Solving Systems of Linear Equations with R and C++

Solving systems of linear equations is a fundamental requirement in data science, engineering, and quantitative finance. To build efficient applications, you need to understand both the mathematical principles and how to implement them in high-performance languages. This text-based course bridges the gap between mathematical theory and computational execution using R and C++. You will start with foundational linear algebra concepts, defining matrix operations and understanding why computational efficiency matters. From there, you will progress from standard analytical methods to advanced numerical libraries, learning how to write clean, optimized code for real-world applications. What you'll learn: - Understand the core mathematical concepts of linear systems, matrix inversion, and Gaussian elimination - Implement basic linear solvers from scratch in R to build your foundational intuition - Integrate C++ with R using RcppArmadillo to accelerate computationally heavy matrix operations - Utilize the Eigen library in C++ for highly optimized, modern numerical linear algebra workflows - Analyze and compare the performance and computational complexity of different solving methods - Apply modern coding practices, including clean code structure and type safety, to mathematical programming This course is structured to build your confidence step by step. You will first master the basic terminology and mathematical foundations before writing your first lines of solver code, ensuring you never feel lost as the complexity increases. This course is designed for beginners in numerical computing, data analysts looking to speed up their workflows, and programmers eager to learn how R and C++ work together. No prior experience with advanced numerical libraries is required. Start reading today to master the computational tools behind modern engineering and data analysis.