I’ve mentioned before that I contribute to answering questions on StackOverflow. Something I see all too often is a question asking for “the most efficient way” to solve a first- or second-semester homework problem. It’s clear from the content of the question that the person asking has absolutely no idea how to approach finding a solution.

The best advice I ever got about computer programming, and I was fortunate to get this advice very early on, was essentially “Find a solution. Any solution. Prove that it works. Then find a more efficient solution.” I got that advice 45 years ago and it’s never failed me. In fact, the times I’ve ignored that advice and gone straight to looking for an efficient solution have been some of the most difficult.

There are two primary benefits to finding a simple (non-optimum) solution:

1. You gain insight into the problem by observing how your solution works.
2. You have a known-good solution against which you can test any optimized solution.

Don’t discount the power of those benefits, especially in entry-level computer science work. The assignment is, essentially, “solve the problem.” A simple solution fulfills the goals of the assignment. Do that first! Then, if you’re curious (or can get extra points), spend some time looking for a more efficient method.

This advice helps not only in school, but also once you get out in the workforce. Very often you will be tasked with solving a problem like a data conversion: a one-time job. The simple solution might take a day’s worth of effort, combined manual work and computer processing. Or you could spend a week developing an optimized solution that will convert the data in an hour of processing time. Which do you choose? I can tell you up front that your employer will be much happier if you get it done tomorrow rather than next week.

Sometimes the non-optimum solution is good enough. Implement it and move on.

You can write a working program and then spend a bit more time making it fast. Or, you can write a fast program and spend the rest of your career trying to make it work.