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Day 44: Competitive Programming Journal

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🗣️ Stimme:

Date: November 5, 2024.

Hello Everyone,



Today marks Day 44 of my competitive programming journey, and I’m here to share my progress.



What I Did Today:

I worked on two problems: Implement binary search to find an element in a sorted array and Count the frequency of each character in a string.



1. Implement binary search to find an element in a sorted array:

Problem:


Given a sorted array and a target element, implement binary search to find its position. If the element is not present, return -1.



Explanation:




  • Binary search works by dividing the array into halves repeatedly and comparing the middle element with the target.

  • If the middle element equals the target, return its position.

  • If the target is smaller, search the left half; otherwise, search the right half.



Here’s the implementation:




CODE
int binarySearch(const vector<int>& arr, int target) {
int left = 0, right = arr.size() - 1;

while (left <= right) {
int mid = left + (right - left) / 2;
if (arr[mid] == target) {
return mid;
} else if (arr[mid] < target) {
left = mid + 1;
} else {
right = mid - 1;
}
}

return -1;
}






2. Count the frequency of each character in a string:

Problem:


Given a string, count the frequency of each character and display the result.



Explanation:




  • Traverse the string and use a frequency array or a map to count the occurrences of each character.

  • Print the characters along with their respective frequencies.



Here’s the implementation:




CODE
void countCharacterFrequency(const string& str) {
unordered_map<char, int> freq;

for (char ch : str) {
freq[ch]++;
}

cout << "Character frequencies:" << endl;
for (auto& pair : freq) {
cout << pair.first << ": " << pair.second << endl;
}
}






Reflection:

Today’s problems were straightforward yet insightful. Implementing binary search refreshed my understanding of the divide-and-conquer approach, while counting character frequencies was a good practice in working with hash maps. Both problems highlighted the importance of optimizing for time complexity.



Stay tuned for more updates, and as always, happy coding!

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