In100 days of algorithmsbyTomáš Bouda·Jul 2, 2017End of Line.I wrote the first algorithm on March 25. I wrote the last one today, on July 2. 100 days, 100 algorithms, 100 articles.A response icon3A response icon3
In100 days of algorithmsbyTomáš Bouda·Jul 2, 2017Day 100: Segmented Eratosthenes sieveFor the 100th algorithm I chose a segmented Eratosthenes sieve for primes up to 10⁹ implemented in Cython. And this time the goal is to…
In100 days of algorithmsbyTomáš Bouda·Jul 1, 2017Day 99: SimplexLinear programming is an area of mathematics that deals with the simplest form of constrained optimization problem — linear program. And…
In100 days of algorithmsbyTomáš Bouda·Jun 30, 2017Day 98: Romberg integrationRomberg’s method to find a definite integral combines two formulas, extended trapezoidal rule and Richardson extrapolation, to get a good…A response icon1A response icon1
In100 days of algorithmsbyTomáš Bouda·Jun 29, 2017Day 97: Locally weighted regressionLocally weighted regression is a very powerful non-parametric model used in statistical learning.
In100 days of algorithmsbyTomáš Bouda·Jun 28, 2017Day 96: Floyd-SteinbergFloyd-Steinberg dithering is a truly magical technique. It is supposed to fool your eye and brain to make you think that you see more than…
In100 days of algorithmsbyTomáš Bouda·Jun 27, 2017Day 95: Strongly connected componentsWe say that two nodes U and V in a directed graph belong to the same strongly connected component [SCC], if there exists path from U to V…
In100 days of algorithmsbyTomáš Bouda·Jun 26, 2017Day 94: Earley parserYesterday I have implemented an algorithm that directly relates to an implementation of parser for formal grammars. Today I have…A response icon1A response icon1
In100 days of algorithmsbyTomáš Bouda·Jun 25, 2017Day 93: FIRST & FOLLOWIf you plan to implement own parser for a context-free grammar, construction of FIRST and FOLLOW sets will be the first algorithm you will…A response icon3A response icon3
In100 days of algorithmsbyTomáš Bouda·Jun 24, 2017Day 92: PCAPrincipal Component Analysis [PCA] is incredibly useful when you need [among others] to visualise high-dimensional data. It’s also very…A response icon1A response icon1
