Dynamic programming is breaking down a problem into smaller sub-problems, solving each sub-problem and storing the solutions to each of these sub-problems in an array (or similar data structure) so each sub-problem is only calculated once.

It is both a mathematical optimisation method and a computer programming method.

*Optimisation problems* seek the maximum or minimum solution. The general rule is that if you encounter a problem where the initial algorithm is solved in O(2^{n}) time, it is better solved using Dynamic Programming.

## Why Is Dynamic Programming Called Dynamic Programming?

Richard Bellman invented DP in the 1950s. Bellman named it Dynamic Programming because at the time, RAND (his employer), disliked mathematical research and didn't want to fund it. He named it Dynamic Programming to hide the fact he was really doing mathematical research.

Bellman explains the reasoning behind the term Dynamic Programming in his autobiography, Eye of the Hurricane: An Autobiography (1984, page 159). He explains:

"I spent the Fall quarter (of 1950) at RAND. My first task was to find a name for multistage decision processes. An interesting question is, Where did the name, dynamic programming, come from? The 1950s were not good years for mathematical research. We had a very interesting gentleman in Washington named Wilson. He was Secretary of Defense, and he actually had a pathological fear and hatred of the word research. I’m not using the term lightly; I’m using it precisely. His face would suffuse, he would turn red, and he would get violent if people used the term research in his presence. You can imagine how he felt, then, about the term mathematical. The RAND Corporation was employed by the Air Force, and the Air Force had Wilson as its boss, essentially. Hence, I felt I had to do something to shield Wilson and the Air Force from the fact that I was really doing mathematics inside the RAND Corporation. What title, what name, could I choose? In the first place I was interested in planning, in decision making, in thinking. But planning, is not a good word for various reasons. I decided therefore to use the word “programming”. I wanted to get across the idea that this was dynamic, this was multistage, this was time-varying. I thought, let's kill two birds with one stone. Let's take a word that has an absolutely precise meaning, namely dynamic, in the classical physical sense. It also has a very interesting property as an adjective, and that is it's impossible to use the word dynamic in a pejorative sense. Try thinking of some combination that will possibly give it a pejorative meaning. It's impossible. Thus, I thought dynamic programming was a good name. It was something not even a Congressman could object to. So I used it as an umbrella for my activities."

Sub-problems are smaller versions of the original problem. Let's see an example. With the equation below:

$$1 + 2 + 3 + 4$$

We can break this down to:

$$1 + 2$$

$$3 + 4$$

Once we solve these two smaller problems, we can add the solutions to these sub-problems to find the solution to the overall problem.

Notice how these sub-problems breaks down the original problem into components that build up the solution. This is a small example but it illustrates the beauty of Dynamic Programming well. If we expand the problem to adding 100's of numbers it becomes clearer why we need Dynamic Programming. Take this example:

$$6 + 5 + 3 + 3 + 2 + 4 + 6 + 5$$

We have $6 + 5$ twice. The first time we see it, we work out $6 + 5$. When we see it the second time we think to ourselves:

"Ah, 6 + 5. I've seen this before. It's 11!"

In Dynamic Programming we store the solution to the problem so we do not need to recalculate it. By finding the solutions for every single sub-problem, we can tackle the original problem itself.

*Memoisation *is the act of storing a solution.

## What is Memoisation in Dynamic Programming?

Let's see why storing answers to solutions make sense. We're going to look at a famous problem, *Fibonacci sequence*. This problem is normally solved in Divide and Conquer.

There are 3 main parts to divide and conquer:

**Divide**the problem into smaller sub-problems of the same type.**Conquer**- solve the sub-problems recursively.**Combine**- Combine all the sub-problems to create a solution to the original problem.

Dynamic programming has one extra step added to step 2. This is memoisation.

The Fibonacci sequence is a sequence of numbers. It's the last number + the current number. We start at 1.

$$1 + 0 = 1$$

$$1 + 1 = 2$$

$$2 + 1 = 3$$

$$3 + 2 = 5$$

$$5 + 3 = 8$$

In Python, this is:

```
def F(n): if n == 0 or n == 1: return n else: return F(n-1)+F(n-2)
```

If you're not familiar with recursion I have a blog post written for you that you should read first.

Let's calculate F(4). In an execution tree, this looks like:

We calculate F(2) twice. On bigger inputs (such as F(10)) the repetition builds up. The purpose of dynamic programming is to not calculate the same thing twice.

Instead of calculating F(2) twice, we store the solution somewhere and only calculate it once.

We'll store the solution in an array. F[2] = 1. Below is some Python code to calculate the Fibonacci sequence using Dynamic Programming.

`def fibonacciVal(n): memo[0], memo[1] = 0, 1 for i in range(2, n+1): memo[i] = memo[i-1] + memo[i-2] return memo[n]`

## How to Identify Dynamic Programming Problems

In theory, Dynamic Programming can solve every problem. The question is then:

"When should I solve this problem with dynamic programming?"

We should use dynamic programming for problems that are between *tractable *and *intractable *problems.

*Tractable problems* are those that can be solved in polynomial time. That's a fancy way of saying we can solve it in a fast manner. Binary search and sorting are all fast. *Intractable problems* are those that run in exponential time. They're slow. Intractable problems are those that can only be solved by bruteforcing through every single combination (NP hard).

When we see terms like:

"shortest/longest, minimized/maximized, least/most, fewest/greatest, "biggest/smallest"

We know it's an optimisation problem.

Dynamic Programming algorithms proof of correctness is usually self-evident. Other algorithmic strategies are often much harder to prove correct. Thus, more error-prone.When we see these kinds of terms, the problem may ask for a specific number ( "find the minimum number of edit operations") or it may ask for a result ( "find the longest common subsequence"). The latter type of problem is harder to recognize as a dynamic programming problem. If something sounds like optimisation, Dynamic Programming can solve it.

Imagine we've found a problem that's an optimisation problem, but we're not sure if it can be solved with Dynamic Programming. First, identify what we're optimising for. Once we realize what we're optimising for, we have to decide how easy it is to perform that optimisation. Sometimes, the greedy approach is enough for an optimal solution.** **

Dynamic programming takes the brute force approach. It Identifies repeated work, and eliminates repetition.