# What We Know We Don’t Know About Algorithms (part 2)

Computer scientists quickly realized that some problems are difficult to solve, no matter the technological advancements of computing power.

written by Mihai Serban (Java Software Engineer), in the April 2023 issue of Today Software Magazine.

Read the first part of the article here

## Classification: by Complexity Classes

Computer scientists quickly realized that some problems are difficult to solve, no matter the technological advancements of computing power.

In the analysis of algorithms, Big O notation has become the most used standard of measuring complexity and was introduced in the 70’s by Donald Knuth, the author of the famous book “The Art of Computer Programming”.

In general, Big O notation measures the worst-case scenario for how run time (or memory consumption) increases with regards to the input size. For instance, the complexity of the GCD algorithm is O(log(min(a, b)).

By comparison, searching into an array with “n” elements has a complexity O(n), as the number of steps (for worst case scenario) grows proportionally with the size of the array. However, retrieving an element of the array has a complexity of O(1) (assuming we know the index of the element we are searching for).

“Hello world” programs have a constant complexity O(1), and Divide et Impera algorithms can reduce linear complexity O(n) to logarithmical complexity O(log n), as not all sub-problems will be evaluated.

Quadratic time complexity algorithms (O(n²)) can sometimes be optimised with better algorithms or more suitable data structures.

Whenever you see a nested for loop, pay careful attention to the size of n and investigate what you can do to improve it: decrease the n, change algorithm, add bounds to input, etc., as run time can grow quite fast.

If the following test runs in ~2 minutes for n = 100 000, for n = 1 000 000 it will run 1⁰² = 100 times slower which is > 3 hours, unacceptable for most applications. But as you will see later, it is not polynomial algorithms that keep us up at night. It is exponential ones.

@Test

public void testNestedLoops(){

int n = 100_000;

Random random = new Random();

int z = 0;

for (int i = 0; i < n; i++){

for (int j=0; j<n; j++) {

z = random.nextInt();

}

}

System.out.println(String. valueOf(z));

}

Computational complexity theory tells us what data structures to use for our business requirements if we aim to achieve the best performance. It is your duty to evaluate and understand how the users are consuming the data and what are the most common use cases.

For instance, suppose your users are searching for data very often, the data size is large and searching must be done quickly, while there are fewer constraints on the insertion, or it is not that often that the data changes. In that case, you might use a Binary search tree instead of an array or a linked list, as the search and insert complexity are both O(log n), proportional to the height of the three. If the tree is unbalanced (it can have a height of n), so the complexity in the worst case can still be O(n).

That is why we need to make sure that we balance the three, which involves additional operations to reconstruct sub-trees when necessary. Of course, “links” are references to objects (or memory locations), so these data structures consume more storage than an array.

No matter the data structures we choose, or the hardware we have, some algorithms are simply too difficult to to compute in a reasonable amount of time.

## Classification: Deterministic vs Non-Deterministic

Before going further into algorithms, we are still struggling with, it is worth mentioning the distinction between deterministic and non-deterministic algorithms as well as exact or approximate ones.

Deterministic algorithms always produce the same result when presented with the same input. Non-deterministic algorithms do not. Turing Machines are deterministic, and consequently all our computers are the same.

Well, what if you want to build an online casino? If the cards that your poker algorithm yields to the players are pre-determined, you are doomed to fail. If you happen to be a Java programmer and you cleverly thought about the infamous java.util.Random(), you are sailing in dangerous waters.

To simulate non-deterministic behavior on Turing Machines, Pseudo-Random Number Generators are used, simply called PRNGs. These generators use a seed number as a source, and an equation to generate numbers based on the seed.

Using the same seed produces the same sequence, and eventually the sequence is repeated (hence the “pseudo”). Java Random() uses a linear congruential generator, with an equation that looks like this:

As you can see, the equation uses the modulo operator, which guarantees repetition after at most m numbers, so m must be quite big and in general a prime number is chosen, as primes don’t tend to have a ton of divisors and as such, they are a good choice for a number generator based on modulo operator.

The m used in our java Random () class is the mask field, a special prime number which is less than one from a power of two, which makes it easy to represent through a bitwise operation.

These primes are called Mersenne primes and are useful because they allow much faster bitwise operations for calculating the pseudo-random numbers with the above formula. Don’t you love it when science meets programming?

private static final long mask = (1L << 48) - 1;

If you keep count, you have already noticed that primes are all over the place in computer science. The seed for the java Random() class is computed using the system time, as we can see by diving into the sources:

public Random(){

this (seed: seedUniquifier () ^ System.nanoTime());

}

The seed “uniquifier” is just a large number that is updated at each invocation of the constructor:

private static final AtomicLong seedUniquifier

= new AtomicLong (initialValue: 8682522807148012L);

Algorithms that use PRNGs are still deterministic, but they can generate numbers which statistically have similar properties to the true random numbers.

@Test

public void testPseudoRandom(){

Random prng1 - new Random (seed: 1 );

System.out.println(prng1.nextInt());

Random prng2 - new Random (seed: 1 );

System.out.println(prng2.nextInt());

// output:

// -1155869325

// -1155869325

}

If you wish to implement a fair online poker app, you need true randomness, which is hard to get. You might want to use the API of random.org, or, if you get successful and external services will get too expensive, you might want to rely on hardware that generates random numbers using the thermal noise of the circuits, or even better: quantum mechanics effects of small particles, which are the most random things we currently know of.

However, if your app becomes extremely popular, you might find your true random generation of numbers is quite slow.

Your only solution might be to combine a true random source with a pseudo-random generator that uses as a seed the true randomly generated numbers. Moreover, true RNGs usually feature asymmetries that make their outcomes not uniformly random, and you also need to apply a hash function on the randomly generated numbers.

Currently used algorithms for computing a hash function collectively named SHA-2 are using block cyphers, which are also deterministic algorithms whose results appear random. The block cypher is carefully created to mix, rotate, and shift the bits in the input to produce an output “random” enough that it is impractical to reverse the operation and inside the blocks are, you guessed it: prime numbers. As you know, hash functions are now used everywhere.

There is a lot we do not know about non-deterministic algorithms, as we are still trying to understand deterministic ones. Luckily, all our computers are now deterministic (except for quantum computers).

For a lot of practical applications, PRNGs algorithms can be used, with the advantages of speed, cost, and ease of integration. This application includes approximation algorithms and techniques, software testing, exploration vs exploitation algorithms, etc.

For some applications like cryptography, PRNGs are not consider suitable, despite continuous efforts to create cryptographically secure pseudo-random number generators.

## Classification: Exact, Approximate, Heuristic and Probabilistic Algorithms

Most of the algorithms we work with in our daily work can be solved in polynomial time: searching an item in the database, sorting a list of elements, performing a payment transaction, or retrieving all restaurants in your area. This class of problems is called P. A more formal definition of P is the set of all decision problems that can be solved by a Deterministic Turing Machine in polynomial time.

Some of these problems are indeed hard, as the degree of the polynomial can be large, or the input size can be large, and we usually can optimise them using the graph theory methods (like sorted binary trees), but in general we prefer to and can find the optimal solution for them. The algorithm is called “exact” if we compute the optimal solution.”

For some problems, however, an exact algorithm is not feasible, as no algorithm is known to find the optimal solution in a reasonable amount of time.

Some problems in P fall into this category, but in general these are problems for which known exact algorithms have a complexity above polynomial: exponential or factorial, both beyond our existing Turing Machines can compute in a reasonable time even for moderate input sizes.

As this is a problem we still must solve, the approach is to find polynomial algorithms that can compute approximate solutions or “good enough” solutions to work with using heuristics based on intuition or having been observed to work in nature.

As all these problems are solvable by brute force, but the run time or computing resources are impractical, we call them intractable problems. Not to be confused with unsolvable problems like the Halting problem.

An important distinction between heuristic and approximation approaches is that an approximation algorithm is provable at a bounded distance from the optimal solution, usually expressed in percentages, while for the heuristics approach, we do not have a measure of how good the algorithm is, and we use it because it works. I know, it does not sound like computer “science”, but it is.

Probabilistic algorithms, also called randomized algorithms, are used both in approximation and heuristics techniques.

We use probabilistic algorithms because they are simple, and because large samples of randomized data (or data generated according to a probability distribution that represents the data) can get asymptotically closer to the optimal solution given enough simple computations are executed. Usually, PRNGs are suitable for the task, so there is no need for true random generators.

## Monte Carlo Algorithms

Randomized algorithms have a significant role in physics-related problems or in mathematics, where we deal with complex formulas and with inputs whose probability distribution is known. This class of algorithms is known as Monte Carlo methods, and the first practical application was the evaluation of neutron diffusion in the core of a nuclear weapon, at Los Alamos National Laboratory, in 1946. Deterministic approaches fail due to the amount of computation needed.

In 1948, Monte Carlo simulations for nuclear reactions were being programmed on ENIAC, the first programmable computer ever built. Monte Carlo algorithms work because of the law of large numbers, which states that the more random trials we perform, the closer we get to the expected value of a function.

As the work on nuclear fission projects was classified, this research project was named Monte Carlo, as Stanislaw Ulam’s uncle, one of the researchers involved in the project, would borrow money from relatives to gamble at Monte Carlo Casino in Monaco.

A good example of how the method works for pi approximation, as seen in the following images.

Let us illustrate the point with an algorithm you are most certainly used to.

## Asymmetric Cryptography

Having now a brief introduction to the history of algorithms and a few ways we can classify them, let us bridge the gap between theory and practice and put the pieces together into one of the most used algorithms today. Let us talk about asymmetric cryptography.

Asymmetric cryptography, also known as public key cryptography, allows secure communication between two (or multiple) parties while allowing the decryption of the message only by its intended receiver.

This is an extreme improvement over symmetric key cryptography, where the secret (password, key) had to be shared between the two parties, which meant that both parties could decrypt the messages, and each of them could pose as the other one.

The first and best-known algorithm for public key cryptography is RSA, published in ’77 by Ron Rivest, Adi Shamir, and Leonard Adleman. It is still the most used algorithm, as it has not yet been broken. You have used it many times (your browser had), whenever you sent data to a website with TLS encryption (over HTTPS).

As a developer, you have generated some keys yourself. With putty, like in the image below, for SSH authentication, or self-signed SSL certificates and wondering how it works, and first, why do you have to frenetically move your mouse over the gray area.

What is happening here? If we allow ourselves to simplify a bit, we generate a public key and a private key. These two keys have to be related, so that a message encrypted with the public key can be decoded with the private key, but the private key cannot be deduced from the public key.

Encryption — decryption formula in the image below relies on the difficulty of finding the number d (which is the private key) while knowing n and e (public key), as n is a product of two prime numbers, let’s call them p and q (n = pq).

The private key, d, can be computed from p and q, but to find p and q, we need to decompose n into its prime factors, which is an exponential complexity algorithm (the naïve approach, at least).

For large enough n’s, it is highly unpractical. Current n values have 2048 bits. It seems we are back to Euclid, closing the cycle, and still discussing primes.

If you have followed the discussion this far, you might have wondered: “Ok, but how do we find such huge prime numbers like p and q, to begin with? Isn’t this a problem almost as hard to begin with?” And you would be right.

One thing that makes prime numbers special is that we know little about their distribution. We know there’s an infinite amount of them since Euclid, but we still don’t know how to find them efficiently.

Except for the good old way that you’ve programmed in high school: take each number (maybe skip the even ones), check if it is prime by dividing them to all primes smaller than itself (sqrt(n) would suffice as your teacher told you)). If there’s no match, you find a prime.

So, finding two large prime numbers is a tedious job. And obviously, we can’t use existing ones, we need new ones for each public-private key pair. This leaves us with one tradeoff option: pick two numbers at “random” and hope they are prime. Now you know enough about randomness and computers to understand why we have to move the mouse to the top of the gray area.

We need a “random” seed for a pseudo-random number generator used to guess the prime numbers we are searching for, p and q. That “random” is you, if you happen to believe in free will. And even if you don’t, your hand movement is still less predictable than your operating system clock.

The "guess" in the equation is the Miller-Rabin primality test which is an efficient probabilistic and approximate algorithm. We can increase the probability of the number being tested to be prime by running the algorithm longer, and the currently used version of it was discovered in 1980.

This is yet another spooky method that seems to work well, we based our entire industry and security on top of it, but the proof that the Miller primality test works relies on the unproven Riemann hypothesis, probably the most famous unresolved problems in mathematics, of which I’m not smart enough to talk about.

As this primality test is fast, we can quickly find two numbers that might be primes with a high enough confidence to used them in computation of the public and private keys.

## Hardest Problem(s) in Computer Science

Amongst decision problems, some problems are unsolvable (undecidable), like the halting problem. And some problems are solvable, and we know polynomial algorithms to solve them. The class of these problems is called P. There are problems for which we can verify solutions quite easily (in polynomial time). The class for these problems is called NP. For example, integer factorization, the base for our cryptography, is an NP problem.

If we know the prime factors, we can quickly verify if they multiply to the result. They are named NP because all these problems can be solved in polynomial time by a Nondeterministic Turing Machine, which is a special theoretical Turing machine that can check all possible branches of an algorithm, or “guess” solutions.

Obviously, P ⊂ NP. There are some decision problems however to which we can reduce any other NP problem, “root” problems if you like. These are called NP-Complete problems, and they can simulate, or solve any other problem in NP. We know quite a few of these and adding a few more to this class will bring you fame, glory and citations.

And there are hard problems, which are called Hard. NP-Hard. They are at least as hard as NP-Complete, and all the problems in NP can be reduced to them in polynomial time. NP-Complete problems are a subset of NP-Hard, but some NP-Hard problems are not even in NP, or they are not decision problems.

We believe no NP-Hard problem (or NP-Complete one by inclusion) can be solved in polynomial time, as no algorithms were discovered to solve them efficiently. However, this has not yet been proven.

This is the most challenging problem in computer science, and it’s called P ≠ NP. Solving this comes with eternal fame and 1 million dollars. If P were to equal NP, a lot of problems we thought were hard could be solved with a polynomial algorithm (P), meaning there are much more efficient ways to solve NP-Complete problems (and some NP-Hard ones).

The most famous NP-Complete problem is the Traveling Salesman Problem.

It has a factorial complexity, so solving it by brute force, even for a modest input size, is unpractical. That is why heuristic approaches are used instead. Most of these heuristics are random algorithms discussed above. For this problem, simulated annealing does a great job, but it’s impossible to tell how good of a job it does.

Another heuristic approach can be ant colony optimisation in the swarm intelligence class algorithms. Less computational ones (and also less efficient) are greedy algorithms, in which you search local optima step by step.

## Brain Teasers for You in the Form of NP-Hard Problems:

You work at the University. The new academic year starts soon, and you have to assign classes to time slots so that there are no conflicts and classes for each group are as closely batched together as possible.

You decided (or most likely finally found someone to marry to), and you need to plan your wedding. However, you know that some of your friends don’t like each other, and some do. Any table can have a configurable number of seats. Place your guests at the table to maximize happiness.

Test the 6 degrees hypothesis.

Knapsack problem: suggest articles to add to a kart (or a recommendation page) to maximize user preference match up to a certain total amount.

## Machine Learning Algorithms

Sometimes, our problems do not involve decisions, but we have to predict, model, or understand. For this type of problem, Machine Learning (ML) algorithms are the choice.

It has been mathematically proven that ML algorithms as universal function approximators, so they can model anything that can be expressed as a function, provided enough neurons are defined. Deep Learning is our best approach to problems where we have a lot of data, no matter how incomplete, and the output looks random.

ML will find the patterns that yield the result, and possibly even more patterns (see overfitting). Pattern matching is not the hammer solution for all your nail problems, as beautifully illustrated in the following example. It has been conjectured that:

The smallest n for which the equation doesn’t hold is n=8424432925592889329288197322308900672459420460792433. Sometimes we just need better math.

## Conclusion

I find it fascinating that our entire IT domain is based on assumption that some mathematical hypothesis might hold true, while some of the most successful algorithms we know are based on heuristics we have yet to learn how to measure. Still, with our poorly understood algorithms underneath, IT looks so smooth on the outside. So mature and scientific, yet so incomplete.