Children: Linear algebra
The Rule of Sarrus is a mnemonic used to evaluate the determinant of a \(3 \times 3\) matrix. It is particularly useful because it provides a simple and intuitive way to compute the determinant without resorting to the more formal cofactor expansion method.
The S-procedure is a mathematical technique used in convex optimization and control theory, specifically in the context of robust control and system stability analysis. It provides a way to transform certain types of inequalities involving quadratic forms into conditions that can be expressed in terms of linear matrix inequalities (LMIs).
The Samuelson–Berkowitz algorithm is a computational method used in the field of operations research, specifically for solving certain types of optimization problems related to network flows and linear programming. While there isn't a vast amount of detailed literature specifically detailing this algorithm, the name typically refers to work by economists Paul Samuelson and others who contributed to economic theories involving optimization under constraints. However, the details of the algorithm, its implementation, and specific applications are not widely discussed in mainstream literature.
The Schmidt decomposition is a mathematical technique used in quantum mechanics and quantum information theory to express a bipartite quantum state in a particularly useful form. It is analogous to the singular value decomposition in linear algebra. For a bipartite quantum system, which consists of two subsystems (commonly referred to as systems A and B), the Schmidt decomposition allows us to write a pure state \(|\psi\rangle\) in such a way that it identifies the correlations between the two subsystems.
The Schur complement is a concept in linear algebra that arises when dealing with block matrices. Given a partitioned matrix, the Schur complement provides a way to express one part of the matrix in terms of the other parts.
The Schur product theorem is a result in linear algebra related to matrices and their positive semi-definiteness. It establishes a relationship between the Schur product (or Hadamard product) of two matrices and the positive semi-definiteness of those matrices.
Sedrakyan's inequality is a result in the field of mathematical analysis, particularly in relation to inequalities involving sums and sequences. While the specific details and formulations of Sedrakyan's inequality can vary based on the context, a common form of this inequality is related to bounding certain sums involving positive real numbers.
Semi-simplicity is a concept used in various fields such as mathematics and physics, often in the context of algebraic structures. The meaning of semi-simplicity can vary depending on the context, but it generally refers to particular types of structures that are "almost" simple or can be decomposed into simpler components. ### In Mathematics 1.
A semilinear map is a type of function that appears in the context of vector spaces, particularly in linear algebra and functional analysis. It generalizes the notion of linear maps by allowing for a change of scalars through a field automorphism. Formally, let \( V \) and \( W \) be vector spaces over a field \( F \).
A seminorm is a mathematical concept used in functional analysis, particularly in the study of vector spaces. It generalizes the idea of a norm but is less restrictive.
A sesquilinear form is a mathematical function that is similar to a bilinear form, but with a crucial distinction related to how it treats its variables. Specifically, a sesquilinear form is defined on a complex vector space and is linear in one argument and conjugate-linear (or antilinear) in the other. To clarify: - Let \( V \) be a complex vector space.
Shear mapping, also known as shear transformation, is a type of linear transformation that distorts the shape of an object by shifting its points in a specific direction, while leaving the other dimensions unchanged. In a shear mapping, lines that are parallel remain parallel, and the angles between lines can change, but the lengths of the lines themselves do not change. In two dimensions, a shear mapping can be represented by a shear matrix.
A shear matrix is a type of matrix used in linear algebra to perform a shear transformation on geometric objects in a vector space. Shear transformations are categorical transformations that "slant" or "shear" the shape of an object in a particular direction while keeping its area (in 2D) or volume (in 3D) unchanged.
The Sherman-Morrison formula is a statement in linear algebra that provides a way to compute the inverse of a matrix when that matrix is modified by the addition of a rank-one update.
A signal-flow graph (SFG) is a graphical representation used in control system engineering and signal processing to illustrate the flow of signals through a system. It represents the relationships between variables in a system, allowing for an intuitive understanding of how inputs are transformed into outputs through various paths. Here are the key components and features of a signal-flow graph: 1. **Nodes**: Represent system variables (such as system inputs, outputs, and intermediate signals). Each node corresponds to a variable in the system.
Singular Value Decomposition (SVD) is a mathematical technique in linear algebra used to factorize a matrix into three other matrices. It is particularly useful for analyzing and reducing the dimensionality of data, solving linear equations, and performing principal component analysis.
A **Skew-Hamiltonian matrix** is a special type of matrix that arises in the context of symplectic geometry and control theory, particularly in the study of Hamiltonian systems. To define a Skew-Hamiltonian matrix, recall that a **Hamiltonian matrix** \( H \) is typically associated with structures that preserve energy in dynamic systems.
The Special Linear Group, commonly denoted as \( \text{SL}(n, \mathbb{F}) \), is a fundamental concept in linear algebra and group theory. It consists of all \( n \times n \) matrices with entries from a field \( \mathbb{F} \) that have a determinant equal to 1.
The Spectral Theorem is a fundamental result in linear algebra and functional analysis that pertains to the diagonalization of certain types of matrices and operators. It provides a relationship between a linear operator or matrix and its eigenvalues and eigenvectors.
Spectral theory is a branch of mathematics that studies the spectrum of operators, particularly linear operators on function spaces or finite-dimensional vector spaces. It is closely related to linear algebra, functional analysis, and quantum mechanics, among other fields. The spectrum of an operator can be thought of as the set of values (often complex numbers) for which the operator does not behave like a regular linear transformation—in particular, where it does not have an inverse.