In mathematics, in the area of numerical analysis, Galerkin methods, named after the Russian mathematician Boris Galerkin, convert a continuous operator problem, such as a differential equation, commonly in a weak formulation, to a discrete problem by applying linear constraints determined by finite sets of basis functions. Often when referring to a Galerkin method, one also gives the name along with typical assumptions and approximation methods used: Ritz–Galerkin method (after Walther Ritz) typically assumes symmetric and positive definite bilinear form in the weak formulation, where the differential equation for a physical system can be formulated via minimization of a quadratic function representing the system energy and the approximate solution is a linear combination of the given set of the basis functions. Bubnov–Galerkin method (after Ivan Bubnov) does not require the bilinear form to be symmetric and substitutes the energy minimization with orthogonality constraints determined by the same basis functions that are used to approximate the solution. In an operator formulation of the differential equation, Bubnov–Galerkin method can be viewed as applying an orthogonal projection to the operator. Petrov–Galerkin method (after Georgii I. Petrov) allows using basis functions for orthogonality constraints (called test basis functions) that are different from the basis functions used to approximate the solution. Petrov–Galerkin method can be viewed as an extension of Bubnov–Galerkin method, applying a projection that is not necessarily orthogonal in the operator formulation of the differential equation. Examples of Galerkin methods are: the Galerkin method of weighted residuals, the most common method of calculating the global stiffness matrix in the finite element method, the boundary element method for solving integral equations, Krylov subspace methods. Example: matrix linear system We first introduce and illustrate the Galerkin method as being applied to a system of linear equations with the following symmetric and positive definite matrix and the solution and right-hand-side vectors Let us take then the matrix of the Galerkin equation is the right-hand-side vector of the Galerkin equation is so that we obtain the solution vector to the Galerkin equation , which we finally uplift to determine the approximate solution to the original equation as In this example, our original Hilbert space is actually the 3-dimensional Euclidean space equipped with the standard scalar product , our 3-by-3 matrix defines the bilinear form , and the right-hand-side vector defines the bounded linear functional . The columns of the matrix form an orthonormal basis of the 2-dimensional