Proving the Maximum Rule in Big O Notation

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In summary, the conversation discusses the proof of the statement O(max(f1(n),f2(n),...,fk(n))) = O(f1(n)+f2(n)+...+fk(n)) and the concept of the "Big Oh" notation in complexity theory. The proof is done by induction and relies on the fact that the sum of the functions is less than or equal to the maximum value. However, there is some uncertainty about the validity of the statement and more clarification is needed.
  • #1
khdani
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Hello,
please help me to prove that:
O(max(f1(n),f2(n),...,fk(n))) = O(f1(n) + f2(n) + ... + fk(n))

thank you
 
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  • #2
Proof by induction looks like the obvious thing to try.
 
  • #3
what O meens?

there is some logical error
weird flying robot question

in the first part you mention:
O(max(f1(n),f2(n),...,fk(n)))
i can only guess that the max function returns the maximal value
among the presented functions
on the other hand you say that this maximal value should be equaled to the sum
of all the presented functions in the list.
i think that the only way i t could work
is when all the functions except one are all zeros
you have f1=0 f2=0 ..f15=50 f16=0 etc..
then their sum also equals to the maximal function value.
this whole thing is only a presumption

well i am back to my digital design questions
 
Last edited:
  • #4
If you do not understand a notation, just ask about it and assume that what other people are saying is wrong!

The "O" notation refers to the "order" or, for functions of an integer, n, the rate of convergence (or divergence) as n goes to infinity. Saying that O(max(f1(n), f2(n),..)= O(f1(n)+ f2(n)+ ...) says that the sequence formed by choosing ai the largest of f1(i), f2(i), ..., converges (or diverges) as fast as the sequence an= sum of f1(n)+ f2(n)+ ...
 
  • #5
'O' is the 'Big Oh' notation in complexity theory.

HallsofIvy, you're right the proof is by induction.
I've used the fact that f1(n)+...+fk(n) <= k*max(f1(n),...,fk(n)) (proved by induction also), am I on the right route ?
 

What is the Maximum Rule in Big O Notation?

The Maximum Rule in Big O Notation is a mathematical concept used to analyze the efficiency of algorithms. It states that in a series of mathematical expressions, the overall growth rate is determined by the term with the highest growth rate.

Why is the Maximum Rule important in computer science?

The Maximum Rule is important because it allows us to quickly determine the time complexity of an algorithm by focusing on the term with the highest growth rate. This allows us to compare and analyze the efficiency of different algorithms and choose the most efficient one for a given problem.

How do you prove the Maximum Rule in Big O Notation?

To prove the Maximum Rule, we need to show that the term with the highest growth rate dominates the overall growth rate of the expression. This can be done through mathematical induction or using the limit comparison test. By showing that the term with the highest growth rate becomes significantly larger as the input size increases, we can prove that it is the dominant term.

What is the difference between the Maximum Rule and the Simplified Maximum Rule?

The Maximum Rule considers the term with the highest growth rate as the dominant term, while the Simplified Maximum Rule only considers the highest degree term. This means that the Simplified Maximum Rule may not always provide an accurate analysis of the efficiency of an algorithm, as it ignores any lower degree terms that may still contribute significantly to the overall growth rate.

Can the Maximum Rule be applied to all algorithms?

Yes, the Maximum Rule can be applied to any algorithm that has a time complexity expressed in terms of an input size. However, it may not be the most efficient approach for some algorithms, as it only considers the asymptotic behavior and not the actual runtime of the algorithm. Other factors such as memory usage and data structure also play a role in determining the efficiency of an algorithm.

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