Maybe you’ve been looking into a coding career, or perhaps you’re already working as a programmer somewhere. At some point in your journey along the yellow brick coding road, you’ve likely encountered talk of “dynamic programming.” Dynamic programming is an essential tool for any programmer’s toolkit. When you use dynamic programming, you improve application efficiency, identify potential problems, and engineer the most optimal solutions to those issues. If you know what you’re doing, dynamic programming will make you a star developer and boost your coding prowess to the stratosphere. But what is dynamic programming?
We’ve dedicated this guide to answering that question. We’ll examine the concept of dynamic programming, taking a look at its origin, and identifying how to use it in today’s programming world. We will help you get the most out of dynamic programming and introduce you to the concept of sub-problems and memoization. Dynamic programming might feel overwhelming at first, but once you understand the idea, you’ll wonder how you managed without it!
The Basic Dynamic Programming Concept
Okay, so back to the original question. To find out about the dynamic programming concept, we first have to take a trip back to the good old 1950s. Men wore white t-shirts and greased their hair back, women wore poodle skirts and danced in their socks, and refrigerators could somehow protect you from a close-range atomic blast–and computers were starting to show up everywhere.
Back then, computers didn’t have embedded memory; instead, programs were run by feeding a series of punch cards into the system. When combined with time constraints, this method could present efficiency issues. Let’s say that you need to create a schedule that runs all punch cards at specific times and in an order that depends on maximum use from each card. To optimize your card schedule, you’ll create a series of sub-problems to handle the variables. This is the basis for dynamic programming.
Instead of tackling a problem head-on, you can divide that larger problem into a number of smaller problems, or sub-problems. The idea is that if you can make those smaller sub-problems as efficient as possible, then your larger problem can be solved by finding its minimum number of complexities, or in the most optimum way. We would call this the “optimal solution,” where the design works as well as it mathematically can. We would call the collection of well-designed sub-problems that make up the bigger problem as an “optimal substructure.”
It’s important to note that dynamic programming is different from the “divide and conquer” approach. The main difference is that dynamic programming uses overlapping sub-problems—where sub-problems are used more than once in a recursive fashion—and in the divide and conquer approach sub-problems are only used once.
There are multiple ways to formulate optimal solutions to subproblems, just as there are different methods for a dynamic programming approach to solve optimization problems. One of the most common is top-down memoization.
Time to Memoize
Once you’ve identified solutions to your sub-problems, you need to memoize—remember that ungainly doofus of a word?—or store them, so that you can use those solutions whenever you need them.
To create your optimal formula, identify the sub-problem first, and then convert it into a mathematical concept. You’re doing this to look for recurrence (the repeating decision). That recurrence is what you’re going to formulate. You’re looking for a fundamental “if-then” question and answer, such as, “if this number appears at step A, then here is what must occur at step A+1.” Use that reoccurring problem to streamline your program so it will never have to ask the same question twice, or if it does, you don’t have to run the same calculation again because the result is saved.
And there’s your answer, cowpoke. Although the concept can seem mysterious and hard to understand for beginners, dynamic programming is a great way to ensure that your applications run at peak efficiency and with minimum waste. We hope our guide has helped you wrap your head around this key programming concept. It’s in your interest, as a coder, to learn dynamic programming and become the crackerjack coder you’ve always wanted to be!
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