Algorithms - Thursday Series - Edition 1

Control System Lecturer and True Scenario

"Imagine you are going in a bike at 60 kmph, in an empty road and a people suddenly crossing the road which was 50 meters away and quickly applying break, Hence you are operating prevention control systems to make sure no issues with you and a stranger people"

Above example one of my college days lecturer delivered a speech when starting the class on the subject called "Control systems"

This lecturer happened on my 3rd year B.E Electronics Communication Engineering, Out of my class 30 plus people I still remember that wonderful example !!

If you ask any of my friends, then whoever remembers that particular class lecturer, definitely he / she will share about the exact details like No No kumaran, the lecturer said same Bike Example but if there is pitfall, that was the exact scenario and timing how to apply brake systems !! that will be the real time example of control systems.

I will say to my friend, definitely yeah that is correct, yep that's exactly he said. I am interested in Learning control systems & Algorithms on my college days research

Here is the one of the below example I tried it on the combination of these two subjects

Control Systems & Algorithms = Automated Coding script generation

back in 2005 to 2010 timelines

Coming back to algorithms on computations,

when we take computational capacities between classical computers vs super computers vs quantum computers then obviously we choose quantum computers which solves complex computations into few timelines like

10 years complex tasks in classical computers can be done quantum computers within 10 minutes.

Big Oh Algorithm Time Space Calculations, is it Fourth Dimensional Time Space Continuum

In General taking any technology and/or any products Big Oh Time & Space Complexity calculations are pretty important (on my own interest with Space Research I am recalling with 4th Dimensional Time & Space continuum, hence it is perfect ways of remembering Big Oh notations O(n), O(1), O(n^2), O(logN) )

When any programmer write single line code with any agendas of adding, removing, replacing any form of manipulations

single line codes are --> O(1)

If a code involving iterative looping scenarios anything like For loop, Foreach, while loop;

All are going to be

for(..){ } --> O(N)

This is like a pseudocode, it can any coding language and it is good to calculate these Big Oh notations before writing any piece programming codes

The same goes for Double times looping scenario then it will be like this

for(..){

for(...){

}

}

==> O(N^2) ==> spelled like O N- Square

Binary Search similar loop scenarios will be ==> O(log N)

Here is the difference Binary Search much more optimized measurement compared to linear search

Hence O(log N ) > O(N) is better on Big Oh Notation

How it will be better

So Linear Search take 10 data comparing in for loop

worst case scenario the comparing criteria will meet at 10th time also possible.

O(N), here O(10).

Here is the interesting part on Binary Search comparison will sort the order and then

half of the size choices

so

1st iteration split 10/2

2nd iteration split 5/2

3rd iteration 3/2

Here it is O(log N) = O(log 10) = 3.32 approximately 3rd time iteration we can able to find the match with comparison !!

Hence it is matching exactly at worst case scenario 3rd iteration mathematically also it is proved !!

Hence O(N) < O(log N) perform much optimized Time Space complexities of Big Oh calculations !!

For doing Product research Product design and development Algorithms are core part !!

Interesting insights, Control Systems and Algorithms on these combinations we can able to build many product research