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Heaps give you the min/max in O(1) and add/remove in O(log n). Learn heapify and priority queues.
Perfect introduction to heap operations (insert, getMin, removeMin) with a greedy approach. Construct valid operation sequences.
No pointers needed
Excellent k-th order statistics problem. Forces you to use Fenwick Trees or creative frequency array approaches.
Classic greedy with priority queue. Maximize pleasure by sorting by one dimension while maintaining heap for the other.
Combines binary search with greedy heap thinking. Maximize median values by focusing operations on elements from median upward.
Perfect interval scheduling simulation using priority queues. Teaches sweep line algorithm with min-heap for active intervals.
Prim/Dijkstra-style graph traversal using priority queues. Find lexicographically smallest paths.
Interval intersection problem requiring greedy segment selection. Use sorting and binary search with multisets.
Greedy digit construction problem. Maximize number's magnitude while respecting resource constraints.
Tree DP with greedy selection using depth and subtree sizes. Select k nodes that maximize total happiness.
Sorting-based greedy optimization. Great introduction to problems where sorting reveals the greedy strategy.
Bitmask DP combined with sorting and greedy heap optimization. Sort by one dimension for efficient DP on subsets.
Cache replacement with variable costs - weighted LRU algorithm. Requires minimum cost flow or clever greedy heap strategies.
Fundamental problem teaching min-heap to maintain k largest elements in O(n log k) time. Essential Top-K pattern.
Classic two-heap problem (max-heap + min-heap). Teaches advanced heap coordination and balancing for streaming data.
Fundamental FAANG interview problem demonstrating heap-based merging. Widely applicable in system design scenarios.
Combines hash maps with heaps for frequency-based problems. The powerful 'heap + hashmap' pattern.
Advanced scheduling combining heaps with greedy algorithms. Simulate CPU task scheduling with cooldown constraints.
Greedy frequency-based rearrangement using max-heap. Solve character spacing problems to avoid adjacency constraints.
Complex multi-list merging with min-heap and sliding window. Maintain range invariants while processing sorted sequences.
Advanced greedy + heap for capital maximization. Combine sorting with heap-based selection for optimal profit.
