General linear methods (GLMs) are a large class of numerical methods used to obtain numerical solutions to ordinary differential equations. They include...

explicit schemes. The so-called general linear methods (GLMs) are a generalization of the above two large classes of methods. From any point on a curve, you...

In mathematics, a linear approximation is an approximation of a general function using a linear function (more precisely, an affine function). They are...

Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical...

Runge–Kutta methods (English: /ˈrʊŋəˈkʊtɑː/ RUUNG-ə-KUUT-tah) are a family of implicit and explicit iterative methods, which include the Euler method, used...

cutting-plane method is any of a variety of optimization methods that iteratively refine a feasible set or objective function by means of linear inequalities...

The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models...

elimination). Iterative methods are often the only choice for nonlinear equations. However, iterative methods are often useful even for linear problems involving...

computation. In linear systems, the two main classes of relaxation methods are stationary iterative methods, and the more general Krylov subspace methods. The Jacobi...

In mathematics, linear interpolation is a method of curve fitting using linear polynomials to construct new data points within the range of a discrete...

In mathematics, a linear differential equation is a differential equation that is linear in the unknown function and its derivatives, so it can be written...

Interior-point methods (also referred to as barrier methods or IPMs) are algorithms for solving linear and non-linear convex optimization problems. IPMs...

convex and general methods from convex optimization can be used in most cases. If the objective function is quadratic and the constraints are linear, quadratic...

generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model...

one to increase accuracy General linear methods — a class of methods encapsulating linear multistep and Runge-Kutta methods Bulirsch–Stoer algorithm —...

appear linearly. Linear regression may also refer to: The ordinary least squares method, one of the most popular methods for estimating a linear regression...

In mathematics, a system of linear equations (or linear system) is a collection of two or more linear equations involving the same variables. For example...

In numerical analysis, the local linearization (LL) method is a general strategy for designing numerical integrators for differential equations based on...

Numerical methods for linear least squares entails the numerical analysis of linear least squares problems. A general approach to the least squares problem...

the gradient of the function at the current point. Examples of gradient methods are the gradient descent and the conjugate gradient. Gradient descent Stochastic...

optimization, Dantzig's simplex algorithm (or simplex method) is a popular algorithm for linear programming. The name of the algorithm is derived from...

and general linear methods. The Butcher group and the Butcher tableau are named after him. More recently, he is investigating a new type of method with...

engineering. It is a method of regularization of ill-posed problems. It is particularly useful to mitigate the problem of multicollinearity in linear regression...

In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory...

In the theory of general relativity, linearized gravity is the application of perturbation theory to the metric tensor that describes the geometry of spacetime...

operations research, the Big M method is a method of solving linear programming problems using the simplex algorithm. The Big M method extends the simplex algorithm...

function. When specialized to solving feasible linear optimization problems with rational data, the ellipsoid method is an algorithm which finds an optimal solution...

in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least...

direct methods, although problems with large numbers of parameters are typically solved with iterative methods, such as the Gauss–Seidel method. In LLSQ...

Safeguarded curve-fitting methods simultaneously execute a linear-convergence method in parallel to the curve-fitting method. They check in each iteration...