Multigrid method

Multigrid (MG) methods in numerical analysis are fast linear iterative solvers based on the multilevel or multi-scale paradigm. The typical application for multigrid is in the numerical solution of elliptic partial differential equations in two or more dimensions.

MG can be applied in combination with any of the common discretization techniques. In these cases, multigrid is among the fastest solution techniques known today. In contrast to other methods, multigrid is general in that it can treat arbitrary regions and boundary conditions. It does not depend on the separability of the equations or other special properties of the equation. MG is also directly applicable to more complicated, non-symmetric and nonlinear systems of equations, like the Lamé system of elasticity or the Navier-Stokes equations.

In all these cases, multigrid exhibits a convergence rate that is independent of the number of unknowns in the discretized system. It is therefore an optimal method. In combination with nested iteration it can solve these problems to truncation error accuracy in a number of operations that is proportional to the number of unknowns.

Multigrid can be generalized in many different ways. It can be applied naturally in a time-stepping solution of parabolic equations, or it can be applied directly to time-dependent PDE. Research on multilevel techniques for hyperbolic equations is under way. Multigrid can also be applied to integral equations, or for problems in statistical physics.

Other extensions of multigrid include techniques where no PDE and no geometrical problem background is used to construct the multilevel hierarchy. Such algebraic multigrid methods (AMG) construct their hierarchy of operators directly from the system matrix and thus become true black-box solvers for sparse matrices.

---

In mathematics, more specifically in numerical analysis, multigrid methods are a group of algorithms for solving differential equations using a hierarchy of discretizations. This approach has the advantage over other methods that it scales linearly with the number of discrete nodes used.

In order for the multigrid methods to be applicable, one needs to make several assumptions. Assume that one has a differential equation which can be solved approximately (with a given accuracy) on a grid ${\displaystyle i}$ with a given grid point density ${\displaystyle N_{i}}$. Assume furthermore that a solution on any grid ${\displaystyle N_{i}}$ may be obtained with a given effort ${\displaystyle W_{i}=\rho KN_{i}}$ from a solution on a coarser grid ${\displaystyle i+1}$ with grid point density ${\displaystyle N_{i+1}=\rho N_{i}}$ (that is, ${\displaystyle K}$ is not dependent on ${\displaystyle i}$).

Then, using the geometric series, we then find for the effort involved in finding the solution on the finest grid ${\displaystyle N_{1}}$

${\displaystyle W_{1}=W_{2}+\rho KN_{1}}$

${\displaystyle W_{1}=W_{3}+\rho ^{2}KN_{1}+\rho KN_{1}}$

${\displaystyle W_{1}/(KN_{1})+1=1+\sum _{p}\rho ^{p}}$

${\displaystyle W_{1}/(KN_{1})+1=1/(1-\rho )}$

${\displaystyle W_{1}=(KN_{1})(1/(1-\rho )-1),}$

that is, a solution may be obtained in ${\displaystyle O(N)}$ time.

See also

• Numerical analysis
• Partial differential equation
• Finite element method
• Finite difference

References and external links

• Brandt, A. 'Multi-Level Adaptive Solutions to Boundary-Value Problems', Math. Comp, 1977(31), 333-390 (jstor link).
• MGNet: a repository for multigrid and other methods
• M. Holst and F. Saied, Multigrid and ___domain decomposition methods for electrostatics problems. Domain Decomposition Methods in Science and Engineering (Proceedings of the Seventh International Conference on Domain Decomposition Methods, October 27-30, 1993, The Pennsylvania State University) D. E. Keyes and J. Xu, eds., American Mathematical Society, Providence, 1995.
• A multigrid tutorial, ISBN 0-89871-462-1
• Introduction to Algebraic Multigrid
• IMTEK Mathematica Supplement (IMS)
• OCW Course on PDE's