bit is a header-only C++ library for numerical work in bit-space, which mathematicians call GF2. This is the simplest Galois field with just two elements, 0 and 1. All arithmetic operations in bit-space are mod 2, so what starts in bit-space stays in bit-space.
The library provides vector and matrix classes for performing linear algebra in bit-space. The bit::vector class represents bit_vectors, and the bit::matrix class represents bit-matrices. The library also has a bit::polynomial class to represent bit-polynomials over GF(2).
These classes are efficient and pack the individual bit elements into natural word blocks. You can size/resize the classes at run-time.
Because arithmetic operations in GF(2) are mod 2, addition/subtraction becomes the XOR operation, and multiplication/division becomes the AND operation. The bit library uses those equivalences to efficiently perform most interactions on and between bit-vectors and bit-matrices by simultaneously working on whole blocks of elements.
The bit library provides a rich interface to set up and manipulate bit-vectors and bit-matrices in various ways. Amongst other things, the interface includes methods to solve systems of linear equations over GF(2) and to look at the eigen-structure of bit-matrices.
The bit::polynomial class has methods to compute
Here is a simple example of a program that uses bit:
#include <bit/bit.h>
int main()
{
auto M = bit::matrix<>::random(6, 6);
auto c = bit::characteristic_polynomial(M);
std::cout << "The bit-matrix M:\n" << M << "\n";
std::cout << "has characteristic polynomial c(x) = " << c << ".\n";
std::cout << "The polynomial sum c(M) gives:\n";
std::cout << c(M) << "\n";
}This program creates a random 6 x 6 bit-matrix M where 0 & 1 are equally likely to occur and then extracts its characteristic polynomial M satisfies its characteristic equation as expected from the Cayley-Hamilton theorem.
Here is the output from one run of the program:
The bit-matrix M:
│0 1 1 0 0 0│
│0 0 1 0 1 0│
│1 1 0 0 0 1│
│0 0 0 0 0 1│
│0 1 0 0 1 1│
│1 1 0 1 0 1│
has characteristic polynomial c(x) = x^1 + x^4 + x^6.
The polynomial sum c(M) gives:
│0 0 0 0 0 0│
│0 0 0 0 0 0│
│0 0 0 0 0 0│
│0 0 0 0 0 0│
│0 0 0 0 0 0│
│0 0 0 0 0 0│NOTE: bit makes it possible to quickly extract the characteristic polynomial for a bit-matrix with millions of elements. This problem chokes a naive implementation that needs to consider the unique nature of arithmetic in GF(2).
This library is header-only, so there is nothing to compile and link—drop the bit include directory somewhere convenient, and you're good to go.
Alternatively, if you are using CMake, you can use the standard FetchContent module by adding a few lines to your project's CMakeLists.txt file:
include(FetchContent)
FetchContent_Declare(bit URL https://github.com/nessan/bit/releases/download/current/bit.zip)
FetchContent_MakeAvailable(bit)This command downloads and unpacks an archive of the current version of the bit library to your project's build folder. You can then add a dependency on bit::bit, a CMake alias for bit. FetchContent will automatically ensure the build system knows where to find the downloaded header files and any needed compiler flags.
Used like this, FetchContent will only download a minimal library version without any redundant test code, sample programs, documentation files, etc.
The standard library already has std::bitset, an efficient bitset class that is familiar and well thought through, so our bit::vector class replicates and extends much of that interface.
All std::bitset objects have a fixed size determined at compile time. The well-known Boost library adds a dynamic version boost::dynamic_bitset, where the bitset size can be set and changed at runtime.
However, as the two names suggest, those types are aimed at bitsets instead of bit-vectors. So, for example, they print in bit-order with the least significant element/bit on the right. More importantly, those classes don't have any particular methods aimed at linear algebra, and neither does the standard library's vector class std::vector.
On the other hand, several well-known linear algebra libraries, such as Eigen, exist. Those packages efficiently manage all the standard numeric types (floats, doubles, integers, etc.) but do not correctly handle GF(2). You can create matrices of integers where all the elements are 0 or 1, but there is no built-in knowledge in those libraries that arithmetic is mod 2.
For example, you might use Eigen to create an integer matrix of all 0's and 1's and then use a built-in function from that library to extract the characteristic polynomial. Modding the coefficients of that polynomial with 2 gets the appropriate version for GF(2). Technically, this works, but you will have overflow problems for even relatively modest-sized matrices with just a few hundred rows and columns. Of course, you might use an underlying BitInt type that never overflows, but the calculations become dog slow for larger bit-matrices, which doesn't help much.
This specialised bit library is better for linear algebra problems over GF(2). Consider it if, for example, your interest is in cryptography or random number generation.
You can read the project's documentation here. The documentation site was generated using Quarto.
You can contact me by email here.
Copyright (c) 2022-present Nessan Fitzmaurice. You can use this software under the MIT license.