Arrays & Hashing

Arrays and Hash Tables form the foundation of algorithmic problem-solving. A Hash Map allows $O(1)$ dynamic lookups by trading space complexity for speed.

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Common Patterns and Techniques

1. Hash Map for Frequency Counting / Lookups: Store element frequencies or indices to resolve lookups in constant time.
2. Prefix Sum: Precomputing cumulative sums enables $O(1)$ subarray sum queries.
3. Kadane’s Algorithm: Maximum subarray sum in $O(N)$ time.
4. Two Pass / Single Pass Tradeoffs: Trading extra space or extra passes for simplicity and speed.

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Solved Problems

• 0001 - Two Sum
• 0152 - Maximum Product Subarray
• 1186 - Maximum Subarray Sum with One Deletion