Mathomatic is a portable, general-purpose computer algebra system (CAS) that can solve, differentiate, simplify, combine, and compare algebraic equations, perform standard, complex number, modular, and polynomial arithmetic, etc. It does some calculus and is very easy to compile/install, learn, and use. The symbolic math application with a simple command-line interface is designed to be a colorful algebra calculator that is reliable, responsive, and convenient to use. The symbolic math library is lightweight and easy to include in other software, due to being written entirely in C with no additional dependencies.
Botan is a crypto library written in C++. It provides a variety of cryptographic algorithms, including common ones such as AES, MD5, SHA, HMAC, RSA, Diffie-Hellman, DSA, and ECDSA, as well as many others that are more obscure or specialized. It also offers SSL/TLS (client and server), X.509v3 certificates and CRLs, and PKCS #10 certificate requests. A message processing system that uses a filter/pipeline metaphor allows for many common cryptographic tasks to be completed with just a few lines of code. Assembly and SIMD optimizations for common CPUs offers speedups for critical algorithms like AES and SHA-1.
SCaVis is an environment for scientific computation, data analysis, and data visualization designed for scientists, engineers, and students. The program can be used for function and data plotting in 2D and 3D, histograms, statistical analysis, and symbolic calculations using the Matlab/Octave high-level interpreted language.
libefgy is a set of C++ headers containing lots of templates loosely related to maths. The headers include templates for fractional arithmetic, big integers (and thus "big fractions"), calculating π, e, and some calculations with those (for trigonometrics), matrix manipulations, tuples, polar and Euclidian spaces in arbitrary dimensions, (perspective) projections, colour space manipulations in RGB and HSL, and assorted other things.
The Shared Scientific Toolbox is a library that facilitates development of efficient, modular, and robust scientific/distributed computing applications in Java. It features multidimensional arrays with extensive linear algebra and FFT support, an asynchronous, scalable networking layer, and advanced class loading, message passing, and statistics packages.
librsb is a library for sparse matrix computations featuring the Recursive Sparse Blocks (RSB) matrix format. This format allows cache-efficient and multithreaded (that is, shared memory parallel) operations on large sparse matrices. The most common operations necessary to iterative solvers are available (matrix-vector multiplication, triangular solution, rows/columns scaling, diagonal extraction/setting, blocks extraction, norm computation, formats conversion). The RSB format is especially well-suited for symmetric and transposed multiplication variants. On these variants, librsb has been found to be faster than Intel MKL's implementation for CSR. Most numerical kernels code is auto-generated, and the supported numerical types can be chosen by the user at buildtime. librsb implements the Sparse BLAS standard, as specified in the BLAS Forum documents.
SLEEF (SIMD Library for Evaluating Elementary Functions) is a library that facilitates programming with SIMD instructions. It implements the trigonometric functions, inverse trigonometric functions, exponential and logarithmic functions in double precision without table look-ups, scattering from, or gathering into SIMD registers, or conditional branches. This library also includes some functions for evaluation in single precision.
MLPACK is a C++ machine learning library with an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. It contains algorithms such as k-means, Gaussian mixture models, hidden Markov models, density estimation trees, kernel PCA, locality-sensitive hashing, sparse coding, linear regression and least-angle regression.