# Max Subset Sum with No Adjacent Elements

# Problem

Write a function that takes in an array of positive integers and returns the maximum sum of non-adjacent elements in the array. If the input array is empty, the function should return

0.

# Concept

A few things to remember about the problem:

- The input array includes only positive integers (negative integers would change the solution)
- You can not add any two elements to the sum if they are next to each other

Let’s walk through an example with the array pictured here.

To solve this problem, the best way to do so is with Dynamic Programming. But what is that? According to Wikipedia:

Dynamic programmingis both a mathematical optimization method and a computer programming method. The method was developed by Richard Bellman in the 1950s and has found applications in numerous fields, from aerospace engineering to economics.In both contexts it refers to simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner. While some decision problems cannot be taken apart this way, decisions that span several points in time do often break apart recursively. Likewise, in computer science, if a problem can be solved optimally by breaking it into sub-problems and then recursively finding the optimal solutions to the sub-problems, then it is said to have optimal substructure.

If sub-problems can be nested recursively inside larger problems, so that dynamic programming methods are applicable, then there is a relation between the value of the larger problem and the values of the sub-problems.[1] In the optimization literature this relationship is called the Bellman equation.

The important point here is simply that we find the solution by breaking the larger problem into smaller problems in order to solve the larger problem! That’s it!

In our example, we will build an auxiliary array of the same length as the input array, and store the max sum up until (and including) that point in the array. There's a reason for this, which we will see shortly.

**Index 0: **the only option at this point is the value at index 0 itself so we store 7 in the auxiliary array.

**Index 1:** Next, we look at 10, and the max sum at that point is just the maximum value of 7 and 10. We can not add them together because they are next to each other.

**Index 2:** At this point, we can add 7 and 12 together because they are not next to each other so the max sum is 19.

**Index 3:** Now we have a few options!

- 7+12 = 19
- 10+7 = 17
- 7+7 = 14

Obviously, 19 is the maximum sum so we will store 19 in the auxiliary array. An important point is illustrated here: we do not necessarily have to use the value at the index we are currently on.

**Index 4:** As the input array grows, the number of options that are possible to choose from grows a lot!

- 7+12+9 = 28
- 10+7 = 17
- 10+9 = 19
- 7 + 12 = 19
- 7+7 = 14
- 7+9 = 16
- 12+9 = 21

And as we move up in indices, the options will go on and on and on and on… For this index, the max sum is 28 so we add that to the auxiliary array.

There’s a pattern here! At each point, the list of options will include the list of options from the previous index. Let’s look at the last index to see the pattern more clearly.

Index 5: I am not going to list all of the options for sums here and instead look at the pattern. The maximum sum at the last index will be the answer and in this case, is 33.

But how does it relate to the rest of the array? At any point, there are really only two choices! The max sum is either the previous max sum and the index before (*i-1*), or it's the max sum at the index before that (*i-2*) plus the value of the index you are on! Don’t believe me? Try it out!

If we look to the index at *i-1*, we can’t add the value because *i *is adjacent to *i-1*. If we look at the index at *i-2*, we already have the max sum of the array to that point, and then we can add the value at *i* to that max sum. After considering both of these options, we will clearly choose the max value out of those two.

So let’s make a formula for that:

There are two base cases we need to be able to use this formula. We need to know maxSums[0] and maxSums[1]. The maxSums[0] is easy because it will always be itself. The maxSums[1] is also easy because it will always be the max value out of the first two numbers. With that knowledge, we can start to loop through the rest of the array and use the formula.

# Solution

We start by writing the function and taking care of the edge cases of the input array being of length 0 or 1.

Now we can start building the auxiliary array called maxSums. We can make a copy of the input array and then overwrite the values as we go. We find the max value of the first and second number, as discussed before to find maxSums[1].

Now we are ready to loop starting at i = 2 and apply our formula from before.

Lastly, we return the last element in the maxSums array and we’re done!

# Time and Space Complexity

The time complexity, in this case, is pretty straightforward, being that we need to loop through each element in the array once giving us O(n) time where n is the length of the array. Considering space, we are creating an auxiliary array of the same length as the input which would also give us O(n). Can you think of a way to optimize space? What if we only kept track of the previous two maxSums? Hmmmmm….. maybe we could get constant space!

Try it out! Happy Hacking!