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In Wikipedia and other similar platforms, a "stub" is a term used to describe an article that is incomplete or lacks sufficient detail. It serves as a placeholder for topics that may be significant but have not yet been fully developed in terms of content. "Computational science stubs" would refer specifically to articles related to computational science that need expansion.

In the context of Wikipedia and other collaborative platforms, a "stub" is a term used to describe a short article or incomplete entry that provides minimal information on a topic. A "Mathematical physics stub" specifically refers to articles that relate to mathematical physics but do not contain enough information to provide a comprehensive overview of the subject. Mathematical physics itself is a field that focuses on the application of mathematical techniques to problems in physics and the formulation of physical theories in mathematically rigorous terms.

In the context of Wikipedia and other online collaborative platforms, a "stub" refers to a very short article that provides minimal information on a given topic but is not fully developed. Theoretical computer science stubs would therefore refer to brief entries about concepts, theories, or topics related to theoretical computer science that need to be expanded or elaborated upon. Theoretical computer science itself is a branch of computer science that deals with the abstract and mathematical aspects of computation.

The Abstract Additive Schwarz Method (AASM) is a domain decomposition technique used for solving partial differential equations (PDEs) numerically. This method is particularly useful for problems that can be split into subdomains, allowing for parallel computation and reducing the overall computational cost. Here's a brief overview of the key concepts: 1. **Domain Decomposition**: The method partitions the computational domain into smaller subdomains.

In mathematics and physics, the term "adjoint equation" often arises in the context of linear differential equations, functional analysis, and optimal control theory. The specific meaning can depend on the context in which it is used. Here’s a brief overview of its applications: 1. **Linear Differential Equations**: In the analysis of linear differential equations, the adjoint of a linear operator is typically another linear operator that reflects certain properties of the original operator.

Arm is a company known for its semiconductor and software design, particularly in the area of processor architecture. Their primary solutions revolve around the design of ARM architecture, which is used in a wide range of devices, from smartphones and tablets to embedded systems and IoT (Internet of Things) devices. Arm does not manufacture chips; instead, it licenses its designs to other companies that produce chips based on Arm architecture.

Artstein's theorem is a result in the field of convex analysis and modern functional analysis, specifically concerning the relationships between convexity, monotonicity, and properties of measures or functions. The theorem provides a framework for understanding when certain inequalities involving integrals hold, particularly in relation to convex functions.

The Baldwin–Lomax model is a mathematical model used in fluid dynamics to predict the behavior of turbulent flows, particularly in the context of boundary layer flows over surfaces. This model specifically addresses the turbulence characteristics in boundary layers, which are layers of fluid in close proximity to a solid surface where viscous effects are significant. The Baldwin–Lomax model is notable for its simplicity and its semi-empirical nature, meaning it combines theoretical concepts with empirical data to provide closure to the turbulence equations.

A barrier function is a concept commonly used in optimization, particularly in the context of constrained optimization problems. Barrier functions help to modify the optimization problem so that the constraints are incorporated into the objective function, allowing for easier handling of constraints during the optimization process. The main idea is to add a penalty to the objective function that becomes increasingly large as the solution approaches the boundaries of the feasible region defined by the constraints.

In the context of linear programming, a **basic solution** refers to a specific type of solution obtained from the standard form of a linear programming problem, which can be solved using methods such as the Simplex algorithm. When linear programming problems are formulated, they are often represented in a tableau, where the solution is represented as a combination of basic and non-basic variables.

Basis Pursuit is an optimization technique used in the field of signal processing and compressed sensing, primarily for recovering sparse signals from limited or incomplete measurements. The fundamental idea behind Basis Pursuit is to express a signal as a linear combination of basis functions and to find the representation that uses the fewest non-zero coefficients, thereby focusing on the sparsest solution.

The Bedlam Cube is a term primarily associated with an art installation and a mathematical object. In the context of art, it refers to a complex, abstract structure or sculpture, often designed to challenge perceptions and spatial understanding, echoing the chaotic and intricate nature of a "bedlam" or disorderly environment. In mathematical or mathematical puzzle contexts, the term can evoke the idea of intricate shapes or complex surfaces that can be difficult to visualize or manipulate, related to topics in topology or geometry.

The Benjamin–Ono equation is a nonlinear partial differential equation that describes the propagation of long waves in one-dimensional shallow water, specifically in the context of surface water waves. It can also be viewed as a model for various other physical phenomena. The equation is named after the mathematicians Jerry Benjamin and A. T. Ono, who derived it in the 1960s.

The Bessel-Maitland functions are a class of special functions that generalize the well-known Bessel functions. They arise in the study of differential equations, particularly those that describe wave propagation, heat conduction, and other physical phenomena.

Biconvex optimization refers to a class of optimization problems that involve a biconvex function. A function \( f(x, y) \) defined on a product space \( X \times Y \) (where \( X \) and \( Y \) are convex sets) is considered biconvex if it is convex in \( x \) for each fixed \( y \), and convex in \( y \) for each fixed \( x \).

A bilinear program is a type of mathematical optimization problem that involves both linear and bilinear components in its formulation.

A binary constraint is a type of constraint that involves exactly two variables in a constraint satisfaction problem (CSP). In the context of CSPs, constraints are rules or conditions that restrict the values that variables can simultaneously take. Binary constraints specify the relationships between pairs of variables and define which combinations of variable values are acceptable.

The Bogomolny equations are a set of partial differential equations that arise in the context of supersymmetric field theories and are particularly significant in the study of solitons, such as magnetic monopoles. Named after the physicist E.B. Bogomolny, these equations provide a way to find solutions that satisfy certain stability conditions. In the context of gauge theory, the Bogomolny equations generally involve a relationship between a gauge field and scalar fields.

The Broer-Kaup equations are a system of partial differential equations that describe long wave interactions in shallow water waves, particularly focusing on the evolution of small amplitude waves in a two-dimensional medium. These equations arise in the context of studying wave phenomena in various physical systems, including fluid dynamics and nonlinear wave interactions. The Broer-Kaup system can be derived from the incompressible Euler equations under certain approximations and is characterized by its ability to model the evolution of wave packets and their interactions over time.

The Brown–Gibson model is a theoretical framework used in the field of economic geography and regional science to analyze and understand the dynamics of technological change and innovation diffusion. Developed by economists William Brown and James Gibson, the model focuses on the spatial aspects of economic activities, particularly how innovations spread across geographic areas and influence regional development.