Data Structures and Algorithms
Primary version
Primary version
  • Data Structures and Algorithms
  • Algorithms Analysis
    • Measuring Resource Consumption
    • Growth Rates
    • Asymptotic Notation
    • Analysis of Linear Search
    • Analysis of Binary Search
    • How to do an analysis in 5 steps
  • Recursion
    • Writing a recursive function
    • How do recursive functions work?
    • Analysis of a Recursive Function
    • Drawbacks of Recursion and Caution
  • Lists
    • Implementation
    • Linked List
      • Concepts
      • Implementation - List and Nodes
      • Implementation - push_front(), pop_front()
      • Implementation - Iterators
      • Modification - Sentinel Nodes
  • Stacks and Queues
    • Stack Implementation
    • Queue Implementation
  • Table
    • A Simple Implementation
    • Hash Tables
      • Bucketing
      • Chaining
      • Linear Probing
  • Sorting
    • Simple Sorts
      • Bubble Sort
      • Insertion Sort
      • Selection Sort
    • Merge Sort
    • Quick Sort
    • Heap and Heap Sort
      • Priority Queues using Binary Heaps
      • Heapify and Heap Sort
  • Trees
    • Binary Trees
    • Binary Search Trees
    • BST Implemenation
    • Iterative Methods
    • Recursive Methods
  • AVL Trees
  • Red Black Trees
  • 2-3 Trees
  • Graphs
  • Introduction to Computational Theory
  • Appendix: Markdown
  • Appendix: Mathematics Review
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On this page
  • Mathematical Notations and Shorthands
  • Shorthands
  • Variables
  • Functions
  • Sigma Notation
  • Mathematical Definitions and Identities
  • Exponents
  • Logarithms
  • Series

Appendix: Mathematics Review

A certain familiarity with certain mathematical concepts will help you when trying to analyze algorithms. This section is meant as a review for some commonly used mathematical concepts, notation, and methodology. Where possible analogies between mathematical and programming concepts are drawn

Mathematical Notations and Shorthands

Shorthands

shorthand

meaning

iff

if and only if

therefore

approximately

a * b

a * b

absolute value of a

Variables

In math, like programming, we use variables. Variables can take on some numeric value and we use it as a short hand in a mathematical expression. Before using a variable, you should define what it means (like declaring a variable in a program)

For example:

"Let n represent the size of an array"

This means that the variable n is a shorthand for the size of an array in later statements.

Functions

Similar to functions in programming, mathematics have a notation for functions. Mathematically speaking, a function has a single result for a given set of arguments. When writing out mathematical proof, we need to use the language of math which has its own syntax

As a function works with some argument, we first define what the arguments mean then what the function represents.

For example:

Summary

When we talk about big-O notation (and related little-o, theta and omega notation) those are NOT functions. For example O(n) is NOT a function named O that takes a variable n. It's meaning is different.

Sigma Notation

Sigma notation is a shorthand for showing a sum. It is similar in nature to a for loop in programming.

General summation notation.

The above notation means that there are n terms and the summation notation adds each of them together.

Example:

Mathematical Definitions and Identities

Mathematical identities are expressions that are equivalent to each other. Thus, if you have a particular term, you can replace it with its mathematical identity.

Exponents

Definition

Identities

Logarithms

Definition

Identities

Series

A series is the sum of a sequence of values. We usually express this using sigma notation (see above).

Identities

PreviousAppendix: Markdown

Last updated 5 years ago

ceiling, round aa up to next biggest whole number. Example:

floor, round aa down to the next smallest whole number. Example:

Let nnn represent the size of the array - (n is the name of the argument).

Let T(n)T(n)T(n) represent the number of operations needed to sort the array - T is the name of the function, and it accepts a single variable nnn

We pronounce T(n)T(n)T(n) as "T at n". Later we will assoicate T(n)T(n)T(n) with a mathematical expression that we can use to make some calculation. The expression will be a mathematical statement that can be used to calculate the number of operations needed to sort the array. If we supply the number 5, then T(5)T(5)T(5) would be the number of operations needed to sort an array of size 5

T(n)T(n)T(n) - read it as T at n, we call the function TTT .

T(n)=n2+n+2T(n) = {n^2} + n + 2T(n)=n2+n+2 means that T(n)T(n)T(n) is the same as the mathematical expression n2+n+2{n^2} + n + 2n2+n+2. Think of T(n)T(n)T(n) as being like the function prototype, and n2+n+2{n^2} + n + 2n2+n+2 as being like the function definition

nn n can take on any value (unless there are stated limitations) and result of a function given a specific value is calculated simply by replacing n with the value

T(5)=52+5+2=32T(5) = {5^2} + 5 + 2 = 32T(5)=52+5+2=32 ( we pronounce T(5) as "T at 5")

∑i=1nti=t1+t2+...+tn\sum\limits_{i=1}^{n} t_i = t_1 + t_2+ ... + t_ni=1∑n​ti​=t1​+t2​+...+tn​

Typically the terms tit_iti​,​​ is some sort of mathematical expression in terms of iii (think of it as a calculation you make with the loop counter). The iii is replaced with every value from the initial value of 1 (at the bottom of the ∑\sum∑ ), going up by 1, to n (the value at the top of the \sum∑)

∑i=15i=1+2+3+4+5\sum\limits_{i=1}^{5} i = 1 + 2 + 3 + 4 + 5i=1∑5​i=1+2+3+4+5

​​ xn{x^n}xn means (x)(x)(x)...(x)(x)(x)(x)...(x)(x)(x)(x)...(x) (n x's multiplied together)

xaxb=xa+b{x^ax^b}={x^{a+b}}xaxb=xa+b

xaxb=xa−b​\frac{x^a}{x^b} = {x^{a-b}}​xbxa​=xa−b​

(xa)b=xab{({x^a})^b} = {x^{ab}}(xa)b=xab

xa+xa=2xa≠x2a{x^a}+{x^a} = 2{x^a} \neq {x^{2a}}xa+xa=2xa=x2a

2a+2a=2(2a)=2a+1{2^a}+{2^a} = 2({2^a}) = {2^{a+1}}2a+2a=2(2a)=2a+1

In computer text books, unless otherwise stated logloglog means log2log_2log2​ ​​ as opposed to log10log_{10}log10​ ​​ like math text books

bn=a{b^n} = abn=a iff logba=n log_ba = nlogb​a=n In otherwords logbalog_balogb​a is the exponent you need to raise bbb by in order to get aaa

log⁡ba=log⁡calog⁡cb\log_ba = \frac{\log_ca}{\log_cb}logb​a=logc​blogc​a​ , where c>0c > 0c>0

log⁡ab=log⁡a+log⁡b\log {ab} = \log a + \log blogab=loga+logb

log⁡(ab)=log⁡a−log⁡b\log (\frac{a}{b}) = \log a - \log blog(ba​)=loga−logb

log⁡ab=blog⁡a\log {a^b} = {b}{\log a} logab=bloga

log⁡x<x\log x < xlogx<x for all x>0x > 0x>0

log⁡1=0\log 1 = 0log1=0

log⁡2=1\log 2 = 1log2=1

∑i=0nc(f(i))=c∑i=0nf(i)\sum\limits_{i=0}^{n} c(f(i)) = c \sum\limits_{i=0}^{n}f(i)i=0∑n​c(f(i))=ci=0∑n​f(i) ​, where ccc is a constant

∑i=0n2i=2n+1−1\sum\limits_{i=0}^{n} {2^i} = {2^{n+1}} - 1i=0∑n​2i=2n+1−1

∑i=0nai=an+1−1a−1​\sum\limits_{i=0}^{n} {a^i} = \frac{a^{n+1}- 1}{ a - 1}​i=0∑n​ai=a−1an+1−1​​

∑i=0nai≤1a−1\sum\limits_{i=0}^{n} {a^i} \leq \frac{1}{a-1}i=0∑n​ai≤a−11​

∑i=1ni=n(n+1)2​\sum\limits_{i=1}^{n} {i} = \frac{n(n+1)}{2}​i=1∑n​i=2n(n+1)​​

∑i=1ni2=n(n+1)(2n+1)6\sum\limits_{i=1}^{n}{i^2} = \frac{n(n+1)(2n+1)}{6}i=1∑n​i2=6n(n+1)(2n+1)​

∑i=1nf(n)=nf(n)​\sum\limits_{i=1}^{n}{f(n)} = nf(n)​ i=1∑n​f(n)=nf(n)​

∑i=n0nf(i)=∑i=1nf(i)−∑i=1n0−1f(i)\sum\limits_{i=n_0}^{n} f(i) = \sum\limits_{i=1}^{n} f(i) - \sum\limits_{i=1}^{n_0 - 1} f(i)i=n0​∑n​f(i)=i=1∑n​f(i)−i=1∑n0​−1​f(i)

∴\therefore∴
≈\approx≈
ababab
a(b)a(b)a(b)
∣a∣|a|∣a∣
⌈a⌉\lceil{a}\rceil⌈a⌉
⌈2.3⌉=3\lceil{2.3}\rceil = 3⌈2.3⌉=3
⌊a⌋\lfloor{a}\rfloor⌊a⌋
⌊2.9⌋=2\lfloor{2.9}\rfloor = 2⌊2.9⌋=2