This project is a comprehensive library for numerical linear algebra and scientific computing, designed to provide optimized routines for matrix decomposition, statistical modeling, and high-performance data analysis. It serves as both a toolkit for solving complex linear systems and an educational resource for understanding the fundamental algorithms behind matrix factorizations and numerical solvers.
The library distinguishes itself through a focus on randomized numerical linear algebra, utilizing probabilistic algorithms and approximate methods to perform dimensionality reduction and matrix decomposition with increased speed and memory efficiency. By incorporating techniques such as random sketching and truncated singular value decomposition, it enables the processing of large-scale datasets while preserving essential structural properties.
To achieve high performance, the framework employs a variety of computational strategies, including hardware acceleration, parallel processing, and just-in-time compilation. It optimizes numerical stability and execution speed through rigorous floating-point analysis, block-based matrix operations, and memory layout configurations that improve data locality. These capabilities extend to statistical modeling, supporting linear regression solvers, regularization, and automatic differentiation for predictive analysis.
The project is implemented as a collection of Jupyter Notebooks, providing an interactive environment for exploring and executing these numerical procedures.
