Chapter 42 — Linear Algebra and Matrix Computations

Efficient matrix and vector operations underpin simulations, ML preprocessing, and scientific computing. This chapter focuses on data layout, cache‑friendly algorithms, and correctness for common linear algebra tasks in pure Java.

What You’ll Learn

• Array layout and memory access patterns for matrices
• BLAS‑like primitives (AXPY, DOT, GEMV) and blocked GEMM (matrix multiply)
• Numerical stability and error analysis basics
• Performance tuning with loop tiling and JMH measurement

Use this chapter to implement predictable, portable numeric kernels without external dependencies.