# pdenonlin

(Not recommended) Solve nonlinear elliptic PDE problem

`pdenonlin`

is not recommended. Use `solvepde`

instead.

## Syntax

## Description

solves the nonlinear PDE`u`

= pdenonlin(`model`

,`c`

,`a`

,`f`

)

$$-\nabla \cdot \left(c\nabla u\right)+au=f$$

with geometry, boundary conditions, and finite element
mesh in `model`

, and coefficients
`c`

, `a`

, and
`f`

. In this context,
“nonlinear” means some coefficient in
`c`

, `a`

, or
`f`

depends on the solution
`u`

or its gradient. If the PDE
is a system of equations
(`model.PDESystemSize`

> 1),
then `pdenonlin`

solves the
system of equations

$$-\nabla \cdot \left(c\otimes \nabla u\right)+au=f$$

,
for any previous arguments, modifies the solution
process with `u`

= pdenonlin(___,`Name,Value`

)`Name`

,
`Value`

pairs.

## Examples

### Minimal Surface Problem

Solve a minimal surface problem. Because this problem has a nonlinear `c`

coefficient, use `pdenonlin`

to solve it.

Create a model and include circular geometry using the built-in `circleg`

function.

model = createpde; geometryFromEdges(model,@circleg);

Set the coefficients.

```
a = 0;
f = 0;
c = '1./sqrt(1+ux.^2+uy.^2)';
```

Set a Dirichlet boundary condition with value $${x}^{2}$$.

boundaryfun = @(region,state)region.x.^2; applyBoundaryCondition(model,'edge',1:model.Geometry.NumEdges,... 'u',boundaryfun,'Vectorized','on');

Generate a mesh and solve the problem.

generateMesh(model,'GeometricOrder','linear','Hmax',0.1); u = pdenonlin(model,c,a,f); pdeplot(model,'XYData',u,'ZData',u)

### Minimal Surface Problem Using [p,e,t] Mesh

Solve the minimal surface problem using the legacy approach for creating boundary conditions and geometry.

Create the geometry using the built-in `circleg`

function. Plot the geometry to see the edge labels.

g = @circleg; pdegplot(g,'EdgeLabels','on') axis equal

Create Dirichlet boundary conditions with value $${x}^{2}$$.Create the following file and save it on your MATLAB path.

function [qmatrix,gmatrix,hmatrix,rmatrix] = pdex2bound(p,e,u,time)

ne = size(e,2); % number of edges qmatrix = zeros(1,ne); gmatrix = qmatrix; hmatrix = zeros(1,2*ne); rmatrix = hmatrix;

for k = 1:ne x1 = p(1,e(1,k)); % x at first point in segment x2 = p(1,e(2,k)); % x at second point in segment xm = (x1 + x2)/2; % x at segment midpoint y1 = p(2,e(1,k)); % y at first point in segment y2 = p(2,e(2,k)); % y at second point in segment ym = (y1 + y2)/2; % y at segment midpoint switch e(5,k) case {1,2,3,4} hmatrix(k) = 1; hmatrix(k+ne) = 1; rmatrix(k) = x1^2; rmatrix(k+ne) = x2^2; end end

Set the coefficients and boundary conditions.

```
a = 0;
f = 0;
c = '1./sqrt(1+ux.^2+uy.^2)';
b = @pdex2bound;
```

Generate a mesh and solve the problem.

[p,e,t] = initmesh(g,'Hmax',0.1); u = pdenonlin(b,p,e,t,c,a,f); pdeplot(p,e,t,'XYData',u,'ZData',u)

### Nonlinear Problem with 3-D Geometry

Solve a nonlinear 3-D problem with nontrivial geometry.

Import the geometry from the `BracketWithHole.stl`

file. Plot the geometry and face labels.

model = createpde(); importGeometry(model,'BracketWithHole.stl'); figure pdegplot(model,'FaceLabels','on','FaceAlpha',0.5) view(30,30) title('Bracket with Face Labels')

figure pdegplot(model,'FaceLabels','on','FaceAlpha',0.5) view(-134,-32) title('Bracket with Face Labels, Rear View')

Set a Dirichlet boundary condition with value 1000 on the back face, which is face 4. Set the large faces 1 and 7, and also the circular face 11, to have Neumann boundary conditions with value `g = -10`

. Do not set boundary conditions on the other faces. Those faces default to Neumann boundary conditions with value `g = 0`

.

applyBoundaryCondition(model,'Face',4,'u',1000); applyBoundaryCondition(model,'Face',[1,7,11],'g',-10);

Set the `c`

coefficient to 1, `f`

to 0.1, and `a`

to the nonlinear value `'0.1 + 0.001*u.^2'`

.

```
c = 1;
f = 0.1;
a = '0.1 + 0.001*u.^2';
```

Generate the mesh and solve the PDE. Start from the initial guess `u0 = 1000`

, which matches the value you set on face 4. Turn on the `Report`

option to observe the convergence during the solution.

generateMesh(model); u = pdenonlin(model,c,a,f,'U0',1000,'Report','on');

Iteration Residual Step size Jacobian: full 0 7.2059e-01 1 1.3755e-01 1.0000000 2 4.0800e-02 1.0000000 3 1.1344e-02 1.0000000 4 2.2737e-03 1.0000000 5 1.7770e-04 1.0000000 6 1.4199e-06 1.0000000

Plot the solution on the geometry boundary.

`pdeplot3D(model,'ColorMapData',u)`

## Input Arguments

`model`

— PDE model

`PDEModel`

object

PDE model, specified as a `PDEModel`

object.

**Example: **`model = createpde`

`c`

— PDE coefficient

scalar | matrix | character vector | character array | string scalar | string vector | coefficient function

PDE coefficient, specified as a scalar, matrix, character vector, character array, string
scalar, string vector, or coefficient function. `c`

represents the *c* coefficient in the scalar PDE

$$-\nabla \cdot \left(c\nabla u\right)+au=f$$

or in the system of PDEs

$$-\nabla \cdot \left(c\otimes \nabla u\right)+au=f$$

**Example: **`'cosh(x+y.^2)'`

**Data Types: **`double`

| `char`

| `string`

| `function_handle`

**Complex Number Support: **Yes

`a`

— PDE coefficient

scalar | matrix | character vector | character array | string scalar | string vector | coefficient function

PDE coefficient, specified as a scalar, matrix, character vector, character array, string
scalar, string vector, or coefficient function. `a`

represents the
*a* coefficient in the scalar PDE

$$-\nabla \cdot \left(c\nabla u\right)+au=f$$

or in the system of PDEs

$$-\nabla \cdot \left(c\otimes \nabla u\right)+au=f$$

**Example: **`2*eye(3)`

**Data Types: **`double`

| `char`

| `string`

| `function_handle`

**Complex Number Support: **Yes

`f`

— PDE coefficient

scalar | matrix | character vector | character array | string scalar | string vector | coefficient function

PDE coefficient, specified as a scalar, matrix, character vector, character array, string
scalar, string vector, or coefficient function. `f`

represents the *f* coefficient in the scalar PDE

$$-\nabla \cdot \left(c\nabla u\right)+au=f$$

or in the system of PDEs

$$-\nabla \cdot \left(c\otimes \nabla u\right)+au=f$$

**Example: **`char('sin(x)';'cos(y)';'tan(z)')`

**Data Types: **`double`

| `char`

| `string`

| `function_handle`

**Complex Number Support: **Yes

`b`

— Boundary conditions

boundary matrix | boundary file

Boundary conditions, specified as a boundary matrix or boundary file. Pass a boundary file as a function handle or as a file name. A boundary matrix is generally an export from the PDE Modeler app.

**Example: **`b = 'circleb1'`

, `b = "circleb1"`

, or ```
b =
@circleb1
```

**Data Types: **`double`

| `char`

| `string`

| `function_handle`

`p`

— Mesh points

matrix

Mesh points, specified as a 2-by-`Np`

matrix of points, where
`Np`

is the number of points in the mesh. For a description of the
(`p`

,`e`

,`t`

) matrices, see Mesh Data as [p,e,t] Triples.

Typically, you use the `p`

, `e`

, and `t`

data exported from the **PDE Modeler** app, or generated by `initmesh`

or `refinemesh`

.

**Example: **`[p,e,t] = initmesh(gd)`

**Data Types: **`double`

`e`

— Mesh edges

matrix

Mesh edges, specified as a `7`

-by-`Ne`

matrix of edges,
where `Ne`

is the number of edges in the mesh. For a description of the
(`p`

,`e`

,`t`

) matrices, see Mesh Data as [p,e,t] Triples.

Typically, you use the `p`

, `e`

, and `t`

data exported from the **PDE Modeler** app, or generated by `initmesh`

or `refinemesh`

.

**Example: **`[p,e,t] = initmesh(gd)`

**Data Types: **`double`

`t`

— Mesh triangles

matrix

Mesh triangles, specified as a `4`

-by-`Nt`

matrix of
triangles, where `Nt`

is the number of triangles in the mesh. For a
description of the (`p`

,`e`

,`t`

)
matrices, see Mesh Data as [p,e,t] Triples.

Typically, you use the `p`

, `e`

, and `t`

data exported from the **PDE Modeler** app, or generated by `initmesh`

or `refinemesh`

.

**Example: **`[p,e,t] = initmesh(gd)`

**Data Types: **`double`

### Name-Value Arguments

Specify optional
comma-separated pairs of `Name,Value`

arguments. `Name`

is
the argument name and `Value`

is the corresponding value.
`Name`

must appear inside quotes. You can specify several name and value
pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

**Example:**

`'Jacobian','full'`

`Jacobian`

— Approximation of Jacobian

`'full'`

(3-D
default) | `'fixed'`

(2-D
default) | `'lumped'`

Approximation of Jacobian, specified as
`'full'`

,
`'fixed'`

, or
`'lumped'`

.

`'full'`

means numerical evaluation of the full Jacobian based on the sparse version of the`numjac`

function. 3-D geometry uses only`'full'`

, any other specification yields an error.`'fixed'`

specifies a fixed-point iteration matrix where the Jacobian is approximated by the stiffness matrix. This is the 2-D geometry default.`'lumped'`

specifies a “lumped” approximation as described in Nonlinear Equations. This approximation is based on the numerical differentiation of the coefficients.

**Example: **```
u =
pdenonlin(model,c,a,f,'Jacobian','full')
```

**Data Types: **`char`

| `string`

`U0`

— Initial solution guess

0 (default) | scalar | vector of characters | vector of numbers

Initial solution guess, specified as a scalar, a vector of characters, or a vector of numbers. A scalar specifies a constant initial condition for either a scalar or PDE system.

For systems of *N*
equations, and a mesh with `Np`

nodes, give a column vector with
*N**`Np`

components. The nodes are either
`model.Mesh.Nodes`

, or the `p`

data from
`initmesh`

or `meshToPet`

. See Mesh Data as [p,e,t] Triples.

The first
`N`

elements contain the values of component 1, where
the value of element _{p}`k`

corresponds to node `p(k)`

. The
next `N`

points contain the values of component 2, etc. It
can be convenient to first represent the initial
conditions _{p}`u0`

as an
`N`

-by-_{p}`N`

matrix, where the first column contains entries
for component 1, the second column contains
entries for component 2, etc. The final
representation of the initial conditions is
`u0(:)`

.

**Example: **```
u =
pdenonlin(model,c,a,f,'U0','x.^2-y.^2')
```

**Data Types: **`double`

| `char`

| `string`

**Complex Number Support: **Yes

`Tol`

— Residual size at termination

1e-4 (default) | positive scalar

Residual size at termination, specified as a
positive scalar. `pdenonlin`

iterates until the residual size is less than
`'Tol'`

.

**Example: **```
u =
pdenonlin(model,c,a,f,'Tol',1e-6)
```

**Data Types: **`double`

`MaxIter`

— Maximum number of Gauss-Newton iterations

`25`

(default) | positive integer

Maximum number of Gauss-Newton iterations, specified as a positive integer.

**Example: **```
u =
pdenonlin(model,c,a,f,'MaxIter',12)
```

**Data Types: **`double`

`MinStep`

— Minimum damping of search direction

`1/2^16`

(default) | positive scalar

Minimum damping of search direction, specified as a positive scalar.

**Example: **```
u =
pdenonlin(model,c,a,f,'MinStep',1e-3)
```

**Data Types: **`double`

`Report`

— Print convergence information

`'off'`

(default) | `'on'`

Print convergence information, specified as
`'off'`

or
`'on'`

.

**Example: **```
u =
pdenonlin(model,c,a,f,'Report','on')
```

**Data Types: **`char`

| `string`

`Norm`

— Residual norm

`Inf`

(default) | p value for L^{p}
norm | `'energy'`

Residual norm, specified as the
`p`

value for
L^{p} norm, or as
`'energy'`

. `p`

can be any positive real value,
`Inf`

, or
`-Inf`

. The `p`

norm of a vector `v`

is
`sum(abs(v)^p)^(1/p)`

. See
`norm`

.

**Example: **```
u =
pdenonlin(model,c,a,f,'Norm',2)
```

**Data Types: **`double`

| `char`

| `string`

## Output Arguments

`u`

— PDE solution

vector

PDE solution, returned as a vector.

If the PDE is scalar, meaning only one equation, then

`u`

is a column vector representing the solution*u*at each node in the mesh.`u(i)`

is the solution at the`i`

th column of`model.Mesh.Nodes`

or the`i`

th column of`p`

.If the PDE is a system of

*N*> 1 equations, then`u`

is a column vector with*N**`Np`

elements, where`Np`

is the number of nodes in the mesh. The first`Np`

elements of`u`

represent the solution of equation 1, then next`Np`

elements represent the solution of equation 2, etc.

To obtain the solution at an arbitrary point in the geometry,
use `pdeInterpolant`

.

To plot the solution, use `pdeplot`

for
2-D geometry, or see 3-D Solution and Gradient Plots with MATLAB® Functions.

`res`

— Norm of Newton step residuals

scalar

Norm of Newton step residuals, returned as a scalar. For information about the algorithm, see Nonlinear Equations.

## Tips

If the Newton iteration does not converge,

`pdenonlin`

displays the error message`Too many iterations`

or`Stepsize too small`

.If the initial guess produces matrices containing

`NaN`

or`Inf`

elements,`pdenonlin`

displays the error message`Unsuitable initial guess U0 (default: U0 = 0)`

.If you have very small coefficients, or very small geometric dimensions,

`pdenonlin`

can fail to converge, or can converge to an incorrect solution. If so, you can sometimes obtain better results by scaling the coefficients or geometry dimensions to be of order one.

## Algorithms

### Nonlinear Equations

The basic idea is to use Gauss-Newton iterations to solve the nonlinear equations. Say you are trying to solve the equation

*r*(*u*) = –∇ ·
(*c*(*u*)∇*u*) +
*a*(*u*)*u* -
*f*(*u*) = 0.

In the FEM setting you solve the weak form of *r*(*u*) =
0. Set as usual

$$u(x)={\displaystyle \sum {U}_{j}}{\varphi}_{j}$$

where **x** represents a 2-D or 3-D point. Then multiply the
equation by an arbitrary test function *ϕ _{i}*,
integrate on the domain Ω, and use Green's formula and the boundary conditions to
obtain

$$\begin{array}{l}0=\rho \left(U\right)={\displaystyle \sum _{j}({\displaystyle \underset{\Omega}{\int}\left(\left(c\left(x,U\right)\nabla {\varphi}_{j}(x)\right)\text{\hspace{0.17em}}\cdot \text{\hspace{0.17em}}\nabla {\varphi}_{j}(x)+a\left(x,U\right){\varphi}_{j}(x){\varphi}_{i}(x)\text{\hspace{0.17em}}\right)dx}}\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}+{\displaystyle \underset{\partial \Omega}{\int}q\left(x,U\right){\varphi}_{j}(x){\varphi}_{i}(x)\text{\hspace{0.17em}}ds}){U}_{j}\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}-{\displaystyle \underset{\Omega}{\int}f\left(x,U\right){\varphi}_{i}(x)\text{\hspace{0.17em}}dx}-{\displaystyle \underset{\partial \Omega}{\int}g\left(x,U\right){\varphi}_{i}(x)\text{\hspace{0.17em}}ds}\end{array}$$

which has to hold for all indices *i*.

The residual vector *ρ*(*U*) can be easily computed
as

*ρ*(*U*) = (*K* +
*M* + *Q*)*U* –
(*F* + *G*)

where the matrices *K*, *M*, *Q* and
the vectors *F* and *G* are produced by assembling the
problem

–∇ · (*c*(*U*)∇*u*) +
*a*(*U*)*u* =
*f*(*U*).

Assume that you have a guess
*U*^{(n)} of the solution.
If *U*^{(n)} is close enough to
the exact solution, an improved approximation
*U*^{(n+1)} is obtained
by solving the linearized problem

$$\frac{\partial \rho \left({U}^{(n)}\right)}{\partial U}\left({U}^{(n+1)}-{U}^{(n)}\right)=-\alpha \rho \left({U}^{(n)}\right)$$

where $$\alpha $$ is a positive number. (It is not necessary that *ρ*(*U*) = 0 have a solution even if *ρ*(*u*) = 0 has.) In this case, the Gauss-Newton iteration tends to be the minimizer
of the residual, i.e., the solution of min_{U}
$$\Vert \rho (U)\Vert $$.

It is well known that for sufficiently small $$\alpha $$

$$\Vert \rho \left({U}^{(n+1)}\right)\Vert <\Vert \rho \left({U}^{(n)}\right)\Vert $$

and

$${p}_{n}={\left(\frac{\partial \rho \left({U}^{(n)}\right)}{\partial U}\right)}^{-1}\rho \left({U}^{(n)}\right)$$

is called a descent direction for $$\Vert \rho (U)\Vert $$, where $$\Vert \cdot \Vert $$ is the *L*_{2}-norm. The iteration is

$${U}^{(n+1)}={U}^{(n)}+\alpha {p}_{n},$$

where $$\alpha $$ ≤ 1 is chosen as large as possible such that the step has a reasonable descent.

The *Gauss-Newton method* is local, and convergence is assured only
when *U*^{(0)} is close enough to the solution. In
general, the first guess may be outside the region of convergence. To improve convergence
from bad initial guesses, a *damping* strategy is implemented for
choosing α, the *Armijo-Goldstein line search*. It chooses the largest
damping coefficient α out of the sequence 1, 1/2, 1/4, . . . such that the following
inequality holds:

$$\Vert \rho \left({U}^{(n)}\right)\Vert -\Vert \rho \left({U}^{(n)}\right)+\alpha {p}_{n}\Vert \ge \frac{\alpha}{2}\Vert \rho \left({U}^{(n)}\right)\Vert $$

which guarantees a reduction of the residual norm by at least 1 – $$\alpha $$/2. Each step of the line-search algorithm requires an evaluation of the residual $$\rho \left({U}^{(n)}+\alpha {p}_{n}\right)$$.

An important point of this strategy is that when
*U*^{(n)} approaches the
solution, then $$\alpha $$→1 and thus the convergence rate increases. If there is a solution to *ρ*(*U*) = 0, the scheme ultimately recovers the quadratic convergence rate of the
standard Newton iteration.

Closely related to the preceding problem is the choice of the initial guess
*U*^{(0)}. By default, the solver sets
*U*^{(0)} and then assembles the FEM matrices
*K* and *F* and computes

*U*^{(1)} =
*K*^{–1}*F*

The damped Gauss-Newton iteration is then started with
*U*^{(1)}, which should be a better guess than
*U*^{(0)}. If the boundary conditions do not
depend on the solution *u*, then
*U*^{(1)} satisfies them even if
*U*^{(0)} does not. Furthermore, if the
equation is linear, then *U*^{(1)} is the exact FEM
solution and the solver does not enter the Gauss-Newton loop.

There are situations where *U*^{(0)} = 0 makes no sense or convergence is impossible.

In some situations you may already have a good approximation and the nonlinear solver can be started with it, avoiding the slow convergence regime. This idea is used in the adaptive mesh generator. It computes a solution $$\tilde{U}$$ on a mesh, evaluates the error, and may refine certain triangles. The interpolant of $$\tilde{U}$$ is a very good starting guess for the solution on the refined mesh.

In general the exact Jacobian

$${J}_{n}=\frac{\partial \rho \left({U}^{(n)}\right)}{\partial U}$$

is not available. Approximation of *J _{n}* by finite
differences in the following way is expensive but feasible. The

*i*th column of

*J*can be approximated by

_{n}$$\frac{\rho \left({U}^{(n)}+\epsilon {\varphi}_{i}\right)-\rho \left({U}^{(n)}\right)}{\epsilon}$$

which implies the assembling of the FEM matrices for the triangles containing grid point
*i*. A very simple approximation to
*J _{n}*, which gives a fixed point iteration, is
also possible as follows. Essentially, for a given

*U*

^{(n)}, compute the FEM matrices

*K*and

*F*and set

*U*^{(n+1)} =
*K*^{–1}*F *.

This is equivalent to approximating the Jacobian with the stiffness matrix. Indeed, since
*ρ*(*U*^{(n)})
= *KU*^{(n)} –
*F*, putting *J _{n}* =

*K*yields

$${U}^{(n+1)}={U}^{(n)}-{J}_{n}^{-1}\rho \left({U}^{(n)}\right)={U}^{(n)}-{K}^{-1}\left(K{U}^{(n)}-F\right)={K}^{-1}F$$

In many cases the convergence rate is slow, but the cost of each iteration is cheap.

The Partial Differential Equation Toolbox™ nonlinear solver also provides for a compromise between the two extremes. To
compute the derivative of the mapping *U*→*KU*, proceed as
follows. The *a* term has been omitted for clarity, but appears again in
the final result.

$$\begin{array}{c}\frac{\partial {\left(KU\right)}_{i}}{\partial {U}_{j}}=\underset{\epsilon \to 0}{\mathrm{lim}}\frac{1}{\epsilon}{\displaystyle \sum _{l}({\displaystyle \underset{\Omega}{\int}c\left(U+\epsilon {\varphi}_{j}\right)\nabla {\varphi}_{l}\nabla {\varphi}_{i}\text{\hspace{0.17em}}dx\left({U}_{l}+\epsilon {\delta}_{l,j}\right)}}\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}-{\displaystyle \underset{\Omega}{\int}c\left(U\right)\nabla {\varphi}_{l}\nabla {\varphi}_{i}\text{\hspace{0.17em}}dx{U}_{l}})\\ ={\displaystyle \underset{\Omega}{\int}c\left(U\right)\nabla {\varphi}_{j}\nabla {\varphi}_{i}\text{\hspace{0.17em}}dx}+{\displaystyle \sum _{l}{\displaystyle \underset{\Omega}{\int}{\varphi}_{j}\frac{\partial c}{\partial u}\nabla {\varphi}_{l}\nabla {\varphi}_{i}\text{\hspace{0.17em}}dx{U}_{l}}}\end{array}$$

The first integral term is nothing more than
*K _{i,j}*.

The second term is “lumped,” i.e., replaced by a diagonal matrix that contains the row
sums. Since
Σ_{j}*ϕ _{j}*
= 1, the second term is approximated by

$${\delta}_{i,j}{\displaystyle \sum _{l}{\displaystyle \underset{\Omega}{\int}\frac{\partial c}{\partial u}\nabla {\varphi}_{l}\nabla {\varphi}_{i}\text{\hspace{0.17em}}dx{U}_{l}}}$$

which is the *i*th component of
*K*^{(c')}*U*,
where *K*^{(c')} is the
stiffness matrix associated with the coefficient ∂*c*/∂*u*
rather than *c*. The same reasoning can be applied to the derivative of the
mapping *U*→*MU*. The derivative of the mapping
*U*→ –*F* is exactly

$$-{\displaystyle \underset{\Omega}{\int}\frac{\partial f}{\partial u}{\varphi}_{i}{\varphi}_{j}\text{\hspace{0.17em}}dx}$$

which is the mass matrix associated with the coefficient
∂*f*/∂*u*. Thus the Jacobian of the residual
*ρ*(*U*) is approximated by

$$J={K}^{(c)}+{M}^{(a-{f}^{\prime})}+\text{diag}\left(\left({K}^{({c}^{\prime})}+{M}^{({a}^{\prime})}\right)U\right)$$

where the differentiation is with respect to *u*, *K*
and *M* designate stiffness and mass matrices, and their indices designate
the coefficients with respect to which they are assembled. At each Gauss-Newton iteration,
the nonlinear solver assembles the matrices corresponding to the equations

$$\begin{array}{l}-\nabla \cdot (c\nabla u)+(a-f\text{'})u=0\\ -\nabla \cdot (c\text{'}\nabla u)+a\text{'}u=0\end{array}$$

and then produces the approximate Jacobian. The differentiations of the coefficients are done numerically.

In the general setting of elliptic systems, the boundary conditions are appended to the stiffness matrix to form the full linear system:

$$\tilde{K}\tilde{U}=\left[\begin{array}{cc}K& {H}^{\prime}\\ H& 0\end{array}\right]\left[\begin{array}{c}U\\ \mu \end{array}\right]=\left[\begin{array}{c}F\\ R\end{array}\right]=\tilde{F}$$

where the coefficients of $$\tilde{K}$$ and $$\tilde{F}$$ may depend on the solution $$\tilde{U}$$. The “lumped” approach approximates the derivative mapping of the residual by

$$\left[\begin{array}{cc}J& {H}^{\prime}\\ H& 0\end{array}\right]$$

The nonlinearities of the boundary conditions and the dependencies of the coefficients on
the derivatives of $$\tilde{U}$$ are not properly linearized by this scheme. When such nonlinearities are
strong, the scheme reduces to the fix-point iteration and may converge slowly or not at all.
When the boundary conditions are linear, they do not affect the convergence properties of
the iteration schemes. In the Neumann case they are invisible (*H* is an
empty matrix) and in the Dirichlet case they merely state that the residual is zero on the
corresponding boundary points.

## See Also

**Introduced before R2006a**

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