Trade offs are present in all our activities. Maybe you have heard the so called “No free lunch theorem” in Machine Learning. The theorem states that there is no silver bullet when it comes to machine learning models, but in fact, there is never a silver bullet when it comes to anything. And that is not a computer science principle but an economics principle.

If you have written code for more than one month is almost sure you have experienced the necessity of trade offs when programming or at least have heard about it. Sometimes you sacrifices performance in the name of security, security in the name of scalability, beauty and readability in the name of performance, and so on. Don’t forget you also sacrifices parties and fun in general on the name of programming so make it’s worth it.

In the specific case of algorithms, the main resources are time and memory, so the trade offs always involve those resources. It’s common to find several solutions for the same problem because one of them is faster, but the other one is cheaper when it comes to storage. Off course there are other factors like implementation, simplicity, and security. In this post I’m going to write about combining several solutions to get the one that fulfill our requirements.

First I’m going to show an interesting idea that E.W. Dijkstra proposed to find prime numbers. After that, I will show an idea I came up with to obtain a data structure that combines the power of arrays and linked lists.

## Requirements

I assume you have basic programming skills as well as some knowledge about data structures like arrays, linked lists, and heaps.

It is necessary some notions of big-O notation to calculate complexity.

It would be good you were familiarized with the algorithm called Sieve of Eratosthenes, in case you don’t, you can check this link.

There is no other previous knowledge required in order to understand what I’m about to say.

## Two algorithms, one problem

The problem is to find all prime numbers from 0 (zero) to N. The kind of problems you learn to do when you are beginning your journey in programming. And I want to start with the simplest solution.

In the naive algorithm, we iterate over all numbers `x`

from 2 to N. Then we check whether `x`

has any divisor besides itself and one. For the last step we can check for every number `d`

between 2 and `x - 1`

whether it is a divisor or not.

There is room for improvement in the last step because we only need to check the divisors that are less than or equal to the square root of x. A pseudocde of the algorithm is written below

```
primes(N):
prime_numbers <- []
for x = 2 to N:
is_prime <- true
for d = 2 to sqrt(x):
if d divides x:
is_prime <- false
if is_prime:
prime_numbers.add(x)
return prime_numbers
```

What’s the runtime complexity of the previous algorithm? Well, we take every number from 2 to N and for each one we iterate over all its possible divisors, so we make `O(N*sqrt(N))`

operations, where `sqrt`

stands for the square root function.

What about memory? We only store the primes we find. For a big enough N the amount of primes is relatively small compared with N. So, let’s denote the memory complexity as `O(Primes(N))`

, where `Primes(N)`

is the amount of primes between 0 and N. Note that this is optimum in terms of memory because we need at least to store all the prime numbers.

Probably you already know that there is a better algorithm to find all the prime numbers in a given range: The sieve of Eratosthenes. Let’s look at it.

The idea is to maintain an array of length N where every entry is either false or true. If the i-th position in the array is true, then we say that `i`

is a prime number, otherwise `i`

is not prime.

We start with all positions with a true value and then, for every position, starting with `i = 2`

we make false every multiple of `i`

. We end up with an array in which every position with a true value represents a prime number. The code with some optimizations is presented below.

```
primes(N):
prime_numbers <- [] # empty list
sieve <- a boolean list with length equal to N and all its elements set to true
for i = 2 to sqrt(N): # we only need to analyze the numbers less than or equal to sqrt(N)
if sieve[i]: # if sieve[i] is true then i is prime
prime_numbers.add(i)
for j = i*i to N: # we can start the inner loop in i*i
sieve[j] <- false
j <- j + i
return prime_numbers
```

It can be proved that the algorithm described above do O(N log(N)) operations which is better than the naive approach. Even it can be improved to O(N)! But we need to store an array with all the N numbers, and thus, we end up with a more expensive algorithm in memory terms. Here we have the trade off: time in exchange for memory.

Can we design an algorithm that is faster than the naive idea but cheaper in memory terms than the sieve of Eratosthenes?

## Dijkstra and the production line

In the 60’s, E.W. Dijkstra wrote in one of his manuscripts [PDF] an algorithm that combines the naive and the sieve ideas. But before we talk about it, let’s analyze the differences between the two previous algorithms.

When applying the naive algorithm we focus on analyzing whether every number is prime or not. When applying the sieve we analyze, for every prime number, all its multiples. The difference can be illustrated with the following analogy.

Imagine we want to build several products, like action figures. We have two options: building them one by one or applying a line of production and in every stage we add one component of the action figure. With the latter we end up producing more in less time, but we need the “line infrastructure”.

Dijkstra combined those ideas by analyzing a number at a time but taking advantage of the previous operations. We can maintain a pool of primes that have been already discovered, and, for each of those prime numbers store the least multiple that has not been analyzed yet. For example:

If we are analyzing number 6, then we have stored primes 2, 3 and 5, along with their least-not-analyzed-multiples that are: 6 for 2, also 6 for 3, and 10 for 5.

Then, when analyzing a new number we take the smallest multiple stored until that moment and if that multiple is greater than the new number, then we have found a new prime, otherwise we have a composed number and we need to update the multiples of the stored primes that have the new number as its least multiple.

We begin storing the prime number `2`

with the least multiple `4`

as well. Then when analyzing `3`

we find that `4 > 3`

so `3`

is prime. We store `3`

along with its smallest multiple that has not been analyzed yet (`6`

). When analyzing `4`

we find that `4`

is stored as a multiple of `2`

, then we update the multiple of `2`

that now will be `6`

. When analyzing `5`

we find that `6 > 5`

so `5`

is a prime number and we store it along with `10`

, and so on… The code is presented below.

```
primes(N):
primes_pool <- Heap( (4, 2) )
prime_numbers <- [2]
for i = 3 to N:
tuple <- getMin(primes_pool)
mult <- tuple.first
if mult > i:
prime_numbers.add(i)
primes_pool.insert( (i*i, i) )
continue
for t such that t is in primes_pool and t.first is equal to mult:
t.first <- t.first + t.second
return prime_numbers
```

The previous method only stores the prime numbers and another number for each of those primes. So we have a memory complexity of `O(Primes(N))`

which is the same as the naive idea. If we store the prime numbers along with their multiples in a structure like a heap, we get a time complexity of `O(N*log N)`

which is the same as the sieve! So we got what we wanted!

The trick here is that we don’t need to mark every multiple of the given prime but just the least multiple.

I need to say that this is not a practical idea in the sense that the memory complexity of the sieve of Eratosthenes is not that bad and it is a very easy to implement algorithm. My point is that sometimes you have several ideas, each of one cannot be applied because of some flaw, then maybe is a good idea to combine their strengths and you can get a hybrid applicable solution for your problem. The Factory of Primes of Dijkstra has taught me to think in that way, although I have never implemented that algorithm in real life scenarios.

## Combining arrays with linked lists

Arrays are simple structures that allows us to get an element by its index in constant time. But we need `O(N)`

operations to insert or remove an element to/from the array in the worst case, where N is the length of the array.

On the other hand, linked list are structures composed by nodes. Every node has a reference to the next one, and, in the case of doubly linked list, each node has also a reference to the previous one. In this case we need `O(N)`

operations to reach a node in the worst case, but the insert and remove operations can be done in constant time. I think it’s natural to think about a “perfect data structure” that allows us to make the three operations in constant time. Sadly, such structure does not exist, but I have found a middle point between the two opposite poles.

The “issue” with arrays is that they maintain a reference to all of their elements. That allows us to retrieve any element with the same amount of operations no matter where the element is. But maintaining those references is what makes the insertions and deletions so costly. When it comes to linked lists we only maintain a reference to the first and the last node and each node has a reference to its neighbors. So, when inserting or removing a new element we only need to change a few references. But that lack of references is the cause that we have to spend so much operations to get an element in the worst case.

Viewing the problem from that angle, the idea of finding a middle point in the amount of references seems natural. What happen if we maintain a reference to `sqrt(N)`

nodes of the linked list instead of referencing just the first and the last element?

That allows us to have a list of length `sqrt(N)`

such that the distance between each of those nodes in the actual list is `sqrt(N)`

. Having that, we can do each operation (index, insertion, and deletion) in `O(sqrt(N))`

.

If you want to see more details about this structure and a Lisp implementation of it, you can do it here .

## Conclusions

We have seen two examples about combining existing solutions in order to get another one that has some of the good qualities of each of the previous ones. My purpose was to show you this way of thinking, not just the specific examples.

Note that in the case of Dijkstra’s idea, we could achieve the time of the fastest solution and the memory complexity of the naive algorithm. In the second example we just got a middle point, so the fastest solution is still faster and the memory-cheaper solution is still cheaper, but the new structure is like a decathlon athlete, it is good in every specificity, but not the best in anyone. So don’t try to find the silver bullet, remember the no free lunch. Even the Dijkstra’s idea has the disadvantage of being harder to implement and to understand.

Hope you have learned or found attractive this post. Feel free to ask whatever you want or give me some feedback, I will appreciate that a lot. Stay tuned for the next post!