DS161 Module 3 Exam

O(logn)

Time complexity of binary search

O(log(log(n))

Time complexity of interpolation search

hash function

algorithm that converts a hash key to a hash value

dynamic programming

algorithmic paradigm that solves a given complex problem by breaking it into subproblems and stores the results of subproblems to avoid computing the same results again

heap

complete binary tree-based data structure

rehashing

creating a new and bigger hash table

hash table

data structure that implements an associative array

linear probing

finding next free position in hash table when a value is already occupied

min-heap

heap wherein each node's value is greater than or equal to parent node's value

max-heap

heap wherein each node's value is less than or equal to parent node's value

hash key

input of a hash function

collision

occurs when the hash function produces the same hash value for two or more keys

0.75

optimal load factor

hash value

output of a hash function

salt

piece of random data that is added to the password before hashing it

bucket

position within an array in hash tables

chaining

storing linked lists instead of putting hash values in a table

Overlapping Subproblems and Optimal Substructure

two main properties of a problem that suggests that the given problem can be solved using Dynamic programming

Memoization and Tabulation

ways to store values for dynamic programming so that these values can be reused