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In this exercise, we're focusing on solving recurrences via recursion trees. It may help to visualize the recurrence by drawing out the tree yourself. Consider

In this exercise, we're focusing on solving recurrences via recursion trees. It may help to visualize the
recurrence by drawing out the tree yourself.
Consider the following recurrence:
T(n)=T(n3)+T(2n3)+O(n),T(1)=1
For the purposes of analyzing the recurrence, we can replace the O(n) term with n. This changes the final
answer by a constant factor, which is irrelevant since we only care about the asymptotic complexity of the
recurrence. Thus, we can rewrite the recurrence as:
T(n)=T(n3)+T(2n3)+n,T(1)=1
Now let's analyze the Big O of this recurrence.
For the (Work Per Node, Number of Nodes) column, the input must be formatted in order to be marked
correctly. Enter your input as a tuple. If there are different values for the work per node, enter as comma
separated tuples. Use ???? for exponentiation. You may use a calculator here. WolframAlpha is useful for
displaying answers in fraction form instead of floating point. Complicated arithmetic will not appear on
exams.
The following are examples of valid inputs:
(n,1)
(n???29,1),(n???24,2)
(2**n9,1)
While these are invalid:
n,1 Not a tuple!
((n3)???2,1),(14**n???2,2) Incorrect formatting!
(2n9,1) Incorrect formatting!
Can you try generalizing the total work for level l?(use I for your input)
What pattern do you see?
Work per level is a(n)
What insight do we gain from this pattern?
The Big O sum is work per level * number of levels
What is total number of levels in this tree? To enter logs in the form logb(a), enter your input as log(a,b).
And finally, let's put it all together! What is the overall running time of the recurrence?
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