DSA: Pattern Recognition
Level up your problem-solving skills by identifying common patterns in data structures and algorithms for coding challenges.
Explore PatternsMastering common coding patterns is a game changer—it helps you recognize underlying structures in problems, making them quicker to understand and solve. Below is a breakdown of the essential coding patterns you should focus on.
Essential Coding Patterns
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1. Two-Pointer Technique
When to use:
- Arrays or strings where you need to compare elements from both ends or move through them with two indices.
- Problems like sorting, searching pairs, or removing duplicates.
Common scenarios:
- Finding a pair with a target sum in a sorted array
- Checking if a string is a palindrome
- Merging two sorted arrays
Example:
Time Complexity: O(n)
Arrays
Strings
Optimization
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2. Sliding Window
When to use:
- Problems involving contiguous sequences (subarrays, substrings) with maximum, minimum, or target sums/lengths.
Common scenarios:
- Maximum sum of a subarray of size k
- Longest substring without repeating characters
- Smallest window containing all characters of another string
Example:
Time Complexity: O(n)
Arrays
Strings
Subarrays
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3. Fast and Slow Pointers
When to use:
- Detecting cycles in linked lists or arrays
- Finding middle elements or cycle lengths
Common scenarios:
- Detecting a cycle in a linked list (Floyd's cycle detection)
- Finding the start of the cycle in a sequence
Example:
Time Complexity: O(n)
Linked Lists
Cycle Detection
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4. Dynamic Programming
When to use:
- Problems with overlapping subproblems and optimal substructure (solvable from solutions to smaller subproblems).
Common scenarios:
- Fibonacci numbers (optimized)
- Longest common subsequence
- Knapsack problems
- Coin change (minimum number of coins)
Example:
Time Complexity: O(n)
Optimization
Memoization
Tabulation
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5. Recursion and Backtracking
When to use:
- Problems requiring exploration of all possible solutions (e.g., permutations, combinations).
Common scenarios:
- N-Queens problem
- Generating subsets or permutations
- Maze-solving problems
- Sudoku solver
Example:
Time Complexity: O(2^n)
Combinatorial
Search
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6. Binary Search
When to use:
- Sorted arrays or search spaces where a decision can be made based on comparisons.
Common scenarios:
- Finding an element in a sorted array
- Searching for boundaries (first/last occurrences)
- Finding square roots or peaks in arrays
Example:
Time Complexity: O(log n)
Search
Divide & Conquer
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7. Prefix Sums
When to use:
- Range-based sum or frequency queries.
Common scenarios:
- Sum of elements between two indices quickly
- Range update queries in arrays
Example:
Time Complexity: Query Time: O(1)
Arrays
Range Queries
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8. Tree and Graph Traversals
When to use:
- Navigating hierarchical or networked data structures.
Common scenarios:
- Finding shortest paths (BFS)
- Counting connected components
- Tree-based problems (lowest common ancestor, diameter of a tree)
Example:
Time Complexity: O(V + E)
Trees
Graphs
Pathfinding
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9. Greedy Algorithms
When to use:
- Problems where making a locally optimal choice leads to a global solution.
Common scenarios:
- Interval scheduling (activity selection)
- Coin change (with standard coin sets)
- Huffman coding
Example:
Time Complexity: O(n log n)
Optimization
Scheduling
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10. Bit Manipulation
When to use:
- Working with integers at the binary level for optimization.
Common scenarios:
- Checking if a number is a power of two
- Finding a single non-duplicate number (XOR tricks)
- Counting set bits
Example:
Time Complexity: O(1) for isPowerOfTwo
Binary
Optimization
How to Master These Patterns
1
Pick one pattern per week to study and practice.
2
Solve 3-5 problems per pattern on platforms like LeetCode, HackerRank, or CodeSignal.
3
Write down the approach and note how the pattern applies.
4
Compare your solution to others for optimization techniques.