Asymptotic Notations: Big O, Big Omega and Big Theta Explained (With Notes)
CodeWithHarry
We 'll talk a little bit about asymptotic notation. we talked about order. We talked about ordering. We
have primarily 3 types of asymptic notation big O, big Theta (Θ ) and big Omega (Ω) big O is
represented by capital (O), which is in our English. Big O is set to be O ( g ( n ) ) if and only if there
exist a constant ( c ) and a constant n -node such that 0 ≤ f ( n) ≤ cg (n) is O (g (N) If you watch this
video completely then I guarantee that you will understand these three notations. Mathematically,
mathematically this function can be anything. When we do analysis of algorithms comparing any 2
algorithms then f ( n ) will be time and what is n , it 's input ok , size of input. G ( n) is your function
which will come inside the big O. O ( n²) is Anything Can Be Algorithm it is g (n) that will be here and
which is your algorithm. If you guys can nd any such constant ( C ) and ( n ) -node , then f ( n) is O
( g ( n)" This is the mathematical de nition of big O. If you ca n't nd it then its is not f (n ) is O. This
question is its own truth , it has validity , it will remain valid.
CodeWithHarry
We 'll talk a little bit about asymptotic notation. we talked about order. We talked about ordering. We
have primarily 3 types of asymptic notation big O, big Theta (Θ ) and big Omega (Ω) big O is
represented by capital (O), which is in our English. Big O is set to be O ( g ( n ) ) if and only if there
exist a constant ( c ) and a constant n -node such that 0 ≤ f ( n) ≤ cg (n) is O (g (N) If you watch this
video completely then I guarantee that you will understand these three notations. Mathematically,
mathematically this function can be anything. When we do analysis of algorithms comparing any 2
algorithms then f ( n ) will be time and what is n , it 's input ok , size of input. G ( n) is your function
which will come inside the big O. O ( n²) is Anything Can Be Algorithm it is g (n) that will be here and
which is your algorithm. If you guys can nd any such constant ( C ) and ( n ) -node , then f ( n) is O
( g ( n)" This is the mathematical de nition of big O. If you ca n't nd it then its is not f (n ) is O. This
question is its own truth , it has validity , it will remain valid.
