VectK

VectK is a hypothetical open-source software library designed to provide high-performance vector operations and kernel-based machine learning tools. It targets efficient vector and matrix algebra, scalable kernel computations, and seamless interoperability across programming languages. The project is described as a toolkit for numerical computing and data analysis, prioritizing speed, portability, and ease of integration across scientific workflows.

Core features include a Vector and Matrix type system with arithmetic, norms, decompositions, and memory-safe abstractions; a library of kernel functions for kernel methods (linear, polynomial, radial basis, and sigmoid); machine learning modules such as support vector machines, kernel ridge regression, and Gaussian process primitives; GPU acceleration via a CUDA backend; language bindings for Python, C++, and Rust; and data interchange with NumPy arrays and JSON.

Architecture is modular, with a core math engine written in optimized C++, backend plug-ins for CPU and GPU execution, and a lightweight scheduler for parallel computations. It provides a clean API designed for integration with existing ML pipelines and external libraries, while remaining portable on Windows, macOS, and Linux.

Development and status: VectK originated in an open-source community of researchers and developers and is distributed under a permissive license to encourage contributions. Documentation, tutorials, and examples are maintained in its official repository. It is aimed at researchers and developers in data science, machine learning, and scientific computing.

See also: kernel methods; vector space; numerical linear algebra libraries.