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• Big O notation is an algebraic way to express Space and time complexity of a programming process
• Big O - Is used to understand time and space complexity , when comparing algorithm that solves the same problem.
• O(n) This is a notation to define complexity: O-> Order of magnitude of complexity (n) -> A function of the size and time
• Data structure and Algorithm practice codes www.AlgoExpert.io

Are set of step/approach used to solve particular task/problem

Key Components that defined good Algorithm.

- Correctness
- Efficiency
     - Time Complexity -> Running Time
         The benchmark for measuring efficiency in terms of time is 
      using a worse case scenario is using the last nth trial 
         In this case a range 1-100 search 100th trial to get the target value 100 will be worst case scenario
     - Space Complexity -> Memory Usage

- Step:1 An Algorithm must be clear and  have clearly defined problem statement

- Step:2 There must be an input ,There must be an output 

- Step:3 They must contain specific instruction set defined in a particular order of execution

- Step:4 The algorithm must complete and should not take the infinite amount of time {for different same 
- input scenario} -> this makes the algorithm correct

- Step:5 The Step must be distinct

- Step:6 The execution step should not break into further subtast

- Step:7 The final output an algorithm must be consistent each time it is rerun {for different same input scenario} -> this makes the algorithm 

Linear Equation time = aX+b 

• Step1: I am searching for a number 10 in the range of 1-10

• Step2: input 1 and 10 to output 10

• Step3: Steps

  • the numbers has to be sorted
  • Algorithm approach : Linear search in ascending order.
  • Starting with 1, This means that my search will linearly go from 1 until it get to the final value 10

The input list of must have been sorted values and the output is position of the final value in the sorted list The Binary Search use a binarily divide mechanism and evaluate the target value against the binary value

-   A binary search between 1-100, the search starts at 50 and evaluate if 5 > 50 if answer is false:
-   Search trims off 51-100, and shift to 25 evaluate if 5 > 25 if answer is false
-   Search trims off 25-50 , and shift to 13 evaluate if 5 > 13 if answer is false
-   Search trims off 13-25 , and shift to 6 evaluate if 5 > 6 if answer is false
-   Search trims off 6-13 ,  and shift to 3 evaluate if 5 > 3 if answer is True
-   Search trims off 1-3 ,  and shift to 5 evaluate if 5 = 5 if answer is True, We have our Final answer compared 

Linear Search - Will have to scan the array to get to target value, It will perform better if the value is 5 , What if the value is 100, then the Binary search requires just six steps while linear requires 100th steps.

This is measuring magnitude complexity as a function of size O(n)- Upper Bounds, This means measuring algorithm at a worst case scenario

This helps to evaluate algorithm instead of haven't to draw up charts Scenario -> (4,10) (?,10m) If it takes 4 tries when n is 10 , how many trials will it take the algorithm when n is 10 million

For Linear Search -O(n) = Runtime Operation is constant irrespective of the size of the list O(1) =>Constant time For Linear Time - O(n)