Algorithms

Internal

• Programming

Overview

• Arrays.
• Lists (single linked and double linked)
• Stack
• Queue
• Sorting
  • Comparison Sort
  • Non-comparison Sort
  • Sorting algorithms characteristics (in-lace, stable)
• Algorithm complexity, Bounds. Understand and document O, Omega and Theta notations. Perform complexity analysis on all the algorithms I examine.
• Set
• Map
• Hash Map
• Distributed Hash Map
• Trees
  • Binary Trees
    • Sorting trees.
  • BTrees
• Tree walking algorithms. Difference between depth first and breadth first.
• Graphs
• Graph algorithms
• Matrix multiplication (NOKB and code).

• lg and ln notation. Link from the Mathematics main page.

• Expected running time, worst-case running time. Average case running time. • Binary tree height. • Stable sort. • Comparison sort algorithm class. Know that comparison sort cannot do better than n ln n. Proof in a separate article. • Random variable analysis. Indicator random variable. • Consider starting upside down, with the bottom sections, and NOKB concepts from them first. • Difference between a binary search tree and a red-black tree. • Difference between list and queue. • Iteration and recursion – fundamentally different approaches. • Probability Distribution. • Understand and document induction. • Need to understand aggregate analysis Section 17.1

Organizatorium

• A data structure is an arrangement of data in computer's memory or external storage. Data structures include arrays, linked lists, stacks, binary trees, hash tables, etc. Algorithms manipulate the data in these structures in various ways.
• Array
• Directed Acyclic Graph
• Associative Array
• Map
• Tree
  • Red-Black Tree
• Consistent Hashing
• https://en.wikipedia.org/wiki/Big_O_notation
• https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo
• http://infotechgems.blogspot.com/2011/11/java-collections-performance-time.html