# Computational Number Theory

by Stephen M. Walker II, Co-Founder / CEO

## What is Computational Number Theory?

Computational number theory, also known as algorithmic number theory, is a branch of mathematics and computer science that focuses on the use of computational methods to investigate and solve problems in number theory. This includes algorithms for primality testing, integer factorization, finding solutions to Diophantine equations, and explicit methods in arithmetic geometry.

Computational number theory is heavily used in cryptography, including RSA, elliptic curve cryptography, and post-quantum cryptography. It's also used to investigate speculations, notions, and open problems in number theory.

The field has seen significant progress in recent years, both in terms of improved computer speed and in terms of finding more efficient algorithms. For example, Agrawal, Saxena, and Kayal found a polynomial time algorithm for testing and proving the primality of general numbers. Although this algorithm is still impractical, it was a landmark discovery, since polynomial time algorithms are considered easy.

Current research topics in computational number theory include faster primality testing and calculations in and regarding number fields of a degree greater than 2. There's also a strong and constructive interplay between computation, heuristic reasoning, and conjecture in this field.

In the context of AI, computational number theory is defined as the utilization of numerical algorithms to solve complex computational problems. It's also used in various applications in industry or technology.

In terms of learning resources, there are several books available such as "A Course In Computational Algebraic Number Theory" by Henri Cohen and "A Computational Introduction to Number Theory and Algebra".

## What are some examples of problems in number theory that can be solved using computational methods?

Computational number theory, or algorithmic number theory, uses computational methods to solve problems in number theory. Here are some examples of problems in number theory that can be solved using computational methods:

1. Primality Testing — This involves determining whether a given number is prime. Various algorithms have been developed for this purpose, including the AKS primality test, which can determine the primality of a number in polynomial time.

2. Integer Factorization — This is the decomposition of a composite number into a product of smaller integers, which when multiplied together give the original number. The most famous algorithm for this is the RSA algorithm, used in cryptography.

3. Solving Diophantine Equations — These are polynomial equations that seek integer solutions. Computational methods can be used to find such solutions, if they exist.

4. Computing the Greatest Common Divisor (GCD) — The GCD of two or more integers is the largest positive integer that divides each of the integers without a remainder. The Euclidean algorithm is a well-known computational method for finding the GCD.

5. Calculations in Number Fields — Current work in computational algebraic number theory involves calculations in and regarding number fields of a degree greater than 2.

6. Investigating Conjectures and Open Problems — Computational methods are used to investigate conjectures and open problems in number theory, including the Riemann hypothesis, the Birch and Swinnerton-Dyer conjecture, the ABC conjecture, the modularity conjecture, the Sato-Tate conjecture, and explicit aspects of the Langlands program.

7. Computing Class Numbers and Class Groups — These are important concepts in algebraic number theory, and computational methods can be used to calculate them.

8. Distribution of Primes — Computational methods can be used to investigate the distribution of prime numbers, a key topic in analytic number theory.

These examples illustrate the wide range of problems in number theory that can be tackled using computational methods. The choice of method often depends on the specific problem and the computational resources available.

## What is the difference between deterministic and non-deterministic algorithms?

Deterministic and non-deterministic algorithms are two fundamental concepts in computer science that describe the predictability and behavior of algorithms. Deterministic algorithms consistently yield the same result for any given input, ensuring predictability in decision-making and optimization tasks. Conversely, non-deterministic algorithms embrace variability, allowing for extensive exploration in search problems to identify satisfactory solutions. The selection between these algorithm types hinges on the problem's nature, with deterministic algorithms being pivotal for precision and reliability, while non-deterministic algorithms are advantageous when a broad search is essential.

### Deterministic Algorithms

A deterministic algorithm is one that, given a specific input and initial conditions, will always produce the same output and follow the same sequence of steps. This predictability comes from the fact that the algorithm operates without randomness, ensuring that the final result is consistent and replicable for the same input. Deterministic algorithms are easier to design, analyze, and debug due to their predictable behavior. They are commonly used in applications where precision is critical, such as cryptography, numerical analysis, and computer graphics. Examples include sorting algorithms like bubble sort and numerical algorithms.

### Non-deterministic Algorithms

Non-deterministic algorithms, on the other hand, can exhibit different behaviors on different runs even with the same input. This variability is due to the introduction of randomness or the presence of multiple potential execution paths. Non-deterministic algorithms are often used to find approximate solutions to problems where an exact solution would be too costly or difficult to obtain using a deterministic approach. They are particularly useful in fields like artificial intelligence, machine learning, and optimization problems. An example of a non-deterministic algorithm is a probabilistic algorithm like Monte Carlo methods.

### Key Differences

• Predictability — Deterministic algorithms guarantee the same output for a given input, while non-deterministic algorithms may produce different outputs for the same input.
• Design and Analysis — Deterministic algorithms are generally easier to design and analyze because of their predictable nature, whereas non-deterministic algorithms require probabilistic analysis and can be more complex to understand.
• Applications — Deterministic algorithms are preferred in scenarios requiring high precision and reliability, while non-deterministic algorithms are suitable for problems where exploring a wide solution space is beneficial.
• Behavior — In deterministic algorithms, the machine follows a single path from input to output. In contrast, a non-deterministic algorithm can take many paths, some leading to the same output and others to different outputs.

## How are large numbers computed efficiently in AI?

Large numbers can be computed efficiently in AI using algorithms that take advantage of the properties of binary representation and bitwise operations. One such algorithm is the Binary Exponentiation, which computes x^n efficiently by breaking down the exponent n into its binary representation and performing a series of squaring and multiplication operations. Another approach is to use logarithms, where the product of two numbers can be computed as the sum of their logarithms, and the power of a number can be computed as the product of that number and its logarithm. These methods allow for efficient computation of large numbers in AI applications.

## What are the strategies for factoring large numbers in AI?

Factoring large numbers is an important problem in cryptography, where it is used to break encryption algorithms. In AI, there are several strategies that can be used to factor large numbers efficiently. One such strategy is Pollard's rho algorithm, which uses random walks and hash functions to find factors of large numbers. Another approach is the quadratic sieve method, which uses a combination of trial division and modular arithmetic to find factors of large numbers. These methods allow for efficient factoring of large numbers in AI applications.

## How is modular arithmetic computed in AI?

Modular arithmetic is a type of arithmetic that involves performing operations on numbers within a certain range, called the modulus. In AI, modular arithmetic can be computed efficiently using bitwise operations and tables of precomputed values. For example, to compute the remainder of a division operation, one can use the modulo operator (%) or perform bitwise AND operation with the modulus value. To compute the multiplicative inverse of a number within a certain range, one can use the extended Euclidean algorithm or tables of precomputed values. These methods allow for efficient computation of modular arithmetic in AI applications.

## What is the most efficient way to compute the greatest common divisor of two numbers in AI?

The most efficient way to compute the greatest common divisor (GCD) of two numbers in AI is using the Euclidean algorithm. This algorithm uses a series of subtraction and modulo operations to find the GCD of two numbers, and it has a time complexity of O(log n), where n is the larger of the two input numbers. Another approach is to use binary GCD algorithm, which takes advantage of the properties of binary representation and bitwise operations to compute the GCD efficiently. These methods allow for efficient computation of the GCD in AI applications.

## How is the least common multiple of two numbers computed in AI?

The least common multiple (LCM) of two numbers can be computed efficiently in AI using the properties of prime factorization. One approach is to first compute the prime factorization of each number, and then multiply all the unique prime factors together. Another approach is to use the Euclidean algorithm to find the GCD of the two numbers, and then compute the LCM as the product of the two numbers divided by their GCD. These methods allow for efficient computation of the LCM in AI applications.

## More terms

### Supervised fine-tuning (SFT)

Supervised fine-tuning (SFT) is a method used in machine learning to improve the performance of a pre-trained model. The model is initially trained on a large dataset, then fine-tuned on a smaller, specific dataset. This allows the model to maintain the general knowledge learned from the large dataset while adapting to the specific characteristics of the smaller dataset.

### What is backward chaining?

Backward chaining in AI is a goal-driven, top-down approach to reasoning, where the system starts with a goal or conclusion and works backward to find the necessary conditions and rules that lead to that goal. It is commonly used in expert systems, automated theorem provers, inference engines, proof assistants, and other AI applications that require logical reasoning. The process involves looking for rules that could have resulted in the conclusion and then recursively looking for facts that satisfy these rules until the initial conditions are met. This method typically employs a depth-first search strategy and is often contrasted with forward chaining, which is data-driven and works from the beginning to the end of a logic sequence.