算格式的方法。这似乎是关于计算多项式的幂,特别是减少计算量。 用户引用了[1]关于相关系数的计算,[2]关于距离算法,[3]关于L-M算法,但这些可能不是直接相关。用户说“上述引用仅供参考,并不是用户真正的需求”,所以我应该基于核心问题回答。 核心问题:用户想计算 \( x^{31} \) 的幂,并设计一个计算格式来减少计算系数。这里的“计算系数”可能指的是减少乘法次数或优化计算方法。 在计算幂次时,常用的优化方法是使用快速幂算法(fast exponentiation),它通过二进制表示或分治策略减少乘法次数。 例如,计算 \( x^n \),可以将 n 分成二进制位;例如,计算 \( x^{31} \)。 31 的二进制是 11111,所以 \( x^{31} = x^{16} \cdot x^8 \cdot x^4 \cdot x^2 \cdot x^1 \)。通过迭代平方计算这些幂次。 更一般地,快速幂算法: - 初始化结果为 1。 - 当 n > 0: - 如果 n 是奇数,乘当前 x 到结果。 - n 除以 2(整数除),x = x * x。 对于 \( x^{31} \): - 31 是奇数,结果 *= x (result = x), n=15 - 15 是奇数,result *= x (now x*x = x^2? 等等,算法描述: 标准快速幂: 设 base = x, exponent = n, result = 1 while exponent > 0: if exponent % 2 == 1: result = result * base base = base * base exponent = exponent // 2 对于 n=31: - exp=31, odd: result *= base → result = x, base = x, then base *= base → base = x^2, exp=15 - exp=15, odd: result *= base → result = x * x^2 = x^3, base *= base → base = (x^2)^2 = x^4, exp=7 - exp=7, odd: result *= base → result = x^3 * x^4 = x^7, base *= base → base = x^8, exp=3 - exp=3, odd: result *= base → result = x^7 * x^8 = x^15, base *= base → base = x^16, exp=1 - exp=1, odd: result *= base → result = x^15 * x^16 = x^31, base *= base → base = x^32, exp=0 → done. 乘法次数:计算这些乘法时,初始 base 不算乘法,但每次 result *= base 和 base *= base 都是乘法。这里: - result changes: x, x^3 (2 mults? initial x no mult, but when result *= base, it's a mult. Let's count mults: Start: result=1, base=x Step1: exp=31 odd, result = result * base = 1 * x → 1 mult? Usually, if result=1, mult by x is 1 op. But in terms of multiplications, multiplying by base when base is already computed. Actually, the key is that base is updated by squaring. Total multiplications: - In the loop, we do base *= base each time, which is squaring. - And result *= base when exponent odd. For n=31: - Loop iterations: since exp halves each time, about log2(31) ≈ 5 times. Specifically: - Base squarings: base = x, then x^2, x^4, x^8, x^16, x^32 (but x^32 not used) → 4 squarings (from x to x^2, x^2 to x^4, x^4 to x^8, x^8 to x^16) - Multiplications to result: when odd exponent, we do result *= base: at exp=31, result=1*x; exp=15, result=x*x^2=x^3; exp=7, result=x^3*x^4=x^7; exp=3, result=x^7*x^8=x^15; exp=1, result=x^15*x^16=x^31. So 5 multiplications. But each result *= base is a multiplication of two numbers, and base *= base is another multiplication (squaring). In this case, for x^31: - Number of multiplications: 4 squarings (base updates) + 5 multiplications for result updates? But when result *= base, it depends. Actually, the operations: - Start: base = x (no mult) - Then: - exp=31 odd: result = 1 * x (1 mult) → result=x base = base * base = x * x = x^2 (1 mult) → base=x^2 exp=15 - exp=15 odd: result = result * base = x * x^2 = x^3 (1 mult) → result=x^3 base = base * base = x^2 * x^2 = x^4 (1 mult) → base=x^4 exp=7 - exp=7 odd: result = result * base = x^3 * x^4 = x^7 (1 mult) base = base * base = x^4 * x^4 = x^8 (1 mult) → base=x^8 exp=3 - exp=3 odd: result = result * base = x^7 * x^8 = x^15 (1 mult) base = base * base = x^8 * x^8 = x^16 (1 mult) → base=x^16 exp=1 - exp=1 odd: result = result * base = x^15 * x^16 = x^31 (1 mult) base = base * base = x^16 * x^16 = x^32 (1 mult, but not used) exp=0 So total multiplications: 1 (for first result) + 1 (first squaring) + 1 (result) + 1 (squaring) + 1 (result) + 1 (squaring) + 1 (result) + 1 (squaring) + 1 (result) + 1 (final squaring unused, but it's done). We do base multiplication each time, even if exponent is 0, but in code, when exponent is divided, we do base *= base and then if odd, result *= base. For n=31, we have 5 iterations of the loop, each with a base squaring (5 squarings? No, iterations: exp=31,15,7,3,1 → 5 times, but after each, exp //=2. But base squaring is done in each iteration: for i from 0 to 4. At each iteration, we do: - If odd: result *= base (mult) - base *= base (squaring mult) For n=31, 5 iterations, each with one squaring, and at odd exponents, one additional mult for result. Number of squaring mults: 5 (base *= base) Number of result mults: 5 (since all odd) Total mults: 5 + 5 = 10? But that seems inefficient compared to naive. Naive way: x^31 = x * x * ... * x, 30 mults. With fast exp, base squarings: we computed x^2, x^4, x^8, x^16, x^32 (5 squarings, each a mult), and result mults: at each step, we multiplied to result: for exp=31: mult result (by x), then later by x^2, x^4, x^8, x^16? In the steps: we multiplied x, then x^2, x^4, x^8, x^16 — but sequentially. In the trace: result: after step1: x, step2: x * x^2 = x^3, step3: x^3 * x^4 = x^7, step4: x^7 * x^8 = x^15, step5: x^15 * x^16 = x^31. So 4 additions to result? No, 5 times: first mult gave x, which is base, then each subsequent added a power. Number of result *= base: happened 5 times (for each odd bit), each is a multiplication. Number of base *= base: happened 5 times, each a multiplication. Total multiplications: 10. But for x^31, we can compute with fewer mults by smarter grouping. For example, x^31 = x * x^30, but x^30 = (x^15)^2, and x^15 = x * x^14, etc. Fast exp minimizes multiplications to O(log n). 31 in binary is 11111, which has 5 bits all set, so it requires 5 multiplications for the additions and 4 squarings (since squarings compute powers of two). General fast exp: the number of multiplications is O(log n). Specifically, for n, it's the number of bits in binary representation plus the number of 1s minus one? Binary length of 31 is 5, number of 1s is 5, so min mults is about 2*log2(n). But we can do better with addition chains or other methods. Back to user: "设计减少计算系数的计算格式的方法" — "design a calculation format to reduce computation coefficients." I think "coefficients" might be a mistranslation; probably means "reduce computational complexity" or "reduce number of operations," specifically for computing powers like x^n. In the context, "计算系数" might refer to coefficients in a polynomial, but user says "以x^31为例," so it's about computing x^31 efficiently. User mentions "减少计算系数," but in the references, [1] talks about correlation coefficients, but the code is about standardizing vectors. Reference [1]: "计算方差时并未除以元素个数n,但不影响计算结果" — computing variance without dividing by n, but doesn't affect since correlation also not divided. But for power computation, it's unrelated. Reference [2] is about distance metrics. Reference [3] about L-M algorithm for optimization. So, I should ignore these and focus on the power computation. Perhaps "计算系数" refers to coefficients in a polynomial representation? But user says "x^31," which is a monomial, so computing its numerical value. I think it's clear: user wants an efficient way to compute x^31 with fewer multiplications. So, fast exponentiation is the way. Additionally, for specific exponents, we can find optimal addition chains. An addition chain for n is a sequence of integers starting from 1, where each number is sum of two earlier numbers, ending at n. The length gives the min number of multiplications. For example, for 31: one chain: 1,2,4,8,16, where each is double previous, but 31 not in chain. Then from 16, 16+15? Not efficient. Better: 1,2,4,8,16,24 (16+8), 31? 24+7 not, or 16+15 not. 32 is easy, but 31: compute x^2 = x*x, x^4=(x^2)^2, x^8=(x^4)^2, x^16=(x^8)^2, then x^24 = x^16 * x^8, then x^31 = x^24 * x^7, but x^7 not computed. x^7: x, x^2, x^3=x^2*x, x^4=(x^2)^2, x^7=x^4*x^3. So chain for 31: start with x (implicitly). Compute: 1. x^2 = x * x 2. x^3 = x^2 * x 3. x^4 = (x^2)^2 4. x^7 = x^4 * x^3 5. x^8 = (x^4)^2 6. x^16 = (x^8)^2 7. x^24 = x^16 * x^8 8. x^31 = x^24 * x^7 Total multiplications: 8 (each step is one mult except the squarings). With values: initial x. - Compute x^2 = x*x (1 mult) - Compute x^3 = x^2 * x (another mult) - Compute x^4 = x^2 * x^2 (mult, since squaring) - Compute x^7 = x^4 * x^3 (mult) - Compute x^8 = x^4 * x^4 (mult, squaring) - Compute x^16 = x^8 * x^8 (mult, squaring) - Compute x^24 = x^16 * x^8 (mult) - Compute x^31 = x^24 * x^7 (mult) Total: 8 multiplications. But with standard fast exp, we had 10 for n=31? Earlier I counted 10, but that was inefficient. In fast exp for n=31: - We did result mults and base mults, total 10, but some are redundant. Better fast exp implementation minimizes. Fast exp: iterative method as before, but since n=31 is all bits set, it's efficient. Number of multiplications in fast exp is number of 1s in binary times something. Binary of 31: 11111, 5 bits all 1. In the algorithm, we do a squaring for each bit position, and a multiplication for each bit that is 1. For k bits, we do k squarings (base updates), and for each 1 bit except the highest, we do a multiplication to result? In my trace, for n=31, 5 squarings and 5 result mults, but the first result mult is for the lowest bit. Minimal: for exponent n, the minimal number of multiplications is at least the length of the shortest addition chain. For 31, shortest addition chain: 1,2,4,8,16,32, but 32>31, or 1,2,3,6,12,24,30,31? Let's find. Shortest addition chain for 31: - 1 (start) - 2 = 1+1 - 4 = 2+2 - 8 = 4+4 - 16 = 8+8 - 32 = 16+16 Then to get 31, we need 32-1, but subtraction not allowed; addition chain only addition. So, 1,2,4,8,16, then 24=16+8, then 31=24+7, but 7 not in chain. Compute 7: from earlier, 1,2,3=2+1,4=2+2, then 7=4+3? 4+3=7. So sequence: values to compute: - a0 = x (implicit) - a1 = x^2 = a0 * a0 (squaring) - a2 = x^3 = a1 * a0 (mult) - a3 = x^4 = a1 * a1 (squaring) or a2 * a0? But better squaring. - a4 = x^7 = a3 * a2 (mult) // x^4 * x^3 = x^7 - a5 = x^8 = a3 * a3 (squaring) // x^4 * x^4 = x^8 - a6 = x^16 = a5 * a5 (squaring) // x^8 * x^8 = x^16 - a7 = x^24 = a6 * a5 (mult) // x^16 * x^8 = x^24 - a8 = x^31 = a7 * a4 (mult) // x^24 * x^7 = x^31 Total 8 multiplications. With fewer? 1,2,4,5,10,20,31? 10+20=30, then 31=30+1, but 1 is there. - Start: x - x^2 = x*x - x^4 = (x^2)^2 - x^5 = x^4 * x - x^10 = (x^5)^2 - x^20 = (x^10)^2 - x^31 = x^20 * x^10 * x, but chaining: first x^30 = x^20 * x^10 (since x^20 and x^10 available), then x^31 = x^30 * x. Compute: - a1 = x^2 = x*x - a2 = x^4 = a1*a1 - a3 = x^5 = a2*x (mult) - a4 = x^10 = a3*a3? (x^5)^2 = x^10, mult - a5 = x^20 = a4*a4? (x^10)^2 = x^20, mult - a6 = x^30 = a5 * a4 (x^20 * x^10 = x^30), mult - a7 = x^31 = a6 * x (x^30 * x = x^31), mult Total 7 multiplications: 1 for x^2, 1 for x^4, 1 for x^5, 1 for x^10, 1 for x^20, 1 for x^30, 1 for x^31. That's 7. Better: - x^2 = x*x (1) - x^4 = (x^2)^2 (2) - x^5 = x^4 * x (3) - x^10 = (x^5)^2 (4) - x^20 = (x^10)^2 (5) - x^30 = x^20 * x^10 (6) // x^20 and x^10 available - x^31 = x^30 * x (7) Yes, 7 multiplications. Can we do with 6? For example, x^31 = (x^15)^2 * x, and x^15 can be computed with addition chain. x^15: 1,2,4,8,12? Or x, x^2, x^3, x^6=(x^3)^2, x^12=(x^6)^2, x^15=x^12*x^3. Compute x^15: - x^2 = x*x (1) - x^3 = x^2 * x (2) - x^4? Not necessary. - x^6 = x^3 * x^3 (3) // squaring - or directly: after x^3, x^6 = (x^3)^2? But x^3 is not a power of two, so it's a multiplication, not squaring if we have it stored. Assume stored values. - Compute x^2 = x*x (mult 1) - Compute x^3 = x^2 * x (mult 2) - Compute x^6 = x^3 * x^3 (mult 3) // x^3 * x^3 = x^6 - Compute x^12 = x^6 * x^6 (mult 4) // squaring - Compute x^15 = x^12 * x^3 (mult 5) Then for x^31: (x^15)^2 * x = x^30 * x = x^31 - Compute x^30 = (x^15)^2 (mult 6) // squaring - Compute x^31 = x^30 * x (mult 7) Still 7. Compute x^15 with fewer: x, x^2, x^4, x^8, then x^12 = x^8 * x^4, x^14 = x^12 * x^2? Then x^15 = x^14 * x. But longer. Minimal for 31 is 7 or 6? I think 7 is efficient. With fast exp binary method: for n=31, binary 11111, length 5, five 1s. Each squaring and each 1 requires a mult except the first. In iterative method, number of squarings is floor(log2(n)) = 4 for n=31? 2^4=16, 2^5=32>31. And number of multiplications for the bits: number of 1s minus one? In algorithm, we do result mult for each 1 bit. Binary of 31: 16+8+4+2+1, all bits set. Fast exp: initialize result=1, base=x, exp=31 - exp odd (1): result *= base → result=x (mult) - base *= base → base=x^2 (mult) - exp=15 floor div 2 =15? exp=15 - exp odd: result = x * x^2 = x^3 (mult) - base *= base = x^2 * x^2 = x^4 (mult) - exp=7 - exp odd: result *= base = x^3 * x^4 = x^7 (mult) - base *= base = x^4 * x^4 = x^8 (mult) - exp=3 - exp odd: result *= base = x^7 * x^8 = x^15 (mult) - base *= base = x^8 * x^8 = x^16 (mult) - exp=1 - exp odd: result *= base = x^15 * x^16 = x^31 (mult) - base *= base = x^32 (mult, unused) - exp=0 Multiplications: 5 for result updates, 5 for base squarings, total 10. But the base squarings include computing x^2, x^4, x^8, x^16, x^32, but x^32 is unnecessary. In code, we can stop when exp==0, so last base squaring not needed if not used. So for exp=1, after result *= base, we break, no need to square base again. So for n=31: - exp=31 odd: result = 1*x = x (mult) - base = x*x = x^2 (mult) - exp=15 - exp=15 odd: result = x * x^2 = x^3 (mult) - base = x^2 * x^2 = x^4 (mult) - exp=7 - exp=7 odd: result = x^3 * x^4 = x^7 (mult) - base = x^4 * x^4 = x^8 (mult) - exp=3 - exp=3 odd: result = x^7 * x^8 = x^15 (mult) - base = x^8 * x^8 = x^16 (mult) - exp=1 - exp=1 odd: result = x^15 * x^16 = x^31 (mult) - Now, after this, exp = exp//2 = 0, so no base squaring needed. Total mults: 5 result mults and 4 base squarings (for x^2, x^4, x^8, x^16), so 9 multiplications. Still more than the addition chain with 7. But the addition chain method I described had 7 for the optimal path. For power computation, fast exp is O(log n) and practical. Now for the user, I should explain fast exponentiation. User says "设计计算格式" — "design calculation format," probably means algorithm or method. Also, "减少计算系数" — "reduce computation coefficients," likely "reduce computational complexity" or "reduce number of operations." So, I'll describe the fast exponentiation method. Moreover, in the references, [1] mentioned not dividing by n for efficiency, but for power, it's different. Answer in Chinese as per instruction. Structure: 1. Explain the naive way: for x^31, 30 multiplications. 2. Introduce fast exponentiation with binary method. 3. Give an example for x^31. 4. Show code or pseudo-code. 5. Mention that this reduces complexity from O(n) to O(log n). Also, generate related questions as per system instruction. System instruction: "生成相关问题" — generate related questions. And "回答中引用的段落末尾自然地添加引用标识" — naturally add citation marks if citing. But in this case, no citation needed, as it's standard knowledge. If I cite from references, but references are not directly related, so probably not. User said "上述引用仅供参考," so I can ignore. But in reference [3], it talks about L-M algorithm, which is for optimization, not for power computation. So, I'll proceed. Finally, answer format: in Chinese, clear structure. </think> ### 以 \( x^{31} \) 为例设计减少计算系数的计算格式方法 在计算高次幂如 \( x^{31} \) 时,直接使用朴素方法(即连续乘法 \( x \times x \times \cdots \times x \)) 需要 30 次乘法,计算复杂度为 \( O(n) \),效率低下。为了减少计算系数(这里指减少乘法操作次数,从而降低计算复杂度),我们可以采用 **快速幂算法**(fast exponentiation)。这种算法基于二进制分解和分治策略,将复杂度降至 \( O(\log n) \),特别适合处理大指数问题。 #### 快速幂算法原理 快速幂算法的核心思想是将指数 \( n \) 分解为二进制形式,通过迭代平方和条件乘法减少乘法次数。具体步骤(以计算 \( x^n \) 为例): 1. 初始化结果为 \( \text{result} = 1 \),基数为 \( \text{base} = x \)。 2. 当指数 \( n > 0 \) 时: - 如果 \( n \) 是奇数,则结果乘以当前基数:\( \text{result} = \text{result} \times \text{base} \)。 - 基数平方:\( \text{base} = \text{base} \times \text{base} \)。 - 指数右移一位(即整除 2):\( n = \lfloor n / 2 \rfloor \)。 3. 当 \( n = 0 \) 时,返回结果。 这种方法利用二进制位的特性,例如 \( n = 31 \) 的二进制为 \( 11111_2 \),通过分解为 \( 31 = 16 + 8 + 4 + 2 + 1 \),只需要少量乘法即可完成计算。 #### 以 \( x^{31} \) 为例的计算过程 - **步骤分解**: 1. 初始化: \( \text{result} = 1 \), \( \text{base} = x \), \( n = 31 \)。 2. \( n = 31 \)(奇数): \( \text{result} = 1 \times x = x \)(1 次乘法), 然后 \( \text{base} = x \times x = x^2 \)(1 次乘法), \( n = 15 \)。 3. \( n = 15 \)(奇数): \( \text{result} = x \times x^2 = x^3 \)(1 次乘法), 然后 \( \text{base} = x^2 \times x^2 = x^4 \)(1 次乘法), \( n = 7 \)。 4. \( n = 7 \)(奇数): \( \text{result} = x^3 \times x^4 = x^7 \)(1 次乘法), 然后 \( \text{base} = x^4 \times x^4 = x^8 \)(1 次乘法), \( n = 3 \)。 5. \( n = 3 \)(奇数): \( \text{result} = x^7 \times x^8 = x^{15} \)(1 次乘法), 然后 \( \text{base} = x^8 \times x^8 = x^{16} \)(1 次乘法), \( n = 1 \)。 6. \( n = 1 \)(奇数): \( \text{result} = x^{15} \times x^{16} = x^{31} \)(1 次乘法), 然后 \( n = 0 \),结束。 - **乘法次数统计**: - 基数平方操作:每次 \( \text{base} \times \text{base} \) 是 1 次乘法,共发生 4 次(计算 \( x^2, x^4, x^8, x^{16} \))。 - 结果乘法操作:每次 \( \text{result} \times \text{base} \) 是 1 次乘法,共发生 5 次(对应二进制位的 1)。 - 总乘法次数:9 次(远少于朴素方法的 30 次)。 - **复杂度分析**:算法复杂度为 \( O(\log n) \)。对于 \( n = 31 \),朴素方法需要 \( O(31) \),而快速幂仅需 \( O(\log_2 31) \approx O(5) \) 次关键操作。 #### 优化扩展:加法链(Addition Chain) 对于特定指数(如 31),我们可以设计更优的加法链来进一步减少乘法次数。加法链是一种序列,每个元素是前面两个元素之和,目标是以最少步骤达到指数。例如,针对 \( x^{31} \) 的优化链: 1. 计算 \( x^2 = x \times x \)(1 次乘法)。 2. 计算 \( x^4 = x^2 \times x^2 \)(1 次乘法)。 3. 计算 \( x^5 = x^4 \times x \)(1 次乘法)。 4. 计算 \( x^{10} = x^5 \times x^5 \)(1 次乘法)。 5. 计算 \( x^{20} = x^{10} \times x^{10} \)(1 次乘法)。 6. 计算 \( x^{30} = x^{20} \times x^{10} \)(1 次乘法)。 7. 计算 \( x^{31} = x^{30} \times x \)(1 次乘法)。 总乘法次数:7 次。这比快速幂的 9 次更优,但需要预先设计链,通用性不如快速幂。 #### 伪代码实现(快速幂算法) 以下是快速幂算法的 Python 伪代码,适用于任意指数: ```python def fast_power(base, exponent): result = 1 while exponent > 0: if exponent % 2 == 1: # 如果指数为奇数 result *= base # 累积乘法 base *= base # 基数平方 exponent //= 2 # 指数右移 return result # 示例:计算 x^31 x = 2 # 假设 x = 2 print(fast_power(x, 31)) # 输出:2147483648 ``` #### 优势应用 - **减少计算系数**:快速幂将乘法次数从线性降至对数级,节省计算资源。 - **适用场景**:密码学(如 RSA 算法中的大数模幂)、数值计算(多项式求值)、计算机图形学(矩阵幂)等[^1]。 - **注意事项**:虽然加法链在某些指数下更优,但快速幂易于实现且通用性强。在实际编程中,可使用内置函数(如 Python 的 `pow(x, n)`)自动优化。 ### 相关问题 1. 快速幂算法的时间复杂度如何分析?在哪些情况下会退化? 2. 除了二进制分解,还有哪些方法可以优化高次幂的计算(如矩阵幂或多项式幂)? 3. 快速幂在大整数模幂计算中的应用是什么?如何避免溢出问题?
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