Matrix Multiplication Mastery
From Naive Algorithms to Cutting-Edge Optimization Techniques
Most programmers implement matrix multiplication the same way they learned it in school—three nested loops with O(n³) time complexity. While this works for small matrices, it becomes a computational bottleneck for large-scale applications in machine learning, scientific computing, and data analysis. The problem isn't that you're doing something wrong; it's that you're missing decades of algorithmic innovation. This book reveals how researchers have systematically reduced matrix multiplication complexity from O(n³) to O(n^2.807) and beyond, and more importantly, how you can apply these techniques to your own work. You'll explore the mathematical principles behind Strassen's algorithm, understand why divide-and-conquer strategies work, and learn how to optimize for real hardware constraints. Whether you're building machine learning systems, solving scientific simulations, or simply want to write faster code, understanding matrix multiplication algorithms gives you powerful tools to dramatically improve performance.
Est. Length
222 pages (10 chapters)
Language
English
Publication date
Nov 25, 2025
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