Category: Blog
-

How to Learn the Hough Line Transform: Parameter Space, Accumulators, Voting, Peak Detection and Robust Computer Vision
Learn the Hough Line Transform from beginner geometry to professional computer vision: rho-theta parameter space, accumulators, voting, peak detection, OpenCV, probabilistic variants and performance trade-offs.
-

How to Learn the Smith–Waterman Algorithm: Local Alignment, Dynamic Programming, Gap Penalties, Traceback and SIMD Acceleration
Learn Smith–Waterman from local-alignment intuition to professional implementation: dynamic programming, traceback, affine gaps, substitution matrices, complexity and SIMD acceleration.
-

How to Learn the Miller–Rabin Primality Test: Modular Exponentiation, Witnesses, Strong Pseudoprimes and Error Bounds
Learn Miller–Rabin from modular arithmetic intuition to professional primality testing: witnesses, strong pseudoprimes, error bounds, Python implementation and cryptographic context.
-

How to Learn the Nelder–Mead Algorithm: Simplexes, Reflection, Expansion, Contraction, Shrinkage and Derivative-Free Optimisation
Learn Nelder–Mead from geometric intuition to professional optimisation practice: simplexes, reflection, expansion, contraction, shrinkage, stopping rules, failure modes and implementation.
-

How to Learn de Boor’s Algorithm: B-Spline Evaluation, Knot Spans, Local Support and Numerically Stable Curve Computation
Quick read: de Boor’s algorithm evaluates a B-spline curve at a chosen parameter value by repeatedly interpolating only the control points that can influence that location. It is the B-spline counterpart of de Casteljau’s method for Bézier curves and is valued for locality, efficiency and numerical stability. One-sentence answer: find the knot span containing the…
-

How to Learn the Matula–Beck Algorithm: Smallest-Last Ordering, Graph Degeneracy, k-Cores and Greedy Coloring
Quick read: The Matula–Beck smallest-last algorithm repeatedly removes a currently minimum-degree vertex, records the removal order, and then reads that order backwards. With the right bucket data structure, this reveals graph degeneracy and supports k-core decomposition and strong greedy-coloring orderings in O(|V| + |E|) time. One-sentence answer: peel the graph from its sparsest exposed vertex…