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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
17 practiced
You have a tight inner loop that performs bitwise transforms on an array of 64-bit integers. Discuss concrete low-level optimizations to maximize throughput: cache friendliness, memory alignment, SIMD vectorization (AVX2/AVX-512), branchless programming, loop unrolling, prefetching, and compiler intrinsics. Provide a C/C++ code outline using AVX2 intrinsics for a simple bitwise operation and reason about expected speedups and pitfalls.
EasyTechnical
16 practiced
Explain amortized analysis and demonstrate it with the dynamic array (vector) resizing example where capacity doubles when full. Show the amortized cost per insertion using both the accounting method and the aggregate method. Also explain the worst-case cost of an individual insertion.
HardTechnical
21 practiced
You have a weighted undirected graph with frequent edge weight updates and queries asking shortest path between nodes. Propose an online algorithm and data structure to support updates and queries. Discuss trade-offs: full recomputation, incremental/dynamic algorithms (decremental/incremental), approximate solutions, and practical engineering choices when low latency is required.
EasyTechnical
16 practiced
Write a function in Python or C++ that counts the number of set bits (1s) in a 64-bit unsigned integer. Provide two approaches: a naive loop over bits and Brian Kernighan's algorithm. Explain time and space complexity of each approach and mention hardware or builtin alternatives for production code.
MediumTechnical
22 practiced
Given a directed graph (n up to 1e5) implement topological sort and cycle detection. Provide both Kahn's algorithm (BFS with indegree) and DFS-based postorder approach. Explain which method is preferable for large streaming inputs and why, and how to produce a lexicographically smallest topological order if required.

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