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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 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.
HardTechnical
0 practiced
You must provide fast approximate all-pairs shortest-path answers on a sparse graph with 1e6 nodes and 1e7 edges (non-negative weights). Full APSP is infeasible. Propose practical algorithms or heuristics (landmarks, hub labeling, contraction hierarchies, hierarchical routing) that trade preprocessing and storage for query speed. Analyze error bounds, storage vs query-time trade-offs, and how you'd validate accuracy in AI path-planning use-cases.
HardTechnical
0 practiced
Beam search on sequence models often yields low diversity. Design an algorithmic modification to beam search that increases diversity (e.g., diverse beam search, stochastic beam) while keeping scores near-optimal. Explain how you would implement it, analyze added time/memory complexity, and discuss failure modes (e.g., repetition, quality drop).
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
Design a meet-in-the-middle algorithm to solve subset-sum when n <= 40. Describe the steps, time/space complexity, and how you'd use the approach in feature subset selection or constrained combinatorial search for an AI application. Include ideas for pruning and parallelization.

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