Category: Blog
-
How to Learn Data Compression Algorithms: Entropy, Huffman Coding, Lempel–Ziv and Real-World Trade-Offs
A beginner-to-professional learning manual for data compression algorithms: model redundancy, trace prefix codes, understand dictionary methods, separate lossless from lossy compression, and evaluate ratios, speed, memory and correctness.
-
How to Learn Cache-Efficient Algorithms: Locality, I/O Complexity, Blocking and Cache-Oblivious Design
A beginner-to-professional learning manual for locality, cache hierarchies, I/O complexity, blocking, cache-oblivious algorithms and rigorous memory-performance benchmarking.
-
How to Learn Parameterized Algorithms: FPT, Branching, Kernelization and Choosing the Right Parameter
A beginner-to-professional learning manual for fixed-parameter tractability, branching, kernelization, parameter choice, FPT versus XP and practical operating ranges.
-
How to Learn NP-Completeness and Reductions: Certificates, Polynomial Time, Hardness and Proof Design
A beginner-to-professional learning manual for P, NP, certificates, polynomial-time reductions, NP-hardness, NP-completeness and choosing the right strategy after hardness.
-
How to Learn Number-Theoretic Algorithms: GCD, Extended Euclid, Modular Exponentiation and Primality
A beginner-to-professional learning manual for Euclid’s algorithm, modular arithmetic, fast modular exponentiation, primality testing, invariants, bit cost and implementation safety.
-
How to Learn Parallel Algorithms: Work, Span, Reduce, Scan and When More Processors Actually Help
A learning manual for parallel algorithms: dependency graphs, work and span, reduce and scan, granularity, races, benchmarking and professional parallel performance judgement.
