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- C++ basics
- Arrays
- Matrix
- Strings
- Stacks
- Queues
- Linked List
- Generic Tree / Tries
- Binary Tree
- Binary Search Tree
- Hashmap and Heap
- Recursion
- Backtracking
- DP
- Greedy
- Graphs
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| Problem | Pattern/Approach | Topic | Time Complexity |
|---|---|---|---|
| print-all-subarrays.md | Recursion | O(n^2) | |
Tips
👉🏻 If we are dealing with top/maximum/minimum/closest ‘K' elements among 'N' elements, we will be using a Heap.
👉🏻 If the given input is a sorted array or a list, we will either be using Binary Search or the Two Pointers.
👉🏻 If we need to try all combinations (or permutations) of the input, we can either use Backtracking or Breadth First Search.
👉🏻 Most of the questions related to Trees or Graphs can be solved either through Breadth First Search or Depth First Search.
👉🏻 Every recursive solution can be converted to an iterative solution using a Stack.
👉🏻 For a problem involving arrays, if there exists a solution in O(n^2)time and O(1) space, there must exist two other solutions: 1) Using a HashMap or a Set for O(n) time and O(n) space, 2) Using sorting for O(n log n) time and O(1) space.
👉🏻 If a problem is asking for optimization (e.g., maximization or minimization), we will be using Dynamic Programming.
👉🏻 If we need to find some common substring among a set of strings, we will be using a HashMap or a Trie.
👉🏻 If we need to search/manipulate a bunch of strings, Trie will be the best data structure.
👉🏻 If the problem is related to a LinkedList and we can't use extra space, then use the Fast & Slow Pointer approach.
Tips - 2
Array: Versatile and dynamic list
Linked List: Efficient insertions and deletions
Hash Table: Quick data retrieval
Stack: Last in, first out (LIFO)
Queue: First in, first out (FIFO)
Heap: Priority data handling
Binary Search Tree: Optimal for data search operations
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