C++ Program Complexity Optimization: Industry Best Practices

Best practice for C++ program complexity optimization: Use concise algorithms and choose algorithms with lower complexity. Use data structures to store data. Reasonable selection of data structures can reduce the number of operations. Reduce copies and avoid unnecessary object copies. Optimize the loop and reduce the number of iterations. Use compiler optimization options such as precompilation and inline expansion. Write concise code that is easy to understand and maintain.

C++ Program Complexity Optimization: Industry Best Practices

Introduction
Complexity Optimization It is the key to improving the performance of C++ programs. This article will introduce some proven best practices to help you optimize the complexity of your program and achieve faster runtimes.

Best Practices

• Use concise algorithms: Choose less complex algorithms, even if they are slightly less efficient . For example, for small data sets, use linear search instead of binary search.
• Use data structures: Store data in appropriate data structures such as arrays, hash tables, trees, etc. Proper selection of data structures can significantly reduce the number of operations required to access and insert data.
• Reduce copies: Avoid unnecessary object copies. Pass objects by reference or pointer rather than creating a new copy.
• Optimize the loop: Optimize the scope and conditions of the loop and reduce the number of iterations as much as possible.
• Use compiler optimization: Take advantage of the compiler's built-in optimization options, such as precompilation and inline expansion, to improve program performance.
• Write concise code: Write concise, readable code that is easy to understand and maintain. Overly complex code results in longer execution times and higher maintenance costs.

Practical Case

Suppose we have an array containing integers and we need to find the largest element in the array. The following are two algorithms implemented in C++:

// 复杂度为 O(n)
int max_element_linear(int arr[], int size) {
  int maximum = arr[0];
  for (int i = 1; i < size; i++) {
    if (arr[i] > maximum) {
      maximum = arr[i];
    }
  }
  return maximum;
}

// 复杂度为 O(log(n))
int max_element_binary_search(int arr[], int size) {
  int low = 0;
  int high = size - 1;
  int maximum;
  while (low <= high) {
    int mid = (low + high) / 2;
    if (arr[mid] > maximum) {
      maximum = arr[mid];
    }
    if (arr[mid] >= arr[high]) {
      low = mid + 1;
    } else {
      high = mid - 1;
    }
  }
  return maximum;
}

Linear search is more efficient for smaller data sets. However, as the data set grows, binary search becomes less complex and performs better.

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