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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.

MediumSystem Design
0 practiced
You must compute strongly connected components (SCCs) on a directed graph that cannot fit entirely in memory (semi-external). Describe algorithms and strategies (e.g., graph partitioning, streaming, label propagation, external-memory variants of Kosaraju/Tarjan) to compute SCCs efficiently, and explain trade-offs between passes over data, I/O, and correctness.
EasyTechnical
0 practiced
Describe what a suffix array is and at a high level how it's constructed. Give one practical NLP application where suffix arrays are preferable to suffix trees because of memory constraints, and explain the role of the LCP array in queries like 'longest repeated substring'.
MediumTechnical
0 practiced
Given n <= 20 items each with weight w_i and value v_i and a capacity W, implement a bitmask DP in Python to find the maximum total value for items with total weight <= W. Explain time and memory complexity and discuss practical optimizations for CPU/GPU execution when n is close to 20.
MediumTechnical
0 practiced
Given a weighted undirected graph constructed from embedding similarities, design an MST-based clustering approach. Explain whether you would use Kruskal or Prim, detail time/space trade-offs for very large sparse graphs, and how to choose the cut threshold to form clusters for deduplication or entity grouping.
EasyTechnical
0 practiced
Explain bitmask dynamic programming (state-compression DP). Give a clear example of how bitmask DP can be used for a small neural architecture search (select subset of layers/features up to n=20) and outline how complexity improves compared to naive enumeration. Include reasoning about memory limits and parallelization opportunities.

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