This is a comprehensive Java library for numerical computing, linear algebra, optimization, machine learning, and quantitative finance. The codebase is pure Java with optional native BLAS/LAPACK acceleration via Java 22+ Foreign Function & Memory API. It provides high-performance dense/sparse matrix storage and decompositions (LU, QR, SVD, Eigenvalue, Cholesky), a full-featured optimization suite (LP, QP, MIP with branch-and-bound and cut generation), artificial neural networks (feedforward, backpropagation), clustering (KMeans, spectral), time series analysis, statistical distributions, and financial portfolio modeling (Markowiz, Black-Litterman). The architecture is modular with clean interfaces (Access1D, Mutate2D, FunctionSet) and factory/build patterns. The library is well-tested and actively maintained (0 open issues, recent commits), though some parts show technical drift due to deprecated APIs and reliance on evolving JDK foreign-memory features. Overall, ojAlgo is a strong, production-ready numerical toolkit for Java applications requiring mathematical optimization and linear algebra, with several reusable infrastructure components.