How to find upper bound for recurrence relation

In summary, the task is to find a tight upper bound for the recurrence relation T(n) = T(n/2) + T(n/3) + c using a recursion tree argument. However, because of the lack of symmetry in the tree and the fact that the index n must be a multiple of 6, it is unclear how to approach this problem. One possibility is to consider the sequence T(36n) = T(18n) + T(12n) + c, but it is not clear how to handle indexes that are not multiples of 12 and 18.
  • #1
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Homework Statement


Find a tight upper bound for the recurrence relation using a recursion tree argument

Homework Equations


T(n)=T(n/2)+T(n/3)+c

The Attempt at a Solution


I don't know how to do this problem because the tree doesn't have symmetry. One side of the tree can keep going because of the lack of symmetry plus at the end there is no way that you get T(1). How do you solve this problem using a recursion tree?
 
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  • #2
I don't understand.
If one consider that the indexation of sequence ##T(n)## is in ##\mathbb{N}-\{0\}##, then the index ##n## must be a multiple of ##6##, since 2 and 3 are mutually prime divisors of ##n##.

At first sight you must consider a sequence of type ##T(6n) = T(3n) + T(2n) + c ##. But for 6 to divide ##3n## and ##2n##, it must divide ##n##. It leads you to consider the sequence ##T(36n) = T(18n) + T(12n) + c ##.

With that, what sense do you give to ##T(n)## for indexes that are not multiple of 12 and 18 ?
 

FAQ: How to find upper bound for recurrence relation

1. What is an upper bound for a recurrence relation?

An upper bound for a recurrence relation is a function that represents the maximum possible value that the recurrence relation can reach. It is used to analyze the time complexity of algorithms and determine their efficiency.

2. How do you find the upper bound for a recurrence relation?

To find the upper bound for a recurrence relation, you can use techniques such as the substitution method, the recursion tree method, or the master theorem. These methods involve solving the recurrence relation and simplifying the resulting equation to determine its upper bound.

3. What is the importance of finding the upper bound for a recurrence relation?

Finding the upper bound for a recurrence relation is important because it helps in determining the time complexity of algorithms. This information is useful in optimizing algorithms and improving their efficiency.

4. Can the upper bound for a recurrence relation be different for different inputs?

Yes, the upper bound for a recurrence relation can vary depending on the input size. It is common for algorithms to have different upper bounds for different input sizes, as the time complexity can vary depending on the size of the input.

5. Are there any limitations to finding the upper bound for a recurrence relation?

Yes, there are some limitations to finding the upper bound for a recurrence relation. For example, the techniques used to find the upper bound may not work for all types of recurrence relations. In some cases, it may also be difficult to accurately determine the upper bound, especially for complex algorithms.

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