Applied Linear Algebra - Olver, Peter J.; Shakiban, Chehrzad (Springer, 2018)
Applied mathematics rests on two central pillars: calculus and linear algebra. While calculus has its roots in the universal laws of Newtonian physics, linear algebra arises from a much more mundane issue: the need to solve simple systems of linear algebraic equations. Despite its humble origins, linear algebra ends up playing a comparably profound role in both applied and theoretical mathematics, as well as in all of science and engineering, including computer science, data analysis and machine learning, imaging and signal processing, probability and statistics, economics, numerical analysis, mathematical biology, and many other disciplines. Nowadays, a proper grounding in both calculus and linear algebra is an essential prerequisite for a successful career in science, technology, engineering, statistics, data science, and, of course, mathematics. Since Newton, and, to an even greater extent following Einstein, modern science has been confronted with the inherent nonlinearity of the macroscopic universe. But most of our insight and progress is based on linear approximations. Moreover, at the atomic level, quantum mechanics remains an inherently linear theory. (The complete reconciliation of linear quantum theory with the nonlinear relativistic universe remains the holy grail of modern physics.) Only with the advent of large-scale computers have we been able to begin to investigate the full complexity of natural phenomena. But computers rely on numerical algorithms, and these in turn require manipulating and solving systems of algebraic equations. Now, rather than just a handful of equations, we may be confronted by gigantic systems containing thousands (or even millions) of unknowns. Without the discipline of linear algebra to formulate systematic, efficient solution algorithms, as well as the consequent insight into how to proceed when the numerical solution is insufficiently accurate, we would be unable to make progress in the linear regime, let alone make sense of the truly nonlinear physical universe. Linear algebra can thus be viewed as the mathematical apparatus needed to solve potentially huge linear systems, to understand their underlying structure, and to apply what is learned in other contexts. The term “linear” is the key, and, in fact, it refers not just to linear algebraic equations, but also to linear differential equations, both ordinary and partial, linear boundary value problems, linear integral equations, linear iterative systems, linear control systems, and so on. It is a profound truth that, while outwardly different, all linear systems are remarkably similar at their core. Basic mathematical principles such as linear superposition, the interplay between homogeneous and inhomogeneous systems, the Fredholm alternative characterizing solvability, orthogonality, positive definiteness and minimization principles, eigenvalues and singular values, and linear iteration, to name but a few, reoccur in surprisingly many ostensibly unrelated contexts.