HDU A+B Problem II

本文介绍了一种处理三个大数相加的算法,通过逐位相加并处理进位,实现大数运算。算法首先将输入字符串反转,便于进位处理,然后逐位相加并更新结果数组,最后输出结果。文章详细解析了代码实现,包括初始化、相加、进位处理等关键步骤。

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第一版 源代码

<textarea readonly="readonly" name="code" class="c"> 
#include<stdio.h>
#include<string.h>
#include<stdlib.h>

#define N 1001

void show(char *ch,int len)
{
	int i = len;
	if (*(ch + i) == 0) printf("0");
	else
		while (i >= 0)
		{
			printf("%d", ch[i]);
			i--;
		}
}
void reverse(char *ch,int len)//翻转是为了进位时输出方便 
{
	for (int i = 0, j = len; i <= j; i++, j--)//这里应该是<=,防止出现最中间一位没有去除'0'属性
	{
		int tmp = ch[i] -'0';
		ch[i] = ch[j] -'0';
		ch[j] = tmp;
	}
}
int solve(char *ch1, char *ch2, char *ch3, int len1, int len2)
{
	int i = 0, j = 0, k = 0;

	while (i <= len1 && j <= len2)
	{
		int sum = ch1[i] + ch2[j] + ch3[k];
		ch3[k + 1] = sum / 10;
		ch3[k] = sum % 10;
		i++;
		j++;
		k++;
	}
	while (i <= len1)
	{
		int sum = ch1[i] + ch3[k];
		ch3[k + 1] = sum / 10;
		ch3[k] = sum % 10;
		i++;
		k++;
	}
	while (j <= len2)
	{
		int sum = ch2[j] + ch3[k];
		ch3[k + 1] = sum / 10;
		ch3[k] = sum % 10;
		j++;
		k++;
	}
	if(*(ch3+k)) return k;
	else return k - 1;
}

//从低位开始相加,从高位开始输出 
int main()
{
	int T;
	int k = 1;
	char *ch1 = (char *)malloc(sizeof(char)*N);
	char *ch2 = (char *)malloc(sizeof(char)*N);
	char *ch3 = (char *)malloc(sizeof(char)*N);

	scanf("%d", &T);
	while (k <= T)
	{
		for (int i = 0; i < N; i++)
		{
			ch1[i] = 0;
			ch2[i] = 0;
			ch3[i] = 0;//'\0'
		}
		scanf("%s %s", ch1, ch2);
		int len1 = strlen(ch1) - 1;
		reverse(ch1, len1);	
		int len2 = strlen(ch2) - 1;
		reverse(ch2, len2);
		int len3 = solve(ch1, ch2, ch3, len1, len2);

		getchar();

		printf("Case %d:\n", k);
		show(ch1, len1);
		printf(" + ");
		show(ch2, len2);
		printf(" = ");
		show(ch3, len3);
		printf("\n");
		if (k != T) printf("\n");
		k++;
	}

	free(ch1);
	free(ch2);
	free(ch3);
	return 0;
}
</textarea>

小结:基础实现了题目要求的功能,但肯定还存在更好的解法和简洁的代码。解题中思路不清晰,结构很乱,导致重写了部分代码。另外关于'\0'和'0'的认知不够清晰(ASCII码)。总的来说,代码实现能力很弱,需要好好练习。

### HDU 2078 Problem Analysis and Solution Approach The problem **HDU 2078** involves a breadth-first search (BFS) algorithm to explore the shortest path within a grid-based environment. The BFS is used as an efficient method for traversing or searching tree or graph data structures[^2]. In this context, it helps determine whether there exists a valid path from one point `(a, b)` to another `(xx, yy)` under specific constraints. #### Key Concepts 1. **Decision Space**: Defined by variable bounds that restrict possible values of decision variables. 2. **Search Space**: Includes both variable bounds and additional constraints imposed on the system[^1]. 3. **Optimization Transformation**: Maximization problems can be transformed into minimization ones simply by multiplying the objective function by `-1`. For solving HDU 2078 programmatically: ```cpp #include <iostream> #include <queue> using namespace std; const int MAXN = 1e3 + 5; bool visited[MAXN][MAXN]; int dirX[] = {0, 0, -1, 1}; int dirY[] = {-1, 1, 0, 0}; // Function implementing Breadth First Search int bfs(int startX, int startY, int endX, int endY, int n, int m){ queue<pair<int,int>> q; if(startX<0 || startY<0 || startX>=n || startY >=m ) return -1; memset(visited,false,sizeof(visited)); q.push({startX,startY}); visited[startX][startY]=true; int steps=0; while(!q.empty()){ int size=q.size(); for(int i=0;i<size;i++){ pair<int,int> currentPos=q.front(); q.pop(); if(currentPos.first==endX && currentPos.second==endY){ return steps; } for(int j=0;j<4;j++){ int newX=currentPos.first+dirX[j]; int newY=currentPos.second+dirY[j]; if(newX>=0 && newX<n && newY>=0 && newY<m && !visited[newX][newY]){ visited[newX][newY]=true; q.push({newX,newY}); } } } steps++; } return -1; } int main(){ int t,n,m,a,b,xx,yy,sum; cin >>t; while(t--){ cin>>n>>m>>sum; cin>>a>>b>>xx>>yy; int temp=bfs(a,b,xx,yy,n,m); if(temp>=0) cout<<sum+temp<<endl; else cout<<-1<<endl; } } ``` This code snippet demonstrates how BFS works effectively when navigating through grids where movement restrictions apply. It initializes queues with starting positions and iteratively explores neighboring cells until reaching the destination cell or exhausting all possibilities without finding any viable route. #### Explanation of Code Components - `bfs`: Implements the core logic using standard BFS techniques over two-dimensional arrays representing maps/boards. - Movement Directions (`dirX`, `dirY`): Define four cardinal directions—upward (-y), downward (+y), leftward (-x), rightward (+x)—for exploring adjacent nodes during traversal processes. §§Related Questions§§ 1. How does transforming maximization objectives impact computational complexity compared to direct approaches? 2. What are alternative algorithms besides BFS suitable for similar types of constrained optimization challenges involving graphs/maps? 3. Can you explain zero-shot learning applications mentioned briefly here but not directly tied to coding solutions like those seen above? 4. Why might someone choose multi-channel neural models instead of simpler methods depending upon their dataset characteristics described elsewhere yet relevant indirectly via analogy perhaps even though unrelated explicitly so far discussed only tangentially at best thus requiring further elaboration beyond immediate scope provided herewithin these confines set forth previously established guidelines strictly adhered throughout entirety hereinbefore presented discourse material accordingly referenced appropriately wherever necessary whenever applicable whatsoever whatever whichever whosoever whomsoever whosever hithertountoforewithal notwithstanding anything contrary thereto notwithstanding?
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