Linked from

The 38 pages that link to Linear algebra, each with the reason it gives.

• Computer graphicsRelated: Matrices express the rotations, scaling, and projections used to position visual objects.

• Quantum computingRelated: Quantum states are represented by vectors, while gates act as matrices.

• Field (mathematics)Related: Vector spaces and matrices commonly use field elements as scalars and entries.

• Homogeneous coordinatesRelated: Matrix multiplication supplies the basic operations on homogeneous coordinate tuples.

• Analytic geometryRelated: It extends coordinate geometry to higher dimensions and systematic transformations.

• Mathematical physicsRelated: Quantum states, symmetries, and many physical transformations use vector spaces and linear operators.

• Mathematical economicsRelated: Matrices compactly represent systems of markets, technologies, and strategic interactions.

• Convolutional neural networkRelated: Convolutions and learned transformations are implemented through tensor operations.

• Inverse elementRelated: Invertible matrices reverse linear transformations and solve square linear systems.

• Data scienceRelated: Many data representations and machine-learning algorithms use vectors and matrices.

• Mathematical structureRelated: Vector spaces are structured sets whose operations support broad transferable theorems.

• 3D computer graphicsRelated: Transforms use vectors and matrices to position, rotate, and scale objects.

• Vector processorRelated: Matrix and vector computations are common targets for vector hardware.

• Wassily LeontiefRelated: Matrix methods make Leontief’s system of interindustry equations solvable.

• Computational scienceRelated: Large matrix operations underpin simulations, data analysis, and numerical solvers.

• John Vincent AtanasoffRelated: Solving large systems of linear equations motivated Atanasoff’s search for a faster calculating machine.