Algorithms

• Alogrithms 1&2 - university of Princeton : couresera page , My notes are in this file.
• data structures and algorithms : coursera page My notes Repo

intro

• Best resource: https://algs4.cs.princeton.edu/home/
• Resource: https://introcs.cs.princeton.edu/java/home/
• “ An algorithm must be seen to be believed. ” — Donald Knuth
• “ Algorithms + Data Structures = Programs. ” — Niklaus Wirth
• “ Algorithms: a common language for nature, human, and computer. ” — Avi Wigderson

General notes

• cost : N^2 > N > lg N > 1.
• cost: quadratic > linear > logaritmic > const
• cost for N=1000:
• N^2 = 1000000 - quadratic.
• N = 1000 - lenear
• lg N = 10 - logaritmic
• lg* N = 0-5 - almost const
• 1 = fixed number - const
• in practice: you start using quick sort algorithm, if you find it a bit slow: you stop and chamge to heap sort
• studying algorithms: algorithms stuff
• knapsack problem here: https://github.com/ahmad-ali14/data-structures-and-algorithms/blob/master/algorithm-toolbox/week3/maximum_loot.js
• algorithm for making a good algorithm:
• always start with a naiive algorithm and make it works, normally slow.
• next find a standard toolbox to help you:
  • greedy algorithm.
  • divide and conqur.
  • dynamic programming.
• optimize your algorithm.
• more on algorithms here: https://github.com/aa947/data-structures-and-algorithms/tree/master/algorithm-toolbox
• greedy algorithm:

1- make the first move.
2- test if it's a safe move or start from the begining
3- test if that move is optimized or optimize it the most
4- you get a sub-problem handle it with the same approach.