A fundamental concept for software engineers when it comes to writing maintainable software is the DRY principle: Don't repeat yourself. Many times when writing code we may find ourselves implementing algorithms that are very similar in structure to ea
The set ADT is an important and unique (see what I did there?) data structure with many uses, and many ways to implement them. Often implemented over a linked structure, sets are not quite a list and not quite a dictionary, but often have similar funct
When implementing data structures, its crucial to validate that your implementation is working as expected. A suite of tests is essential to not only debugging, but also optimizing performance one your implementation is correct. In the past I've discus
As computer architectures continue to evolve, data structures which were once considered well suited for one task may shift to become more applicable for a different task. One example of this is the family of balanced search tree's known as B-Trees. B-
I want you to read through the following implementation of mergesort, and think about the reasoning behind why writing this particular algorithm in this particular way would be. I mean, it starts off with a caveat that if a cer
-
Procedural Map Generation with Binary Space Partitioning
-
Exact Match String Searching: The Knuth-Morris-Pratt Algorithm
-
The visitor pattern: OOP takes on the Expression Problem
-
Improving mgcLisp's define syntax
-
Separating the Men from the Boys, Knuth Style
-
Reducing rotations during deletion from AVL trees
-
Parsing Lisp: From Data To Code
-
Swiz Heaps: A deterministic modification of Randomized Meldable Heaps
-
Let's talk Eval/Apply
-
BST Deletion: Removal By Merge