Feature #11: Generate Movie Viewing Orders
Explore how to generate all permutations of movies in a marathon using backtracking. Understand the approach and its time and space complexity, improving your problem-solving skills and preparing for coding interviews focused on generating permutations.
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Description
We want to offer marathons for our viewers. Each marathon will have a fixed set of movies catering to a specific taste. For a given marathon, different viewing orders of the movies will yield different user satisfaction results. We want to experiment (A/B testing) with different viewing orders of the same marathon.
Your task is to generate all the possible permutations of movies in a given marathon.
Let’s look at an example to better understand this:
Solution
To solve this problem, we will use the backtracking approach.
We will assume a Backtrack function that takes the index of the first movie to consider as an argument Backtrack(first).
-
If the first movie to consider has index
n, then that means that the current permutation is done. -
We will iterate over the marathon from index
Firstto indexn - 1. -
We will place the
ith movie first in the permutation, that is,Movies[First], Movies[i] = Movies[i], Movies[First]. -
We will proceed to create all the permutations that start from the
ith movie:Backtrack(First + 1). -
Now we will backtrack, that is,
Movies[First], Movies[i] = Movies[i], Movies[First]back.
Let’s look at some illustrations to better understand this:
Note: We are using numbers to represent movies in the following illustration.
Let’s look at the code for this solution:
Complexity measures
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