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Introduction to Dynamic Programming: Java

Learn how to use dynamic programming to solve complex recursive problems.

What is Dynamic Programming?

Dynamic Programming is a programming technique used to solve recursive problems more efficiently. Specifically, it adds time efficiency, and it does so by taking advantage of data structures to store reusable solutions to intermediate steps, thus saving redundant computations. It’s a way of solving problems with recursive relationships by solving smaller problems and building up to the solution to the original problem.

Let’s take a look at a simple algorithm that can get computationally complex very quickly, and then let’s use dynamic programming to increase its efficiency.


The Fibonacci series is a classic mathematical series in which each number is equal to the sum of the two numbers before it, always starting with 0 and 1:

0, 1, 1, 2, 3, 5, 8, 13, 21, etc.

The 0th Fibonacci number is always 0 and first Fibonacci number is always 1. So the second Fibonacci number is 0 + 1 = 1, third Fibonacci number is 1 + 1 = 2, and so on. You could calculate the nth number iteratively this way, but you could also calculate it recursively:

  if n is 0 or 1
    return n
    return fib(n - 1) + fib(n - 2)

This technique breaks up calculating the nth number into many smaller problems, calculating each step as the sum of calculating the previous two numbers.

Although this technique will certainly work to find the correct number, as n grows, the number of recursive calls grows very quickly. Let’s visualize all the function calls if we were to calculate the fourth Fibonacci number:

fib(4) -> fib(3) + fib(2)
  fib(3) -> fib(2) + fib(1)
    fib(2) -> fib(1) + fib(0)
  fib(2) -> fib(1) + fib(0)

As you can seefib(2) is called twice, fib(1) is called three times. If n were larger than 4, you’d see these numbers of calls get high very quickly. For instance, to calculate the 10th number, we’d make 34 calls to fib(2) and 177 total function calls! Why do we need to call the same function multiple times with the same input?

We don’t! We can use a dynamic programming technique called memoization to cut down greatly on the number of function calls necessary to calculate the correct number.


Memoization is a specialized form of caching used to store the result of a function call. The next time that function is called, if the result of that function call is already stored somewhere, we’ll retrieve that instead of running the function itself again. Memoization can result in much faster overall execution times (although it can increase memory requirements as function results are stored in memory).

Memoization is a great technique to use alongside recursion. The memo can even be saved between function calls if it’s being used for common calculations in a program.

Memoizing Fibonacci

In the previous example, many function calls to fib() were redundant. Let’s memoize it in order to speed up execution.

To begin, we’ll use a Java HashMap to store the memoized values. Each key will represent n (starting from 0), and the corresponding value will be the result of that Fibonacci number. Then, whenever we need to calculate a number, if it’s already been calculated, we can retrieve the value from the map in O(1) time.

In pseudocode, our approach to memoization will look something like this:

Create a memo map

fibMemo(n, map)
  if n is 0 or 1
    return n
  if n key exists in map
    return map.get(n)
    calculate current fibonacci number through a recursive call
    store value in map
    return value

Let’s implement it in Java!

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