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01 Algorithms

Definitions

Word Definition
Algorithm step-by-step procedure to solve a problem in finite time
Program implementation of algorithm in a programming language
Data Structure Organization of data needed to solve the problem

Algorithms

graph LR
i[/Input/] -->
Algorithm -->
o[/Output/]

Algorithmic outputs always depend on the input.

For example, binary search requires a sorted list as input.

Efficiency of Algorithm

Complexity

Determined by

  1. Space complexity (Space used)
  2. Time complexity (Running Time)

This is more important

Both of the above are defined as a function of input size

Cases

  • Worst
  • Average
  • Best
  • Amortized (Sequence opertions applied to input size \(n\) averaged over time)

Experimental/Emperical Analysis

  1. Write the program for the algorithm
  2. Run the program with different input sizes
  3. Measure running time

For eg, in Java we can use System.currentTimeMillis() 4. Plot the result

Limitations

  1. We need to implement and test
  2. Same programming language must be used to compare 2 algorithms

Because our interpretation of the efficiency may vary with different programming languages 3. Only limited set of input is possible 4. Same h/w & s/w should be used to compare 2 algorithms

Theoretical/Mathematical Analysis

  1. Use pseudocode
  2. Determine the primitive operations

We assume that each primitive operation takes 1 unit of time. 3. Define running time as a function of input size \(n\)

Algorithm arrayMax(a, n)
    Input array A of n integers
    Output maximum element of A

    currentMax <- A[0]
    for i<-1 to (n-1) do
        if A[i] > currentMax then
            currentMax <-A[i]

    return currentMax
Primitive Operations
currentMax <- A[0] 2
- getting A[0] from memory
- assignment
for i \(\leftarrow\) 1 to (n-1) \(n+1\)
comparison
A[i] > currentMax \(2(n-1)\)
currentMax <-A[i] \(2(n-1)\)
{increment counter i} \(2(n-1)\)
1. add
2. assign
return currentMax 1
Total \(7n-2\)

Advantage

  • acknowledges all possible inputs
  • evaluate speed of algorithm independent of hardware/software used

Pseudocode

Simple representation of your program

  • High-level description of algorithm
  • more structured than english, but less detailed than a program
  • hides program design issues

All mathematical formatting like \(n^2\) (subscript) is allowed

Notation Meaning
if … then … [else …] Control Flow
while … do
(more are there, please complete this Thahir)
\(\leftarrow\) Assignment
= Equality (like ==)
>, <, … Comparison

eg

Algorithm arrayMax(A, n)
    Input array A of n integers
    Output maximum element of A

    currentMax <- A[0]
    for i<-1 to (n-1) do
        if A[i] > currentMax then
            currentMax <-A[i]

    return currentMax

Notations

All notations take the worst-case scenario

Let \(f(n)\) be the algorithm for which we are finding the notation

Notation Purpose Condition
\(O(\ g(n) \ )\) Upper Bound \(f(n) \le c \cdot g(n)\)
\(o(\ g(n) \ )\) Strict Upper Bound \(f(n) < c \cdot g(n)\)
\(\Omega(\ g(n) \ )\) Lower Bound \(f(n) \ge c \cdot g(n)\)
\(\omega(\ g(n) \ )\) Strict Lower Bound \(f(n) > c \cdot g(n)\)
\(\Theta(\ g(n) \ )\) Tight 2-Sided Bounds \(c_1 \cdot g(n) \le f(n) \le c_2 \cdot g(n)\)

Big Oh notation

Most commonly-used notation

we neglect the constant factors

examples:

\[ O(1) < O(\log n) < O(n) < O(n \log n) < O(n^2 \log n) < O(n^2) < \dots < O(2^n), O(e^n) \]
// O(n)
for(int i = 0; i<n; i++)

for(int i = 0; i<n; i--)

// O(n^2)
for(int i = 0; i<n; i++)
  for(int i = 0; i<n; j++)

// O( n(n+1)/2 ) = O(n^2)
for(int i = 0; i<n; i++)
  for(int j = 0; j<i; j++)

// O(log_2 n)
for(int i = 1; i<n; i*=2)

for(int i = 1; i<n; i/=2)

// O(log_b n)
for(int i = 1; i<n; i*=b)

for(int i = 1; i<n; i/=b)

// O(n log_2 n)
for(int i = 0; i<n; i++)
    for(int i = 0; i<n; i/=2)

Math Required

  1. Summations

  2. Log formulae

\[ \begin{aligned} \log xy &= \log x + \log y \\ \log \left( \frac{x}{y} \right) &= \log x - \log y \\ \log x^n &= n \log x \end{aligned} \]

  1. Proof Techniques

  2. Basic Probability

Last updated: 2023-01-25 • Contributors: AhmedThahir

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