Memoization

Today I will be explaining memoization and it’s uses when solving algorithmic problems with recursion. In a previous blog I listed the pros and cons behind using recursive solutions in the place of iterative solutions when solving common interview questions. Two of the biggest cons being that recursion uses a large amount of memory and that it often times takes longer to complete in comparison to the iterative solution.

However, memoization can help you cut down on both the memory used and time needed to solve problems with recursive solutions. By definition, memoization is a technique used to optimize the time needed to run computer programs. It is done by storing expensive function calls in a cache and returning the cached value of the stored function if it has been called previously.

Below are three possible solutions to one of the most popular interview questions out there. Be sure to see how memoization affects both the time needed for completion and the time complexity!

Iterative solution

Recursive solution without memoization

Recursive solution with memoization

In the last picture you can see that the embedded function inside of memoize checks to see if there is an existing key in the cache object. If so, the result of that function is then returned to prevent repetitive function calls. Another important thing to notice between the last two functions is the difference in time complexity. This is a property that measures the efficiency of a program. We can see it has drastically improved with the successful implementation of memoization.

As a new developer looking for their first role, practicing common algorithmic problems and how to solve them in a variety of ways is very important. Although, I think it is just as important to know how efficient each solution is. With memoization, you can see how the speed and time complexity of a program improve dramatically (roughly 680 times faster). Keep this in mind when being asked how you can improve your solutions in an interview!

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