Category: Blog
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How to Learn Algorithmic Game Theory: Best Responses, Nash Equilibria, Regret and Mechanism Design
A beginner-to-professional learning manual for algorithmic game theory: best responses, Nash equilibria, mixed strategies, regret, inefficiency, auctions and mechanism design.
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How to Learn Succinct Data Structures: Bit Vectors, Rank/Select, Wavelet Trees and Space–Time Trade-Offs
A beginner-to-professional learning manual for succinct data structures: bit vectors, rank/select, wavelet trees, information-theoretic space, cache behaviour and practical trade-offs.
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How to Learn Nearest-Neighbour Search Algorithms: k-d Trees, LSH, HNSW and Recall–Latency Trade-Offs
A beginner-to-professional learning manual for nearest-neighbour search: distance metrics, brute force, k-d trees, LSH, HNSW, recall, latency, memory and production trade-offs.
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How to Learn Persistent Data Structures: Structural Sharing, Path Copying, Versioned Trees and Persistence Trade-Offs
A beginner-to-professional learning manual for persistent data structures: structural sharing, path copying, partial/full persistence, persistent trees, memory growth and professional versioning trade-offs.
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How to Learn Sketching Algorithms: Count-Min Sketch, HyperLogLog, Error Bounds and Mergeable Summaries
A beginner-to-professional learning manual for sketching algorithms: Count-Min Sketch, HyperLogLog, probabilistic error bounds, heavy hitters, mergeable summaries and production trade-offs.
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How to Learn Constraint Programming: Variables, Domains, Propagation, Arc Consistency and Search
A beginner-to-professional learning manual for constraint programming: variables, domains, propagation, forward checking, arc consistency, heuristics, global constraints, optimisation and CP-SAT.
