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Everything about Square matrix
Matrix with the same number of rows and columns
In mathematics, a square matrix is a matrix with the same number of rows and columns. An n-by-n matrix is known as a square matrix of order . Any two square matrices of the same order can be added and multiplied. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Square matrixA 5-minute orientation
- Skim a recent paper: Repair of fire-damaged RC square columns with CFRP textile-reinforced ECC matrixInfluential work since 2022
- Pick up a classic book: The incomplete inverse and its applications to the linear least squares problemThe most-reprinted book on the subject
- Read the landmark paper: Computing real square roots of a real matrixCited 155 times
- Go sideways: Definite matrixA closely related topic
- Go sideways: Hermitian matrixA closely related topic
📖 Definitions
Noun A matrix having the same number of rows as columns.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Computing real square roots of a real matrix
The Toluenedithiolate and Maleonitriledithiolate Square-Matrix Systems
Stable iterations for the matrix square root
A Generalized Least-Square Matrix Decomposition
The permanent of a square matrix
Limits and the Index of a Square Matrix
Congruences of a square matrix and its transpose
Functions Preserving Matrix Groups and Iterations for the Matrix Square Root
🆕 Influential since 2022
Repair of fire-damaged RC square columns with CFRP textile-reinforced ECC matrix
Fast Differentiable Matrix Square Root and Inverse Square Root
Flexible active pixel-matrix ultrafast thermal camouflage using square-wave temperature
Strengthening pre-damaged RC square columns with fabric-reinforced cementitious matrix (FRCM): Experimental investigation
Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root
📚 Books worth reading
- 📕The incomplete inverse and its applications to the linear least squares problemGeorge E. Morduch · 1977
Total Least Squares and Errors-in-Variables ModelingSabine Huffel · 2002
2-inverses and their statistical applicationAlbert J. Getson · 1988
Horatio Gates & Benedict ArnoldRobin McKown · 1969
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Definite matrix In mathematics, a symmetric matrix M with real entries is positive-definite if the real number 𝐱𝖳M𝐱 is positive for every nonzero real column vector…
- Hermitian matrix In mathematics, a Hermitian matrix (or self-adjoint matrix) is a square matrix with complex-valued entries that is equal to its own conjugate transpose…
- Normal matrix In mathematics, a complex square matrix A is normal if it commutes with its conjugate transpose A*: A normal⟺A∗A=AA∗. The concept of normal matrices can…
- Eigendecomposition of a matrix In linear algebra, eigendecomposition (also known as eigenvalue decomposition or EVD) is a factorization of a matrix A into a canonical form given by A=QDQ−𝟣…
- Skew-symmetric matrix In mathematics, particularly in linear algebra, a skew-symmetric (or antisymmetric or antimetric) matrix is a square matrix whose transpose equals its…
- Transpose In linear algebra, transposition is an operation that flips a matrix over its diagonal; that is, transposition switches the row and column indices of the…
- Eigenvalue algorithm In numerical analysis, one of the most important problems is designing efficient and stable algorithms for finding the eigenvalues of a matrix. These eigenvalue…
- Gram matrix In linear algebra, the Gram matrix (or Gramian matrix, Gramian) of vectors v1,…,vn in an inner product space is the Hermitian matrix of inner products…