# The Context Clues of an Algorithm

A hard lesson I have been learning from my coding challenges lately, is to pay more attention to the context clues in the description, and less to ...

A hard lesson I have been learning from my coding challenges lately, is to pay more attention to the context clues in the description, and less to the test cases.

I would think that a brand new programmer doing their first coding challenge, could be delighted to see that the problem only has 2 test cases, and both are quite simple. Now having done several challenges, theres nothing worse for me to see than this. It’s really only a problem when the coding challenge is automated and virtual, because when you see such simple test cases, it leaves you with many questions and no one to answer them. I have always been given constraints such as 0<n<10⁵ but I am left wondering, are the values in an array always sorted as they are in the test cases? Is the first element in the array always 1 as it is in the test cases? There are plenty of questions to be asked in any given algorithm, and when you’re left without an examiner, it is up to you to make all the right assumptions, and prepare for every possible case. So more test cases can often tell you more about the possible inputs, but there are other clues that can further help you solve the problem.

When a problem asks for the maximum or minimum solution from its input that satisfies its constraints, it is often taking advantage of a greedy algorithm, and looking for an optimal solution. Common algorithms involving this include the coin change problem, where you have to find the minimum coins to reach a certain value, and the efficiency problem. The efficiency problem is often themed in different ways, but you are typically given 2 arrays representing arrival time and length of stay of events, and you have to find the maximum number of events that can take place without overlapping. This is a problem I have seen time and time again and if you are taking coding challenges I suggest practicing with this. These are problems looking for an optimal solution which can be solved through a greedy algorithm. An example of the coin change solution could be as follows:

It is important to note that if an algorithm can be solved with a greedy method, it does not mean that it is the best solution.

Another context clue that appears in algorithms is ‘number of ways’. This is actually what inspired me to write this blog post, as I was so disappointed that I missed something I had practiced so much. If a problem asks for you to return the ‘number of ways’ a value can be reached or manipulated, chances are, a key aspect of solving the algorithm is the Fibonacci sequence. As a reminder, the Fibonacci sequence solves for what the value of n is, by adding the values of n-1 and n-2. It is not just important to understand the sequence, you also have to know when you need to implement it. Common problems involving this sequence include fining the number of ways you can reach the nth step, and finding the number of ways you can decode an integer into characters. Lets work out the 2nd of the two. Numbers can decode alphabetic characters as follows: 1 = A, 2 =B… 26 = Z. Return the number of ways a given input string of numbers can be decoded, ex. 12 => 2 because 1,2 = AB or 12 = L.

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