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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.

HardTechnical
19 practiced
Given a text T of length n, show how to build a suffix array and LCP array and use them to answer pattern existence and occurrence count queries in O(m log n) time (m = pattern length). Implement searching by binary searching over the suffix array and discuss how LCP can speed repeated queries.
HardSystem Design
17 practiced
Design a parallel BFS/SSSP algorithm suitable for GPU execution for large sparse graphs used in graph embedding preprocessing. Discuss graph representation (CSR/COO), frontier-based processing, load balancing for high-degree nodes, atomic updates, and strategies to minimize warp divergence and memory bandwidth issues.
EasyTechnical
32 practiced
Explain the differences between Dijkstra's algorithm and Bellman–Ford in terms of correctness conditions, time complexity, and when to use each in practice. Give concrete graph examples (with/without negative weights) where one applies and the other does not.
EasyTechnical
23 practiced
Explain time and space complexity of the naive k-nearest-neighbors (kNN) search (brute-force linear scan) for high-dimensional vectors. Describe kd-tree and its limitations in high dimensions; explain when you would choose approximate methods such as LSH or HNSW instead of exact kd-tree.
EasyTechnical
24 practiced
Compare recursive DFS to iterative DFS (explicit stack). Provide a Python iterative DFS implementation that can handle a graph with up to 10^5 nodes without recursion limit issues. When is iterative DFS preferable in production code for ML pipelines?

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