Linear algebra is extensively used in computer graphics for solving systems of linear equations, performing matrix transformations, and calculating eigenvalues and eigenvectors, which are essential for tasks like 3D modeling, rendering, and animation.

The Leontief input-output model uses systems of linear equations to represent the interdependence of different sectors in an economy. It helps analyze the flow of goods and services between industries and determine the impact of changes in one sector on the rest of the economy.

Linear algebra is used in physics to solve Schrödinger's equation in quantum mechanics, calculate electromagnetic fields in electromagnetism, and analyze mechanical systems in classical mechanics.

Linear algebra is used in signal processing to decompose signals into linear combinations of basis vectors, which allows for efficient analysis, filtering, and compression of signals.

The singular value decomposition (SVD) is a powerful tool in statistics used for dimensionality reduction, matrix factorization, and calculating eigenvalues and eigenvectors. It has applications in data analysis, machine learning, and image processing.

Linear algebra is used in finance for portfolio optimization, which involves finding the optimal allocation of assets in a portfolio to maximize returns and minimize risk.

Linear algebra is used in cryptography for both creating encryption algorithms and breaking encryption algorithms. It provides the mathematical foundation for techniques like public-key cryptography and matrix-based ciphers.

Linear algebra is used in social network analysis to represent social networks as matrices, calculate centrality measures for nodes, identify communities and clusters, and perform various other analyses on social network data.

The state-space representation of a system in control theory is based on systems of linear equations that describe the system's dynamics. It allows for analysis and design of control systems using linear algebra techniques.

Linear algebra is used in operations research for solving linear programming problems, which involve optimizing a linear objective function subject to linear constraints. It provides the mathematical foundation for techniques like the simplex method.

Linear algebra is the mathematical language of quantum mechanics. It is used to describe quantum states, calculate probabilities of quantum events, represent quantum operators, and perform various other calculations related to quantum systems.

Linear algebra is used in biology for modeling population dynamics, analyzing gene expression data, studying protein structures, and various other applications. It provides a mathematical framework for understanding and analyzing biological systems.

Linear algebra is extensively used in machine learning for solving optimization problems, representing data in vector spaces, calculating distances and similarities, and performing various other operations essential for machine learning algorithms.

Linear algebra is used in robotics for calculating forward and inverse kinematics, controlling robot movement, planning robot trajectories, and various other tasks related to robot motion and control.

Linear algebra is used in electrical engineering for solving systems of linear equations, performing matrix transformations, calculating eigenvalues and eigenvectors, and various other operations related to circuit analysis and design.