Complexity Classes
The Computational Zoo is far more subtle and complex than I thought it was.
It contains P, NP (+complete), EXP, NP-hard, CO-NP (+complete), PSPACE, BPP, BQP, EXPSPACE, 2-EXP, halting problem, decidable, etc!
Big Oh Notation
Big-Oh ( \(O\) ) gives an upper bound on how an algorithm’s resource consumption grows with input size \(n\).
QRKDB
Cassandra MongoDB
PostGres MySQL
MST
Dijkstra
Greedy
Floyd-Warshall
Honestly, the diagrams that I wish to reproduce already exist here. Currently this page is in construction and probably will be until I finish my Doctorate.
“Memory is the mother of all wisdom." — Aeschylus
Babbage's Big Brain
Memory as a Hierarchy — Not a Monolith
Hierarchy exists for two intertwined reasons:
- Physics – Smaller structures are faster and nearer to ALUs but hold less data; larger structures store more but are farther away and thus slower.
- Economics – Fast memory costs disproportionately more per byte.
An efficient system arranges multiple layers so that > the majority of accesses hit the small, fast part, > while the bulk of bytes reside in the large, cheap part.
Naive Search
DFS
BFS
Heuristic Search
A star search
Monte Carlo (MCMT)
Time Complexity Explainer
O(n^2) Searches
Insertion Sort
Selection Sort
etc
O(n log(n)) Searches
Merge Sort
O(n) Searches
Bucket Sort
To classify
Bubblesort
Bogosort
I am working through the second edition of this book by Scott Chacon and Ben Straub.
I have printed out the first 100 pages of the book1, and conducted an inspectional reading (in Mortimer J. Adler's sense of the word).
Deep Work 1: Git Basics, Git Branching.
getting started
"Deep Work: Professional activity performed in a state of distraction-free concentration that pushes your cognitive capabilities to their limit." —Cal Newport