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Strengthen your algorithm skills with practice questions covering sorting, searching, dynamic programming, graph algorithms, and common problem-solving patterns.
Binary search, BFS/DFS, dynamic programming, two pointers, sliding window, and sorting algorithms are the most common. Companies also test recursion, backtracking, and greedy algorithms frequently.
Start with simple DP problems like Fibonacci and climbing stairs. Learn to identify overlapping subproblems and optimal substructure. Practice both top-down (memoization) and bottom-up (tabulation) approaches, and build up to 2D DP problems.
Two pointers use two indices that move through an array to solve problems in O(n) time instead of O(n^2). Common applications include finding pairs that sum to a target, removing duplicates, and merging sorted arrays. It works best on sorted data or linked lists.
Use BFS for shortest path in unweighted graphs, level-order traversal, and finding nearest neighbors. Use DFS for detecting cycles, topological sorting, path finding in mazes, and problems requiring exhaustive search. BFS uses more memory (queue) while DFS uses the call stack.
Look for clues: 'find minimum/maximum' often means binary search or DP, 'all combinations/permutations' means backtracking, 'shortest path' means BFS or Dijkstra, 'contiguous subarray' means sliding window, and 'optimal substructure' means dynamic programming.
Essential. You are expected to analyze time and space complexity for every solution. Interviewers will ask you to optimize from brute force to efficient solutions. Understand O(1), O(log n), O(n), O(n log n), O(n^2), and O(2^n) and when each applies.