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Matrix representation

In mathematics, a matrix representation is a method to encode linear transformations between vector spaces using matrices, relative to chosen bases, thereby translating abstract linear operations into concrete computational forms. For a linear transformation T: U \to V, where U and V are vector spaces over a field with bases B for U and C for V, the matrix representation [T]_C^B is the matrix whose columns are the coordinate vectors of T applied to the basis vectors of B, expressed in the basis C.^[1] This construction preserves the structure of linear operations: addition and scalar multiplication of transformations correspond to the same operations on their matrices, while composition of transformations corresponds to matrix multiplication.^[1] The concept extends naturally to representation theory, where matrix representations describe actions of algebraic structures such as groups or associative algebras on vector spaces. A matrix representation of a group G is a homomorphism \rho: G \to \mathrm{GL}_n(F), mapping each group element to an invertible n \times n matrix over a field F, such that the group operation is preserved via matrix multiplication.^[2] Similarly, for an associative algebra A, a representation is a homomorphism \rho: A \to \mathrm{End}(V) to the endomorphism algebra of a vector space V, which, upon choosing a basis for V, yields matrix entries.^[3] These representations are fundamental for classifying symmetries and solving systems of linear equations derived from algebraic relations, with applications in physics, chemistry, and computer science.^[3] In computing, matrix representations refer to the data structures and storage formats used to implement matrices in software and hardware, enabling efficient operations in fields like computer graphics, numerical simulations, and machine learning. Common approaches include dense storage in two-dimensional arrays using row-major or column-major ordering, as well as specialized formats for sparse matrices to optimize memory and computation.^[4] Key properties include the notions of irreducibility and equivalence: a representation is irreducible if the only invariant subspaces are trivial, and two representations are equivalent if they are related by a change of basis, resulting in similar matrices.^[2] Characters, defined as the traces of the representing matrices, provide invariants that facilitate decomposition into irreducible components.^[3] In finite-dimensional settings over algebraically closed fields like the complex numbers, every representation decomposes into a direct sum of irreducibles, underpinning theorems like Maschke's for group representations.^[3]

Mathematical Foundations

Definition and Linear Transformations

A matrix is defined as a rectangular array of numbers, symbols, or expressions arranged in rows and columns, specifically an m \times n matrix A over a field F (such as the real numbers \mathbb{R}) consists of m rows and n columns with entries a_{ij} \in F for i = 1, \dots, m and j = 1, \dots, n, denoted as A = [a_{ij}].^[5]^[6] Matrices provide a fundamental representation for linear transformations between vector spaces. A linear transformation T: \mathbb{R}^n \to \mathbb{R}^m can be expressed as matrix multiplication T(\mathbf{x}) = A \mathbf{x}, where A is an m \times n matrix and \mathbf{x} is a column vector in \mathbb{R}^n.^[7]^[8] In the standard basis, the columns of A correspond to the images of the standard basis vectors of \mathbb{R}^n under T; that is, the j-th column of A is T(\mathbf{e}_j), where \mathbf{e}_j is the vector with 1 in the j-th position and 0 elsewhere.^[9] This representation ensures dimension compatibility: an m \times n matrix maps vectors from \mathbb{R}^n to \mathbb{R}^m.^[10] A concrete example is the linear transformation representing a counterclockwise rotation by an angle \theta in the plane, given by the $2 \times 2 matrix

\begin{pmatrix} \cos \theta & -\sin \theta \\ \sin \theta & \cos \theta \end{pmatrix},

which maps (\mathbb{R}^2, \mathbb{R}^2) while preserving lengths and angles. Associated with any such matrix representation are the kernel (or null space), the set of vectors \mathbf{x} \in \mathbb{R}^n such that A \mathbf{x} = \mathbf{0}, and the image (or column space), the span of the columns of A in \mathbb{R}^m; these subspaces characterize the transformation's injectivity and surjectivity, respectively, serving as key prerequisites for further analysis.^[11]^[12]

Basic Operations

Matrix addition is defined for two matrices A and B of the same dimensions m \times n as the matrix C = A + B, where each entry is given by c_{ij} = a_{ij} + b_{ij}.^[13] This operation is commutative, meaning A + B = B + A, and associative, meaning (A + B) + C = A + (B + C) for compatible matrices A, B, and C.^[14] Scalar multiplication of a matrix A by a scalar k produces the matrix kA, where each entry is multiplied by k, so (kA)_{ij} = k \cdot a_{ij}.^[15] This operation distributes over matrix addition: k(A + B) = kA + kB and (k + l)A = kA + lA for scalars k and l.^[16] Matrix multiplication is defined for an m \times n matrix A and an n \times p matrix B as the m \times p matrix C = AB, where each entry is c_{ij} = \sum_{k=1}^n a_{ik} b_{kj}.^[17] This operation corresponds to the composition of linear transformations: if A represents the transformation T and B represents S, then AB represents T \circ S, since AB\mathbf{x} = A(B\mathbf{x}) = T(S(\mathbf{x})) for any vector \mathbf{x}.^[18] The transpose of a matrix A, denoted A^T, is the matrix where (A^T)_{ij} = a_{ji}.^[19] A key property is that the transpose reverses the order of multiplication: (AB)^T = B^T A^T.^[19] For a square n \times n matrix A, the inverse A^{-1} is the matrix satisfying A A^{-1} = I_n = A^{-1} A, where I_n is the n \times n identity matrix.^[20] Such an inverse exists if and only if \det(A) \neq 0.^[20] As an example of matrix multiplication, consider the $2 \times 2 matrices representing simple shear transformations: A = \begin{pmatrix} 1 & 1 \\ 0 & 1 \end{pmatrix} (horizontal shear by factor 1) and B = \begin{pmatrix} 1 & 0 \\ 1 & 1 \end{pmatrix} (vertical shear by factor 1). Their product is

AB = \begin{pmatrix} 1 & 1 \\ 0 & 1 \end{pmatrix} \begin{pmatrix} 1 & 0 \\ 1 & 1 \end{pmatrix} = \begin{pmatrix} 2 & 1 \\ 1 & 1 \end{pmatrix},

which represents a combined shear transformation.

Storage Conventions

Row-Major Ordering

Row-major ordering is a convention for storing the elements of a multidimensional array in linear memory such that the elements of each row are placed contiguously, followed immediately by the elements of the next row. For an m \times n matrix A, using 0-based indexing, the element a_{ij} is mapped to the position i \cdot n + j in a one-dimensional array of length m \cdot n. This layout ensures that traversing a row sequentially accesses consecutive memory locations, leveraging the linear nature of computer memory. This storage scheme is standard in the C programming language, where a two-dimensional array is treated as an array of rows, with the rightmost subscript varying fastest during memory allocation. As described in early C documentation, multidimensional arrays in C are stored row by row, contrasting with column-oriented languages like Fortran. This design choice facilitates intuitive programming for row-based iterations and aligns with the language's array declaration syntax. Consider a simple $2 \times 3 matrix

\begin{bmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \end{bmatrix}.

In row-major order, it is laid out in memory as the contiguous sequence [1, 2, 3, 4, 5, 6], allowing efficient access to entire rows without striding through memory. One key advantage of row-major ordering is its efficiency in operations involving row-wise access, such as computing the column-vector matrix product y = A x, where x is an n \times 1 column vector. Here, each element of y is the dot product of a row of A with x, and the contiguous storage of rows minimizes memory jumps, enhancing data locality. This layout also benefits matrix multiplication algorithms that process rows sequentially, as the inner loops can exploit contiguous access patterns. Row-major ordering improves cache performance for algorithms that iterate over rows, as modern processors prefetch contiguous blocks into cache lines, reducing misses during sequential reads. In image processing, for instance, operations like horizontal filters or scanline traversals—common in raster graphics—achieve higher throughput because pixel data in each row resides adjacently in memory, aligning with typical cache line sizes of 64 bytes or more.

Column-Major Ordering

In column-major ordering, the elements of a matrix are stored in memory such that all elements in a given column are contiguous, with the elements of column j preceding those of column j+1. For an m \times n matrix A with 0-based indexing, the linear memory index for element a_{ij} (row i, column j) is given by j \cdot m + i.^[21]^[22] This storage convention became the standard in Fortran, originating from its development in the mid-1950s for the IBM 704 computer, where the language's design aligned with the machine's instruction set and debugging tools that favored column-oriented access patterns for scientific computations.^[23] Column-major ordering provides advantages in efficiency for operations requiring sequential access to entire columns, such as matrix-vector multiplication y = Ax where x is a column vector, as this formulation sums scaled columns of A, leveraging contiguous memory for each column.^[24] For example, consider the 2 × 3 matrix

\begin{pmatrix} 1 & 2 & 3 \\ 4 & 5 & 6 \end{pmatrix};

in column-major order, it is stored linearly as [1, 4, 2, 5, 3, 6], allowing direct sequential reads for the first column (1, 4), second (2, 5), and third (3, 6).^[21] Regarding cache performance, column-major ordering is particularly suited to algorithms that process data column by column, such as Gaussian elimination for solving linear systems, where column-oriented implementations in libraries like LINPACK align with Fortran's layout to minimize cache misses and achieve up to 30% reductions in execution time for medium-sized problems by optimizing memory locality.^[24] This contrasts with row-major ordering, the alternative convention used in languages like C, which prioritizes row contiguity.^[25]

Applications in Computing

Computer Graphics

In computer graphics, matrix representations are fundamental for performing geometric transformations on vertices in 3D space, enabling efficient rendering of scenes through translations, rotations, scalings, and projections. These operations are typically handled using 4×4 homogeneous matrices, which extend 3D coordinates to four dimensions by adding a homogeneous component (usually 1), allowing affine transformations—including non-linear ones like translation—to be expressed as linear matrix multiplications.^[26] This approach unifies all transformations into a single matrix form, facilitating composition and hardware acceleration on GPUs.^[27] A key example is the translation matrix, which shifts a point or vector by offsets t_x, t_y, t_z:

\begin{pmatrix} 1 & 0 & 0 & t_x \\ 0 & 1 & 0 & t_y \\ 0 & 0 & 1 & t_z \\ 0 & 0 & 0 & 1 \end{pmatrix}

When multiplied by a homogeneous vertex vector \begin{pmatrix} x & y & z & 1 \end{pmatrix}^T, it yields the translated position \begin{pmatrix} x + t_x & y + t_y & z + t_z & 1 \end{pmatrix}^T.^[28] Similar 4×4 matrices represent rotations (via Euler angles or quaternions converted to matrix form), scalings (diagonal matrices with factors along the spatial axes), and projections (perspective or orthographic to map 3D to 2D screen space). Matrix storage conventions significantly impact implementation in graphics APIs, particularly regarding row-major versus column-major ordering, which affects memory layout and vector multiplication order. In OpenGL, matrices are stored in column-major order, treating vertices as column vectors and applying transformations via pre-multiplication (matrix × vector).^[29] Conversely, DirectX employs row-major storage, using row vectors and post-multiplication (vector × matrix) for transformations.^[30] This difference influences how developers pass matrices to shaders and shaders interpret them, often requiring transposition when porting code between APIs to maintain consistent mathematical results.^[31] The graphics rendering pipeline relies on composing these matrices to transform vertices from model space to screen space. The model matrix positions objects in world coordinates, the view matrix aligns the scene relative to the camera, and the projection matrix handles perspective or orthographic mapping to clip space, with the combined model-view-projection (MVP) matrix applied via multiplication: Projection × View × Model.^[32] This sequential multiplication enables efficient batch processing of vertex data on GPUs, where the final MVP matrix is uploaded once per draw call.^[33] Storage layouts also influence SIMD optimizations for matrix-vector multiplications, critical for real-time performance on CPUs and GPUs. Column-major ordering in OpenGL aligns well with SIMD instructions like SSE/AVX on x86, allowing contiguous column loads into vector registers for faster dot products during M × v operations.^[34] In DirectX's row-major format, row vectors enable similar vectorization for v × M, but mismatches can lead to transposed loads, increasing cache misses; GPU shaders mitigate this through dedicated matrix hardware that processes 4×4 blocks in parallel, achieving up to 16x throughput gains over scalar CPU code for transformations.^[35]^[36]

Numerical and Scientific Computing

In numerical and scientific computing, dense matrix representations form the backbone of many algorithms for solving linear systems, eigenvalue problems, and simulations, with libraries like BLAS and LAPACK standardizing column-major storage to align with Fortran's memory layout and optimize core operations.^[37] This convention ensures contiguous storage of matrix columns, which enhances data locality for routines such as the general matrix-vector multiplication (GEMV) in BLAS, a fundamental kernel in iterative solvers like the conjugate gradient method for symmetric positive definite systems.^[38] For instance, in conjugate gradient iterations, repeated GEMV calls benefit from reduced cache misses when matrices are stored column-major, as the vector is accessed sequentially while traversing columns.^[39] The choice of matrix storage order significantly influences algorithmic efficiency, particularly in terms of cache performance and memory bandwidth. In finite element methods for partial differential equation simulations, row-major ordering can optimize access patterns for bandwidth-limited operations, such as assembling or solving banded approximations of stiffness matrices, by promoting sequential row-wise traversals that minimize cache misses compared to mismatched column-major layouts.^[40] Ill-suited storage, such as using column-major for predominantly row-oriented algorithms, can increase cache pollution and degrade performance by factors of 2–5 in memory-bound scenarios, underscoring the need to align representation with the dominant access stride in the computational kernel.^[41] A canonical example is solving the linear system Ax = b for an n \times n dense matrix A, where LU decomposition factors A = LU (with L lower triangular and unit diagonal, U upper triangular) in O(n^3) operations, followed by forward and back substitution in O(n^2) each to recover x.^[42] This direct method, implemented in LAPACK's DGETRF routine, leverages column-major storage for pivoted factorization to maintain numerical robustness while exploiting hardware vectorization. For large-scale simulations exceeding single-node memory, parallelism via distributed matrix representations is essential, as in ScaLAPACK, which extends LAPACK over MPI by partitioning dense matrices into block-cyclic distributions across a 2D process grid, balancing load and communication for operations like parallel LU on supercomputers.^[43] This approach scales to thousands of cores, with efficiency demonstrated in applications like climate modeling, where matrix sizes reach petascale dimensions. The condition number \kappa(A) = \|A\| \cdot \|A^{-1}\| quantifies a matrix's sensitivity to perturbations, guiding stability analysis in computations, but storage representation indirectly affects floating-point error accumulation through its influence on operation ordering in blocked or cache-optimized algorithms.^[44] For example, column-major layouts in LAPACK promote summation orders that can alter rounding error bounds by up to machine epsilon times the growth factor in factorizations, emphasizing the role of representation in preserving accuracy for ill-conditioned problems common in scientific simulations.

Advanced and Specialized Representations

Sparse Matrix Storage

Sparse matrices, which contain a significant number of zero entries, require specialized storage formats to optimize memory usage and computational efficiency in large-scale applications, as dense storage would waste resources on explicit zeros.^[45] These formats exploit the sparsity pattern by storing only non-zero elements along with their positions, enabling faster operations in contexts where zero entries dominate.^[45] One common format is the coordinate list (COO), which represents the matrix as a list of triples consisting of the row index i, column index j, and non-zero value for each entry.^[45] This approach uses three parallel arrays—one for values, one for row indices, and one for column indices—each of length equal to the number of non-zeros, allowing elements to be stored in arbitrary order.^[45] COO is straightforward to implement and flexible for dynamic modifications, such as inserting or deleting entries, making it suitable as an initial or intermediate format in software libraries.^[45] However, it lacks efficient random access to specific rows or columns and requires sorting for operations like matrix-vector multiplication, limiting its use in performance-critical routines.^[45] The compressed sparse row (CSR) format addresses these limitations by organizing data in a row-major manner, using three arrays: one for non-zero values, one for their column indices, and a third for row pointers that indicate the starting position of each row in the value and index arrays.^[45] The row pointers array has length n+1 for an n \times n matrix, enabling quick traversal of rows without storing zeros explicitly.^[45] This structure excels in row-wise operations, such as matrix-vector multiplication, where it achieves good cache locality and is amenable to parallelization over rows.^[45] CSR is a staple in iterative solvers due to its compactness and efficiency for common sparse linear algebra tasks.^[45] The compressed sparse column (CSC) format mirrors CSR but orients data column-wise, employing arrays for values, row indices, and column pointers.^[45] It facilitates efficient column-oriented operations, including transpose matrix-vector products, and supports parallelism over columns.^[45] Like CSR, CSC minimizes storage to the essentials of non-zeros, making it valuable when algorithms favor column access patterns.^[45] For illustration, consider a 5 × 5 tridiagonal matrix with 1s on the main diagonal and off-diagonals, and 0s elsewhere; a dense representation requires 25 entries, but CSR stores only approximately 3n = 15 non-zeros (precisely 13, including boundaries) via the value array [1,1,1,1,1,1,1,1,1,1,1,1,1], column indices [1,2,1,2,3,2,3,4,3,4,5], and row pointers [1,3,5,7,9,11,13].^[45] This compression highlights the storage savings for banded or structured sparsity.^[45] These formats find extensive use in graph algorithms, where adjacency matrices are inherently sparse and represented in CSR or COO to model node connections efficiently, and in solvers for partial differential equations (PDEs), where discretized systems yield sparse coefficient matrices amenable to CSR-based iterative methods.^[46]^[45] While conversions between formats like COO to CSR incur sorting and indexing overhead, the resulting efficiency gains in operations outweigh these costs in large-scale problems.^[45]

Compressed and Block Formats

Block matrices, also known as partitioned matrices, divide a larger matrix into smaller submatrices or blocks, facilitating efficient computations on structured data. This partitioning allows operations to be performed on individual blocks rather than the entire matrix, reducing complexity in algorithms such as inversion or factorization. For instance, a matrix M can be represented in 2 × 2 block form as

M = \begin{pmatrix} A & B \\ C & D \end{pmatrix},

where A, B, C, D are submatrices of compatible dimensions.^[47]^[48] Such block structures enable specialized techniques like the Schur complement, which simplifies solving linear systems by eliminating blocks; for the above partitioning, the Schur complement of D is A - BD^{-1}C, assuming D is invertible, and it preserves properties like positive definiteness if the original matrix does.^[49] This method is foundational in numerical linear algebra for handling large-scale systems with hierarchical or recursive structures. Banded storage formats exploit matrices where non-zero elements are confined to a band around the main diagonal, minimizing memory usage by storing only these elements. A banded matrix with p subdiagonals and q superdiagonals has a total bandwidth of p + q + 1, and storage allocates space solely for the main diagonal and the p lower and q upper diagonals.^[50] This approach is particularly effective for tridiagonal matrices (bandwidth 3, with p = q = 1), common in finite difference discretizations of differential equations, where full dense storage would waste resources on zeros outside the band.^[51] Hierarchical matrices (H-matrices) extend block partitioning by recursively dividing the matrix into low-rank off-diagonal blocks, approximating distant interactions with factorized representations to achieve near-linear storage and computational complexity. Introduced by Hackbusch, H-matrices represent matrices from integral equations or discretizations where off-diagonal blocks admit low-rank approximations, enabling fast matrix-vector multiplications in O(n \log n) time for n \times n matrices.^[52] They are widely applied in N-body simulations, such as electrostatics or gravitational potentials, where the kernel's decay allows low-rank blocks to capture far-field effects efficiently.^[53] A specific example of compression in structured matrices is the bordered diagonal format, where a diagonal matrix is augmented with a bordering row and column of non-zeros; storage separates the diagonal elements from the border to avoid redundant zero entries. This format arises in certain optimization problems or bordered systems, allowing parallel factorization by treating the diagonal blocks independently while handling the border sequentially.^[54] In GPU computing, tiled block storage divides matrices into small tiles that fit into shared memory, optimizing data locality and reducing global memory accesses during operations like matrix multiplication in CUDA. Each thread block loads a tile into shared memory, performs local computations, and synchronizes before writing results, achieving up to 10x speedups over naive global memory access for large matrices.^[55] This technique leverages CUDA's memory hierarchy to minimize latency, with tile sizes typically 16×16 or 32×32 elements to match warp sizes and shared memory capacity.^[56]

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Abstract. The aim of this paper is a short introduction to a fundamental al- gorithm for the fast multiplication of vectors with fully populated, spe-.
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This paper presents research into parallel direct methods for block-diagonal-bordered sparse matrices --- LU factorization and Choleski factorization ...
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CUDA C++ Programming Guide
The programming guide to the CUDA model and interface.
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Matrix Multiplication Background User's Guide - NVIDIA Docs
Feb 1, 2023 · This guide describes matrix multiplications and their use in many deep learning operations. The trends described here form the basis of performance trends.Math And Memory Bounds · GPU Implementation · Typical Tile Dimensions In...