Essential Algorithm Tricks Every Developer Should Know for Interviews

Algorithm Patterns That Solve 80% of Problems

Most coding interview problems fall into recognizable patterns. Master these patterns, and you'll be able to tackle the majority of questions with confidence.

Pattern 1: Two Pointers

When to Use: Arrays, strings, or linked lists where you need to find pairs or compare elements.

The Trick: Use two pointers moving toward each other or in the same direction.

Classic Problems:

• Two Sum (sorted array)
• Container With Most Water
• Remove Duplicates from Sorted Array
• Valid Palindrome

Template:

• Start pointers at opposite ends (or same end for sliding window)
• Move based on a condition
• Process until pointers meet or cross

Pattern 2: Sliding Window

When to Use: Finding subarrays or substrings that meet certain criteria.

The Trick: Maintain a window and slide it efficiently without recalculating from scratch.

Classic Problems:

• Maximum Sum Subarray of Size K
• Longest Substring Without Repeating Characters
• Minimum Window Substring

Key Insight: Expand the window to include new elements, shrink to remove elements, and track your answer as you go.

Pattern 3: Binary Search

When to Use: Sorted arrays, or when searching for a value in a range.

The Trick: Binary search isn't just for "find element X" - it's for any problem where you can eliminate half the search space.

Classic Problems:

• Search in Rotated Sorted Array
• Find Peak Element
• Kth Smallest Element in Sorted Matrix

Pro Tip: When stuck, ask "Can I frame this as searching for the first/last element satisfying a condition?"

Pattern 4: BFS/DFS for Graphs and Trees

When to Use: Traversing trees, graphs, or exploring all possibilities.

The Trick:

• BFS = Level by level, shortest path, use a queue
• DFS = Go deep first, use recursion or stack

Classic Problems:

• Number of Islands (DFS/BFS on grid)
• Binary Tree Level Order Traversal (BFS)
• Course Schedule (Topological Sort with BFS/DFS)

Pattern 5: Dynamic Programming

When to Use: Optimization problems with overlapping subproblems.

The Trick: Identify the recurrence relation, then choose top-down (memoization) or bottom-up (tabulation).

Steps to Solve DP Problems:

1. Define state - what variables describe subproblems?
2. Write recurrence - how do smaller problems combine?
3. Identify base cases
4. Determine traversal order
5. Optimize space if possible

Classic Problems:

• Fibonacci, Climbing Stairs (1D DP)
• Longest Common Subsequence (2D DP)
• Coin Change (unbounded knapsack)
• House Robber (decision DP)

Pattern 6: Hash Maps for O(1) Lookups

When to Use: Need fast lookups, counting, or tracking seen elements.

The Trick: Trade space for time. Store what you've seen to avoid repeated work.

Classic Problems:

• Two Sum (unsorted)
• Group Anagrams
• LRU Cache

Quick Tips for Interview Day

1. Clarify Before Coding

• Input size and constraints
• Edge cases (empty input, single element, duplicates)
• Expected time/space complexity

2. Start with Brute Force

Mention the naive approach first, then optimize. This shows you understand the problem.

3. Test Your Code

Walk through a simple example before saying you're done. Catch off-by-one errors.

4. Know Your Complexities

Be ready to analyze time and space complexity for your solution.

5. Practice Explaining

Technical communication matters as much as getting the right answer.

Study Plan

Week 1-2: Arrays and Strings (Two Pointers, Sliding Window)

Week 3-4: Trees and Graphs (BFS, DFS, Binary Search Trees)

Week 5-6: Dynamic Programming (start with 1D, progress to 2D)

Week 7-8: Mixed practice and mock interviews

Consistency beats intensity. 1-2 problems per day for 8 weeks will transform your interview performance.