Solving problems on the ZOJ online evaluation system is not just as simple as writing a few lines of code. It requires you to build a rigorous project, build a maintainable project, build an efficient project, and coordinate these three.
The significance of project structure
The basis for success is a clear project structure. In questions such as ZOJ 1048, you usually have to create an independent source code file, such as main.cpp as the program entry. At the same time, key functions or data structures should be declared in the .h header file and defined in the corresponding .cpp file.
The benefits of this are obvious. It can convert codes with different functions into modules, thereby making the logic clearer. When the program needs to be debugged or expanded, you can quickly find the relevant files instead of searching among hundreds of lines of mixed code. This saves a lot of trouble for subsequent collaboration and code maintenance.
main.cpp
.
helper.h
.
g++.exe
-Wall -fexceptions -g
g++.exe
-g
main.exe
Deeply understand the question requirements
Before you start coding, you must do your best to thoroughly digest the topic. First of all, you have to read the input and output format carefully, for example, to see whether the input is a number, a string, or data in a specific format. The data range is not general, it directly determines which algorithm you can use. For example, when n is 100, and when n is 100,000, the corresponding solution is really very different.
graph LR
A[项目根目录] --> B(main.cpp)
A --> C(helper.h)
A --> D(helper.cpp)
Hard constraints are time and space constraints. Time constraints require that your algorithm must complete all calculations within the specified time, which forces you to choose a solution with better time complexity. There is a space limit, which requires you to use memory carefully to prevent creating too large arrays or engaging in uncontrolled dynamic allocation.
Choice of algorithms and data structures
#include
#include
#include
#include "helper.h"
using namespace std;
int main() {
// 输出题目要求或初始信息
cout << "Welcome to the ZOJ 1048 problem solution." << endl;
// 初始化问题特定的变量
vector words;
string word;
cout <> word) {
words.push_back(word);
}
// 调用函数，统计并输出结果
cout << "The number of distinct words entered is: " << countDistinctWords(words) << endl;
return 0;
}
The key is to select the appropriate data structure based on the characteristics of the problem. If search and insertion operations occur frequently in the problem, using unordered_map based on a hash table may be much faster than using a linear search vector. If the data must be maintained in an ordered state, set or map implemented by red-black tree is a more suitable choice.
The implementation of the algorithm still requires careful consideration. For example, in ZOJ 1048, loops or recursion may be associated. You have to evaluate the different boundary conditions achieved and analyze the worst-case performance. Sometimes, a very small optimization, like replacing recursion with iteration, can avoid the risk of stack overflow.
main.o : main.cpp main.h helper.h
helper.o : helper.cpp helper.h
Input, output and error handling
High-performance input and output processing can significantly improve program performance. In C++, using ios::sync_with_stdio(false) and eliminating the synchronization relationship with C input and output, and then using cin.tie(nullptr) can greatly speed up the speed of cin and cout . For large amounts of data, occasionally using C's scanf and printf is a more reliable choice.
A powerful and robust program is inseparable from complete error handling. For user input, validity verification must be performed, such as checking whether the number is within a given range and whether the string format is correct. Once an error is discovered, a clear and clear prompt should be given immediately, such as "Input error: the number n must be a positive integer", rather than letting the program crash or output meaningless and valuable results.
{
"windows": {
"compiler": "visible, 0.675, 0.138, 0.325, 0.513",
"search": "visible, 0.675, 0.638, 0.325, 0.863",
"messages": "visible, 0.675, 0.75, 0.325, 0.938",
"files": "visible, 0.0, 0.0, 0.675, 1.0"
},
"panes": {
"down": {
"bottom_notebook": "visible",
"bottom_dockable": "collapsed"
},
"right": {
"right_notebook": "visible",
"right_dockable": "collapsed"
}
},
"toolbars": {
"main": "visible",
"build": "visible",
"debug": "visible"
}
}
Code optimization and maintainability
The aspects involved through optimization are not limited to performance, but are also related to code quality. Reduce unnecessary header file inclusions and use forward declarations to achieve the effect of shortening compilation time. Separate the interface and implementation, so that when you modify the internal logic, it will not affect other modules, thereby improving the maintainability of the code.
#include
using namespace std;
// 使用一维数组模拟栈的实现
#define MAXSIZE 100 // 定义栈的最大容量
int stack[MAXSIZE]; // 栈的数组表示
int top = -1; // 栈顶指针初始化
bool Push(int x) { // 入栈操作
if (top >= MAXSIZE - 1) return false; // 栈满
stack[++top] = x; // 元素x入栈
return true;
}
bool Pop(int &x) { // 出栈操作
if (top < 0) return false; // 栈空
x = stack[top--]; // 返回栈顶元素并出栈
return true;
}
int main() {
int element;
Push(10); // 元素10入栈
Push(20); // 元素20入栈
Pop(element); // 出栈并返回栈顶元素
cout << element << endl; // 输出：20
return 0;
}
Code comments are road signs left for future self or others. For complex algorithm logic and key parameters, concise comments should be used to explain their intentions. However, to avoid the situation of over-annotation, good code itself should actually be self-explanatory, where comments should focus on explaining "why this is done" rather than repeating "what is done" over and over again.
#include
using namespace std;
// 快速排序中的分区函数
int Partition(int arr[], int low, int high) {
int pivot = arr[high]; // 选择最后一个元素为基准
int i = low - 1;
for (int j = low; j <= high - 1; j++) {
if (arr[j] < pivot) {
i++;
swap(arr[i], arr[j]);
}
}
swap(arr[i + 1], arr[high]);
return (i + 1);
}
// 快速排序主函数
void QuickSort(int arr[], int low, int high) {
if (low < high) {
int pi = Partition(arr, low, high);
QuickSort(arr, low, pi - 1);
QuickSort(arr, pi + 1, high);
}
}
int main() {
int arr[] = {10, 7, 8, 9, 1, 5};
int n = sizeof(arr) / sizeof(arr[0]);
QuickSort(arr, 0, n - 1);
for (int i = 0; i < n; i++)
cout << arr[i] << " ";
cout << endl;
return 0;
}
Testing and Performance Analysis
It is extremely important to design test cases that cover all aspects. Here are the example cases given in the question, there are boundary conditions such as the minimum or maximum input cases, and the unique data cases you create with your own construction. To ensure that the program can run correctly in a variety of different situations, it does not just happen to pass the example case by accident.
#include
#include
#include
using namespace std;
// 自定义异常类
class FileReadException : public exception {
public:
const char* what() const throw() {
return "File read error!";
}
};
void ReadFromFile(const string& filename) {
ifstream file;
file.open(filename);
if (!file.is_open()) {
throw FileReadException();
}
int data;
while (file >> data) {
cout << data << " ";
}
file.close();
}
int main() {
try {
ReadFromFile("data.txt");
} catch (const FileReadException& e) {
cerr << e.what() << endl;
}
return 0;
}
If you want to perform performance analysis, you can calculate the time complexity theoretically, or measure the elapsed time during actual execution. If a function is detected to be a performance bottleneck, it should be optimized in a targeted manner, such as changing the data structure, reducing loop nesting, or precomputing the results. Remember, be sure to ensure correctness before optimizing.
When successfully submitting a project on ZOJ, do you think the biggest difficulty is the difficulty of formulating the algorithm, or is it the engineering nuances such as project organization and code specifications? Welcome to share your views and experiences in the comment area?
