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Advanced Algorithms and Problem Solving Questions

Comprehensive assessment of advanced algorithmic reasoning, design, and optimization for hard and composite problems. Covers advanced dynamic programming techniques including state compression and bitmask dynamic programming, combinatorial generation and backtracking, recursion and divide and conquer strategies, greedy algorithms with correctness proofs, and advanced graph algorithms such as breadth first search, depth first search, shortest path algorithms including Dijkstra and Bellman Ford, minimum spanning tree, network flow, strongly connected components, and topological sort. Also includes advanced tree and string algorithms such as suffix arrays and advanced hashing, bit manipulation and low level optimizations, algorithmic reductions and heuristics, and complexity analysis including amortized reasoning. Candidates should recognize applicable patterns, combine multiple data structures in a single solution, transform brute force approaches into optimized solutions, prove correctness and derive time and space complexity bounds, handle edge cases and invariants, and articulate trade offs and incremental optimization strategies. At senior levels expect mentoring on algorithmic choices, designing for tight constraints, and explaining engineering implications of algorithm selection.

MediumTechnical
0 practiced
Given N ≤ 20 numbers, implement a subset-sum algorithm using bitmask DP in Python to determine if there's a subset summing to a target. Use bitmask iteration and explain time/space bounds. Then describe the meet-in-the-middle optimization to handle N up to ~40.
HardSystem Design
0 practiced
Design a dynamic 2D k-NN data structure supporting insert(point), delete(point), and kNN queries with logarithmic average time in low dimensions. Evaluate kd-tree, cover tree, and vantage-point tree options, and describe balancing strategies, lazy deletes, and production considerations for ML inference latency.
EasyTechnical
0 practiced
Explain what a suffix array is, how it supports substring queries, and the difference between a suffix array and a suffix tree. Describe common construction algorithms such as the O(n log n) doubling algorithm and the tradeoffs in memory and query speed for large corpora used in feature engineering.
MediumTechnical
0 practiced
Implement Dijkstra's algorithm in Python for a weighted graph with non-negative edge weights using adjacency lists and heapq. Discuss optimizations for very large sparse graphs (e.g., decrease-key alternatives, early exits) and when to prefer other data structures like pairing heaps or specialized libraries.
EasyTechnical
0 practiced
Implement a Union-Find (Disjoint Set Union) data structure in Python with path compression and union by rank. Provide methods find(x) and union(x, y), handle initialization for an arbitrary set of nodes, and state the amortized time complexity per operation.

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