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The 35 pages that link to Matrix multiplication, each with the reason it gives.

• Big O notationRelated: Standard and faster methods have distinct asymptotic time bounds.

• Function compositionRelated: Matrix multiplication encodes composition of linear maps in chosen bases.

• Rotation matrixRelated: Multiplying rotation matrices combines rotations, generally in an order-sensitive way.

• Linear combinationRelated: A matrix-vector product forms linear combinations of the matrix's columns.

• Identity matrixRelated: The identity matrix leaves a compatible matrix unchanged under this operation.

• Tensor productRelated: Matrix multiplication can be represented using tensor contraction and products of component spaces.

• Time complexityRelated: Its algorithms illustrate how asymptotic improvements can change large-scale computation.

• Distributive propertyRelated: It obeys distribution even though matrix multiplication is generally not commutative.

• Linear mapRelated: Multiplying representing matrices computes the composition of linear maps.

• Matrix exponentialRelated: Matrix powers in the defining series use repeated multiplication, which is generally noncommutative.

• AssociativityRelated: Matrix products are associative, so products can be regrouped without changing their value.

• LU decompositionRelated: The factors reconstruct the original matrix through matrix multiplication.

• Square matrixRelated: A square matrix can be multiplied by itself, enabling powers and polynomial expressions.

• Kronecker deltaRelated: The identity entries given by the delta leave vectors and matrices unchanged under multiplication.

• ParenthesesRelated: Parentheses distinguish matrix factors and make the intended product grouping explicit.

• Permutation matrixRelated: Left and right multiplication apply the encoded rearrangement to rows or columns.

• Diagonal matrixRelated: Multiplying by a diagonal matrix independently scales the coordinates of a vector.

• Floating-point unitRelated: Specialized floating-point units accelerate this core workload in machine learning and simulation.

• Sherman–Morrison formulaRelated: Direct verification expands the product of the updated matrix and its proposed inverse.

• Woodbury matrix identityRelated: Compatible dimensions make the low-rank factors and reduced inverse fit together.

• Optical computingRelated: Optical transformations can implement large matrix operations central to machine learning.