Recognizing and applying common algorithmic patterns like Two Pointers, Sliding Window, and Backtracking.
Expert problem-solvers don't just know data structures; they recognize underlying patterns in problems that suggest a particular approach. This topic focuses on identifying and mastering these patterns. The 'Two Pointers' technique is extremely common for problems involving sorted arrays or linked lists. It uses two pointers that iterate through the data structure to find a pair or a subsequence that satisfies certain conditions, often achieving linear time complexity. The 'Sliding Window' pattern is used for problems that involve finding a subarray or substring that meets a specific criterion. A window of a certain size slides over the data, and we efficiently update our calculations as the window moves, avoiding re-computation. 'Backtracking' is a methodical way to explore all potential solutions to a problem. It builds a solution incrementally and abandons a path ('backtracks') as soon as it determines the path cannot lead to a valid solution. It's the core of solving problems like Sudoku, N-Queens, and generating permutations/combinations. Other patterns include 'Divide and Conquer' (e.g., Merge Sort), 'Greedy', and recognizing when a problem can be modeled as a graph problem (e.g., BFS/DFS, topological sort). Mastering these patterns is key to quickly formulating solutions during a high-pressure interview.