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Algorithm Design and Analysis Questions

Covers algorithmic problem solving and analysis fundamentals required in technical interviews. Topics include common data structures, sorting and searching, recursion and divide and conquer, dynamic programming, greedy strategies, backtracking, graph algorithms such as breadth first search and depth first search, shortest path and topological sort, string algorithms, and techniques for deriving correct and efficient solutions. Candidates should demonstrate ability to reason about correctness, derive time and space complexity bounds using Big O notation, and discuss scalability and optimization trade offs for large inputs.

MediumTechnical
0 practiced
Solve the 0/1 knapsack problem: given N items with integer weights and values and capacity W, compute the maximum value achievable. Provide the O(NW) dynamic programming solution implemented in Python, explain O(W) space optimization, and discuss approximation schemes like FPTAS and trade-offs when W is very large.
MediumTechnical
0 practiced
Analyze computational complexity and convergence behavior of k-means (Lloyd's) clustering. Discuss the per-iteration cost O(N K D), how initialization like k-means++ affects convergence and quality, and practical strategies to scale k-means to large datasets such as mini-batch k-means and distributed implementations. Discuss trade-offs between speed and cluster quality.
HardTechnical
0 practiced
Implement Bellman-Ford in Python to compute shortest paths from a source node in graphs that may have negative edge weights. Show how to detect negative cycles reachable from the source and return nodes involved in a negative cycle. Explain why Bellman-Ford is O(VE) and when it is preferred over Dijkstra.
HardTechnical
0 practiced
Explain the role of convexity in optimization and how it affects convergence guarantees for gradient descent. Provide convergence rate bounds for L-smooth convex functions and μ-strongly convex functions, discuss step-size selection and the effect of condition number, and explain practical consequences for training convex surrogate models.
HardTechnical
0 practiced
Given a tree where each node has a weight, compute the maximum-weight independent set (no two adjacent nodes both selected). Provide an O(N) dynamic programming solution that computes include and exclude values per node, explain root selection and recursion, and outline how to reconstruct the chosen set in Python.

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