Dynamic Programming explained (hopefully)

Okay, so some of you certainly already heard about Dynamic Programming (DP), but this is what I understood so far and I am happy to share with you.


In short, DP is all about ordering your computations in a way that avoids recalculating duplicate work: you have a main problem and a lot of subproblems.

There are two key attributes that a problem must have in order for DP to be applicable: optimal structure and overlapping subproblems:

  • when you have an optimal structure, then the optimal solution of a given problem can be obtained by the combination of optimal solutions of its subproblems
  • when you have overlapping subproblems then a solution of a problem should require the same subproblem again and again

Hey, please note that if a problem can be solved by combining optimal solution of non overlapping subproblems then we are in the “divide and conquer” area, where for example merge sort and quick sort lies.

Dynamic Programming is typically implemented using two common techniques, tabulation and memoization:

  • when you solve a DP problem using tabulation you solve the problem using a bottom-up approach, by solving all subproblems first, and creating a n-dimensional table: based on such table the solution to the original problem is computed. Because of that, tabulation solves all the subproblems.
  • when you solve a DP problem using memoization you do it by maintaining a map of already solved subproblem: you solve the problem¬†top-down, basically solving the top problem first and then recursing in solving the subproblems. Memoization may pay an overhead due to the recursion, but it does not need to solve all the subproblems

Please note that in DP you will often use backtracking, that incrementally builds candidates for the solution and then abandons them when it determines that they cannot contribute to the solution.

Some code, please!

Ok, all good. Now where do we go from here? Some code will help ūüôā A typical DP problem is the fibonacci sequence:

fib(n) = fib(n-1) + fib(n-2)

I guess you can already see the overlapping subproblems and the optimal structure: let’s try to solve this with the most natural solution (I guess), which is a recursion.

    private static int fib(int val) {
        if (val == 0 || val == 1)
            return 1;
            return fib(val - 1) + fib(val - 2);

Ok, cool. It works: result! Pretty inefficient tough,  as it uses a large amount of stack memory and computes the solution to the same problem again and again! In fact, for example, to compute fib(5) it will compute three times fib(2). How can we improve this? Well, memoization comes in handy:

    private static Map<Integer, Integer> cache = new HashMap<Integer, Integer>();

    private static int fib(int val) {
        if (val == 0 || val == 1)
            return 1;
        else {
            Integer res = cache.get(val);
            if (res == null) {
                res = fib(val - 1) + fib(val - 2);
                cache.put(val, res);
            return res;

Ok, this is better. At least we do not recompute a lot of times the same solution, but we still use a lot of stack memory, to handle the recursion. And, at the end of the day, we need to compute all the solution to solve this problem, don’t we? Why don’t we use tabulation then? if we do so, we can revert to a nice iterative solution!

    private static int fib(int val) {
        if (val == 0 || val == 1)
            return 1;
        int fibs[] = new int[val+1];
        fibs[0] = 1;
        fibs[1] = 1;
        for (int i=2; i<=val; i++)
            fibs[i] = fibs[i-1] + fibs[i-2];
        return fibs[val];

Ah, that’s better! No more recursion, a plain iterative process going on, just a bit of memory used for our table. But wait… can we do any better? Do we really need the whole table? Can we do better than Dynamic Programming?

    private static int fib(int val) {
        int prev = 0;
        int curr = 1;
        for (int i=2; i<=val; i++) {
            int next = curr + prev;
            prev = curr;
            curr = next;
        return curr;

Oh yeah ūüôā We just need to keep the last two values, n-1 and n-2: job done!

Conclusions (?)

DP was useful to think out the best algorithm, it was instrumental to discover it but, then, well, we needed that plain old spark of genius that not all of us have (certainly not me!) and some help has been very welcome. But without DP (and without a bigger spark) we would never easily found out an O(n) elegant and efficient solution: so it helps knowing about it. And sometimes some problems are really not solvable without DP, so please do not underestimate it!

Let me know if you are interested in this stuff, I can post more ūüôā




6 thoughts on “Dynamic Programming explained (hopefully)

  1. Hi Bruno,
    I think there’s a typo:

    when you solve a DP problem using memoization you do it by maintaining a map if already solved subproblem:

    should be

    when you solve a DP problem using memoization you do it by maintaining a map OF already solved subproblem:

    BTW, very interesting article.

  2. Don’t let a programmer do the work of a mathematician

    public class Fibonacci {

    static double sqrt5 = Math.sqrt(5);
    static double phi = (1 +sqrt5)/2;

    public static long fibonacci(int n) {
    return (long) Math.floor((Math.pow(phi, n)/sqrt5 + 0.5));

    public static void main(String[] args) {
    for(int i=0;i “+fibonacci(i));



  3. That is because Java use finite number approximation for primitive numbers (ie.32 64 bit) .
    That’s not happen with python as example, if you want the same with java you have to use the wrappers of Numbers like BigDecimal.

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