In
mathematics
Mathematics is an area of knowledge that includes the topics of numbers, formulas and related structures, shapes and the spaces in which they are contained, and quantities and their changes. These topics are represented in modern mathematics ...
, preconditioning is the application of a transformation, called the preconditioner, that conditions a given problem into a form that is more suitable for
numerical solving methods. Preconditioning is typically related to reducing a
condition number
In numerical analysis, the condition number of a function measures how much the output value of the function can change for a small change in the input argument. This is used to measure how sensitive a function is to changes or errors in the input ...
of the problem. The preconditioned problem is then usually solved by an
iterative method
In computational mathematics, an iterative method is a Algorithm, mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the ''n''-th approximation is derived fr ...
.
Preconditioning for linear systems
In
linear algebra
Linear algebra is the branch of mathematics concerning linear equations such as:
:a_1x_1+\cdots +a_nx_n=b,
linear maps such as:
:(x_1, \ldots, x_n) \mapsto a_1x_1+\cdots +a_nx_n,
and their representations in vector spaces and through matrices.
...
and
numerical analysis
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic computation, symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). It is the study of ...
, a preconditioner
of a matrix
is a matrix such that
has a smaller
condition number
In numerical analysis, the condition number of a function measures how much the output value of the function can change for a small change in the input argument. This is used to measure how sensitive a function is to changes or errors in the input ...
than
. It is also common to call
the preconditioner, rather than
, since
itself is rarely explicitly available. In modern preconditioning, the application of
, i.e., multiplication of a column vector, or a block of column vectors, by
, is commonly performed in a
matrix-free fashion, i.e., where neither
, nor
(and often not even
) are explicitly available in a matrix form.
Preconditioners are useful in
iterative methods
In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the ''n''-th approximation is derived from the pr ...
to solve a linear system
for
since the
rate of convergence
In numerical analysis, the order of convergence and the rate of convergence of a convergent sequence are quantities that represent how quickly the sequence approaches its limit. A sequence (x_n) that converges to x^* is said to have ''order of co ...
for most iterative linear solvers increases because the
condition number
In numerical analysis, the condition number of a function measures how much the output value of the function can change for a small change in the input argument. This is used to measure how sensitive a function is to changes or errors in the input ...
of a matrix decreases as a result of preconditioning. Preconditioned iterative solvers typically outperform direct solvers, e.g.,
Gaussian elimination
In mathematics, Gaussian elimination, also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of operations performed on the corresponding matrix of coefficients. This method can also be used ...
, for large, especially for
sparse, matrices. Iterative solvers can be used as
matrix-free methods In computational mathematics, a matrix-free method is an algorithm for solving a linear system of equations or an eigenvalue problem that does not store the coefficient matrix explicitly, but accesses the matrix by evaluating matrix-vector products. ...
, i.e. become the only choice if the coefficient matrix
is not stored explicitly, but is accessed by evaluating matrix-vector products.
Description
Instead of solving the original linear system above, one may consider the right preconditioned system
:
and solve
:
for
and
:
for
.
Alternatively, one may solve the left preconditioned system
:
Both systems give the same solution as the original system as long as the preconditioner matrix
is
nonsingular
In linear algebra, an -by- square matrix is called invertible (also nonsingular or nondegenerate), if there exists an -by- square matrix such that
:\mathbf = \mathbf = \mathbf_n \
where denotes the -by- identity matrix and the multiplicati ...
. The left preconditioning is more traditional.
The two-sided preconditioned system
:
may be beneficial, e.g., to preserve the matrix symmetry: if the original matrix
is real symmetric and real preconditioners
and
satisfy
then the preconditioned matrix
is also symmetric. The two-sided preconditioning is common for diagonal scaling where the preconditioners
and
are diagonal and scaling is applied both to columns and rows of the original matrix
, e.g., in order to decrease the dynamic range of entries of the matrix.
The goal of preconditioning is reducing the
condition number
In numerical analysis, the condition number of a function measures how much the output value of the function can change for a small change in the input argument. This is used to measure how sensitive a function is to changes or errors in the input ...
, e.g., of the left or right preconditioned system matrix
or
. Small condition numbers benefit fast convergence of iterative solvers and improve stability of the solution with respect to perturbations in the system matrix and the right-hand side, e.g., allowing for more aggressive
quantization of the matrix entries using lower
computer precision.
The preconditioned matrix
or
is rarely explicitly formed. Only the action of applying the preconditioner solve operation
to a given vector may need to be computed.
Typically there is a trade-off in the choice of
. Since the operator
must be applied at each step of the iterative linear solver, it should have a small cost (computing time) of applying the
operation. The cheapest preconditioner would therefore be
since then
Clearly, this results in the original linear system and the preconditioner does nothing. At the other extreme, the choice
gives
which has optimal
condition number
In numerical analysis, the condition number of a function measures how much the output value of the function can change for a small change in the input argument. This is used to measure how sensitive a function is to changes or errors in the input ...
of 1, requiring a single iteration for convergence; however in this case
and applying the preconditioner is as difficult as solving the original system. One therefore chooses
as somewhere between these two extremes, in an attempt to achieve a minimal number of linear iterations while keeping the operator
as simple as possible. Some examples of typical preconditioning approaches are detailed below.
Preconditioned iterative methods
Preconditioned iterative methods for
are, in most cases, mathematically equivalent to standard iterative methods applied to the preconditioned system
For example, the standard
Richardson iteration
Modified Richardson iteration is an iterative method for solving a system of linear equations. Richardson iteration was proposed by Lewis Fry Richardson in his work dated 1910. It is similar to the Jacobi and Gauss–Seidel method.
We seek the ...
for solving
is
:
Applied to the preconditioned system
it turns into a preconditioned method
:
Examples of popular preconditioned
iterative methods
In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the ''n''-th approximation is derived from the pr ...
for linear systems include the
preconditioned conjugate gradient method
In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is positive-definite. The conjugate gradient method is often implemented as an iterative ...
, the
biconjugate gradient method
In mathematics, more specifically in numerical linear algebra, the biconjugate gradient method is an algorithm to solve systems of linear equations
:A x= b.\,
Unlike the conjugate gradient method, this algorithm does not require the matrix A to ...
, and
generalized minimal residual method In mathematics, the generalized minimal residual method (GMRES) is an iterative method for the numerical solution of an indefinite nonsymmetric system of linear equations. The method approximates the solution by the vector in a Krylov subspace with ...
. Iterative methods, which use scalar products to compute the iterative parameters, require corresponding changes in the scalar product together with substituting
for
Matrix splitting
A
stationary iterative method is determined by the matrix splitting
and the iteration matrix
. Assuming that
* the system matrix
is
symmetric
Symmetry (from grc, συμμετρία "agreement in dimensions, due proportion, arrangement") in everyday language refers to a sense of harmonious and beautiful proportion and balance. In mathematics, "symmetry" has a more precise definiti ...
positive-definite In mathematics, positive definiteness is a property of any object to which a bilinear form or a sesquilinear form may be naturally associated, which is positive-definite. See, in particular:
* Positive-definite bilinear form
* Positive-definite fu ...
,
* the splitting matrix
is
symmetric
Symmetry (from grc, συμμετρία "agreement in dimensions, due proportion, arrangement") in everyday language refers to a sense of harmonious and beautiful proportion and balance. In mathematics, "symmetry" has a more precise definiti ...
positive-definite In mathematics, positive definiteness is a property of any object to which a bilinear form or a sesquilinear form may be naturally associated, which is positive-definite. See, in particular:
* Positive-definite bilinear form
* Positive-definite fu ...
,
* the stationary iterative method is convergent, as determined by
,
the
condition number
In numerical analysis, the condition number of a function measures how much the output value of the function can change for a small change in the input argument. This is used to measure how sensitive a function is to changes or errors in the input ...
is bounded above by
:
Geometric interpretation
For a
symmetric
Symmetry (from grc, συμμετρία "agreement in dimensions, due proportion, arrangement") in everyday language refers to a sense of harmonious and beautiful proportion and balance. In mathematics, "symmetry" has a more precise definiti ...
positive definite In mathematics, positive definiteness is a property of any object to which a bilinear form or a sesquilinear form may be naturally associated, which is positive-definite. See, in particular:
* Positive-definite bilinear form
* Positive-definite f ...
matrix
the preconditioner
is typically chosen to be symmetric positive definite as well. The preconditioned operator
is then also symmetric positive definite, but with respect to the
-based
scalar product
In mathematics, the dot product or scalar productThe term ''scalar product'' means literally "product with a scalar as a result". It is also used sometimes for other symmetric bilinear forms, for example in a pseudo-Euclidean space. is an algebra ...
. In this case, the desired effect in applying a preconditioner is to make the
quadratic form
In mathematics, a quadratic form is a polynomial with terms all of degree two ("form" is another name for a homogeneous polynomial). For example,
:4x^2 + 2xy - 3y^2
is a quadratic form in the variables and . The coefficients usually belong to a ...
of the preconditioned operator
with respect to the
-based
scalar product
In mathematics, the dot product or scalar productThe term ''scalar product'' means literally "product with a scalar as a result". It is also used sometimes for other symmetric bilinear forms, for example in a pseudo-Euclidean space. is an algebra ...
to be nearly spherical.
Variable and non-linear preconditioning
Denoting
, we highlight that preconditioning is practically implemented as multiplying some vector
by
, i.e., computing the product
In many applications,
is not given as a matrix, but rather as an operator
acting on the vector
. Some popular preconditioners, however, change with
and the dependence on
may not be linear. Typical examples involve using non-linear
iterative methods
In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the ''n''-th approximation is derived from the pr ...
, e.g., the
conjugate gradient method
In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is positive-definite. The conjugate gradient method is often implemented as an iterativ ...
, as a part of the preconditioner construction. Such preconditioners may be practically very efficient, however, their behavior is hard to predict theoretically.
Random preconditioning
One interesting particular case of variable preconditioning is random preconditioning, e.g.,
multigrid In numerical analysis, a multigrid method (MG method) is an algorithm for solving differential equations using a hierarchy of discretizations. They are an example of a class of techniques called multiresolution methods, very useful in problems exhi ...
preconditioning on random course grids. If used in
gradient descent
In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the ...
methods, random preconditioning can be viewed as an implementation of
stochastic gradient descent
Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of ...
and can lead to faster convergence, compared to fixed preconditioning, since it breaks the asymptotic "zig-zag" pattern of the
gradient descent
In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the ...
.
Spectrally equivalent preconditioning
The most common use of preconditioning is for iterative solution of linear systems resulting from approximations of
partial differential equations
In mathematics, a partial differential equation (PDE) is an equation which imposes relations between the various partial derivatives of a multivariable function.
The function is often thought of as an "unknown" to be solved for, similarly to ...
. The better the approximation quality, the larger the matrix size is. In such a case, the goal of optimal preconditioning is, on the one side, to make the spectral condition number of
to be bounded from above by a constant independent of the matrix size, which is called ''spectrally equivalent'' preconditioning by
D'yakonov. On the other hand, the cost of application of the
should ideally be proportional (also independent of the matrix size) to the cost of multiplication of
by a vector.
Examples
Jacobi (or diagonal) preconditioner
The Jacobi preconditioner is one of the simplest forms of preconditioning, in which the preconditioner is chosen to be the diagonal of the matrix
Assuming
, we get
It is efficient for
diagonally dominant matrices
In mathematics, a square matrix is said to be diagonally dominant if, for every row of the matrix, the magnitude of the diagonal entry in a row is larger than or equal to the sum of the magnitudes of all the other (non-diagonal) entries in that row ...
. It is used in analysis softwares for beam problems or 1-D problems (EX:- STAAD PRO)
SPAI
The Sparse Approximate Inverse preconditioner minimises
where
is the
Frobenius norm
In mathematics, a matrix norm is a vector norm in a vector space whose elements (vectors) are matrices (of given dimensions).
Preliminaries
Given a field K of either real or complex numbers, let K^ be the -vector space of matrices with m rows ...
and
is from some suitably constrained set of
sparse matrices
In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero. There is no strict definition regarding the proportion of zero-value elements for a matrix to qualify as sparse b ...
. Under the Frobenius norm, this reduces to solving numerous independent least-squares problems (one for every column). The entries in
must be restricted to some sparsity pattern or the problem remains as difficult and time-consuming as finding the exact inverse of
. The method was introduced by M.J. Grote and T. Huckle together with an approach to selecting sparsity patterns.
Other preconditioners
*
Incomplete Cholesky factorization In numerical analysis, an incomplete Cholesky factorization of a symmetric positive definite matrix is a sparse approximation of the Cholesky factorization. An incomplete Cholesky factorization is often used as a preconditioner for algorithms like ...
*
Incomplete LU factorization In numerical linear algebra, an incomplete LU factorization (abbreviated as ILU) of a matrix is a sparse approximation of the LU factorization often used as a preconditioner.
Introduction
Consider a sparse linear system Ax = b. These are often s ...
*
Successive over-relaxation In numerical linear algebra, the method of successive over-relaxation (SOR) is a variant of the Gauss–Seidel method for solving a linear system of equations, resulting in faster convergence. A similar method can be used for any slowly converging ...
**
Symmetric successive over-relaxation
In applied mathematics, symmetric successive over-relaxation (SSOR), is a preconditioner.
If the original matrix can be split into diagonal, lower and upper triangular as A=D+L+L^\mathsf then the SSOR preconditioner matrix is defined as
M=(D+L) D^ ...
*
Multigrid preconditioning
External links
Preconditioned Conjugate Gradient– math-linux.com
Preconditioning for eigenvalue problems
Eigenvalue problems can be framed in several alternative ways, each leading to its own preconditioning. The traditional preconditioning is based on the so-called ''spectral transformations.'' Knowing (approximately) the targeted eigenvalue, one can compute the corresponding eigenvector by solving the related homogeneous linear system, thus allowing to use preconditioning for linear system. Finally, formulating the eigenvalue problem as optimization of the
Rayleigh quotient
In mathematics, the Rayleigh quotient () for a given complex Hermitian matrix ''M'' and nonzero vector ''x'' is defined as:
R(M,x) = .
For real matrices and vectors, the condition of being Hermitian reduces to that of being symmetric, and the con ...
brings preconditioned optimization techniques to the scene.
Spectral transformations
By analogy with linear systems, for an
eigenvalue
In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted b ...
problem
one may be tempted to replace the matrix
with the matrix
using a preconditioner
. However, this makes sense only if the seeking
eigenvectors
In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted b ...
of
and
are the same. This is the case for spectral transformations.
The most popular spectral transformation is the so-called ''shift-and-invert'' transformation, where for a given scalar
, called the ''shift'', the original eigenvalue problem
is replaced with the shift-and-invert problem
. The eigenvectors are preserved, and one can solve the shift-and-invert problem by an iterative solver, e.g., the
power iteration
In mathematics, power iteration (also known as the power method) is an eigenvalue algorithm: given a diagonalizable matrix (mathematics), matrix A, the algorithm will produce a number \lambda, which is the greatest (in absolute value) eigenvalue of ...
. This gives the
Inverse iteration In numerical analysis, inverse iteration (also known as the ''inverse power method'') is an Iterative method, iterative eigenvalue algorithm. It allows one to find an approximate
eigenvector when an approximation to a corresponding eigenvalue is alr ...
, which normally converges to the eigenvector, corresponding to the eigenvalue closest to the shift
. The
Rayleigh quotient iteration Rayleigh quotient iteration is an eigenvalue algorithm which extends the idea of the inverse iteration by using the Rayleigh quotient to obtain increasingly accurate eigenvalue estimates.
Rayleigh quotient iteration is an iterative method, that is, ...
is a shift-and-invert method with a variable shift.
Spectral transformations are specific for eigenvalue problems and have no analogs for linear systems. They require accurate numerical calculation of the transformation involved, which becomes the main bottleneck for large problems.
General preconditioning
To make a close connection to linear systems, let us suppose that the targeted eigenvalue
is known (approximately). Then one can compute the corresponding eigenvector from the homogeneous linear system
. Using the concept of left preconditioning for linear systems, we obtain
, where
is the preconditioner, which we can try to solve using the
Richardson iteration
Modified Richardson iteration is an iterative method for solving a system of linear equations. Richardson iteration was proposed by Lewis Fry Richardson in his work dated 1910. It is similar to the Jacobi and Gauss–Seidel method.
We seek the ...
:
The ''ideal'' preconditioning
The
Moore–Penrose pseudoinverse is the preconditioner, which makes the
Richardson iteration
Modified Richardson iteration is an iterative method for solving a system of linear equations. Richardson iteration was proposed by Lewis Fry Richardson in his work dated 1910. It is similar to the Jacobi and Gauss–Seidel method.
We seek the ...
above converge in one step with
, since
, denoted by
, is the orthogonal projector on the eigenspace, corresponding to
. The choice
is impractical for three independent reasons. First,
is actually not known, although it can be replaced with its approximation
. Second, the exact
Moore–Penrose pseudoinverse requires the knowledge of the eigenvector, which we are trying to find. This can be somewhat circumvented by the use of the
Jacobi–Davidson preconditioner , where
approximates
. Last, but not least, this approach requires accurate numerical solution of linear system with the system matrix
, which becomes as expensive for large problems as the shift-and-invert method above. If the solution is not accurate enough, step two may be redundant.
Practical preconditioning
Let us first replace the theoretical value
in the
Richardson iteration
Modified Richardson iteration is an iterative method for solving a system of linear equations. Richardson iteration was proposed by Lewis Fry Richardson in his work dated 1910. It is similar to the Jacobi and Gauss–Seidel method.
We seek the ...
above with its current approximation
to obtain a practical algorithm
:
A popular choice is
using the
Rayleigh quotient
In mathematics, the Rayleigh quotient () for a given complex Hermitian matrix ''M'' and nonzero vector ''x'' is defined as:
R(M,x) = .
For real matrices and vectors, the condition of being Hermitian reduces to that of being symmetric, and the con ...
function
. Practical preconditioning may be as trivial as just using
or
For some classes of eigenvalue problems the efficiency of
has been demonstrated, both numerically and theoretically. The choice
allows one to easily utilize for eigenvalue problems the vast variety of preconditioners developed for linear systems.
Due to the changing value
, a comprehensive theoretical convergence analysis is much more difficult, compared to the linear systems case, even for the simplest methods, such as the
Richardson iteration
Modified Richardson iteration is an iterative method for solving a system of linear equations. Richardson iteration was proposed by Lewis Fry Richardson in his work dated 1910. It is similar to the Jacobi and Gauss–Seidel method.
We seek the ...
.
External links
Templates for the Solution of Algebraic Eigenvalue Problems: a Practical Guide
Preconditioning in optimization
![gradient descent](https://upload.wikimedia.org/wikipedia/commons/7/79/Gradient_descent.png)
In
optimization
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
, preconditioning is typically used to accelerate
first-order
In mathematics and other formal sciences, first-order or first order most often means either:
* "linear" (a polynomial of degree at most one), as in first-order approximation and other calculus uses, where it is contrasted with "polynomials of high ...
optimization
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
algorithms
In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing c ...
.
Description
For example, to find a
local minimum
In mathematical analysis, the maxima and minima (the respective plurals of maximum and minimum) of a function, known collectively as extrema (the plural of extremum), are the largest and smallest value of the function, either within a given ran ...
of a real-valued function
using
gradient descent
In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the ...
, one takes steps proportional to the ''negative'' of the
gradient
In vector calculus, the gradient of a scalar-valued differentiable function of several variables is the vector field (or vector-valued function) \nabla f whose value at a point p is the "direction and rate of fastest increase". If the gradi ...
(or of the approximate gradient) of the function at the current point:
:
The preconditioner is applied to the gradient:
:
Preconditioning here can be viewed as changing the geometry of the vector space with the goal to make the level sets look like circles.
In this case the preconditioned gradient aims closer to the point of the extrema as on the figure, which speeds up the convergence.
Connection to linear systems
The minimum of a quadratic function
:
,
where
and
are real column-vectors and
is a real
symmetric
Symmetry (from grc, συμμετρία "agreement in dimensions, due proportion, arrangement") in everyday language refers to a sense of harmonious and beautiful proportion and balance. In mathematics, "symmetry" has a more precise definiti ...
positive-definite matrix
In mathematics, a symmetric matrix M with real entries is positive-definite if the real number z^\textsfMz is positive for every nonzero real column vector z, where z^\textsf is the transpose of More generally, a Hermitian matrix (that is, a co ...
, is exactly the solution of the linear equation
. Since
, the preconditioned
gradient descent
In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the ...
method of minimizing
is
:
This is the preconditioned
Richardson iteration
Modified Richardson iteration is an iterative method for solving a system of linear equations. Richardson iteration was proposed by Lewis Fry Richardson in his work dated 1910. It is similar to the Jacobi and Gauss–Seidel method.
We seek the ...
for solving a
system of linear equations
In mathematics, a system of linear equations (or linear system) is a collection of one or more linear equations involving the same variable (math), variables.
For example,
:\begin
3x+2y-z=1\\
2x-2y+4z=-2\\
-x+\fracy-z=0
\end
is a system of three ...
.
Connection to eigenvalue problems
The minimum of the
Rayleigh quotient
In mathematics, the Rayleigh quotient () for a given complex Hermitian matrix ''M'' and nonzero vector ''x'' is defined as:
R(M,x) = .
For real matrices and vectors, the condition of being Hermitian reduces to that of being symmetric, and the con ...
:
where
is a real non-zero column-vector and
is a real
symmetric
Symmetry (from grc, συμμετρία "agreement in dimensions, due proportion, arrangement") in everyday language refers to a sense of harmonious and beautiful proportion and balance. In mathematics, "symmetry" has a more precise definiti ...
positive-definite matrix
In mathematics, a symmetric matrix M with real entries is positive-definite if the real number z^\textsfMz is positive for every nonzero real column vector z, where z^\textsf is the transpose of More generally, a Hermitian matrix (that is, a co ...
, is the smallest
eigenvalue
In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted b ...
of
, while the minimizer is the corresponding
eigenvector
In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted b ...
. Since
is proportional to
, the preconditioned
gradient descent
In mathematics, gradient descent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the ...
method of minimizing
is
:
This is an analog of preconditioned
Richardson iteration
Modified Richardson iteration is an iterative method for solving a system of linear equations. Richardson iteration was proposed by Lewis Fry Richardson in his work dated 1910. It is similar to the Jacobi and Gauss–Seidel method.
We seek the ...
for solving eigenvalue problems.
Variable preconditioning
In many cases, it may be beneficial to change the preconditioner at some or even every step of an
iterative algorithm
In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the ''n''-th approximation is derived from the pre ...
in order to accommodate for a changing shape of the level sets, as in
:
One should have in mind, however, that constructing an efficient preconditioner is very often computationally expensive. The increased cost of updating the preconditioner can easily override the positive effect of faster convergence.
References
Sources
*
*
*
*
* Ke Chen: "Matrix Preconditioning Techniques and Applications", Cambridge University Press, (2005).
{{Numerical linear algebra
Numerical linear algebra