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fsolve

Solve system of nonlinear equations

Description

Nonlinear system solver

Solves a problem specified by

F(x) = 0

for x, where F(x) is a function that returns a vector value.

x is a vector or a matrix; see Matrix Arguments.

x = fsolve(fun,x0) starts at x0 and tries to solve the equations fun(x) = 0, an array of zeros.

Note

Passing Extra Parameters explains how to pass extra parameters to the vector function fun(x), if necessary. See Solve Parameterized Equation.

example

x = fsolve(fun,x0,options) solves the equations with the optimization options specified in options. Use optimoptions to set these options.

example

x = fsolve(problem) solves problem, a structure described in problem.

example

[x,fval] = fsolve(___), for any syntax, returns the value of the objective function fun at the solution x.

example

[x,fval,exitflag,output] = fsolve(___) additionally returns a value exitflag that describes the exit condition of fsolve, and a structure output with information about the optimization process.

example

[x,fval,exitflag,output,jacobian] = fsolve(___) returns the Jacobian of fun at the solution x.

Examples

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This example shows how to solve two nonlinear equations in two variables. The equations are

e-e-(x1+x2)=x2(1+x12)x1cos(x2)+x2sin(x1)=12.

Convert the equations to the form F(x)=0.

e-e-(x1+x2)-x2(1+x12)=0x1cos(x2)+x2sin(x1)-12=0.

The root2d.m function, which is available when you run this example, computes the values.

type root2d.m
function F = root2d(x)

F(1) = exp(-exp(-(x(1)+x(2)))) - x(2)*(1+x(1)^2);
F(2) = x(1)*cos(x(2)) + x(2)*sin(x(1)) - 0.5;

Solve the system of equations starting at the point [0,0].

fun = @root2d;
x0 = [0,0];
x = fsolve(fun,x0)
Equation solved.

fsolve completed because the vector of function values is near zero
as measured by the value of the function tolerance, and
the problem appears regular as measured by the gradient.
x = 1×2

    0.3532    0.6061

Examine the solution process for a nonlinear system.

Set options to have no display and a plot function that displays the first-order optimality, which should converge to 0 as the algorithm iterates.

options = optimoptions('fsolve','Display','none','PlotFcn',@optimplotfirstorderopt);

The equations in the nonlinear system are

e-e-(x1+x2)=x2(1+x12)x1cos(x2)+x2sin(x1)=12.

Convert the equations to the form F(x)=0.

e-e-(x1+x2)-x2(1+x12)=0x1cos(x2)+x2sin(x1)-12=0.

The root2d function computes the left-hand side of these two equations.

type root2d.m
function F = root2d(x)

F(1) = exp(-exp(-(x(1)+x(2)))) - x(2)*(1+x(1)^2);
F(2) = x(1)*cos(x(2)) + x(2)*sin(x(1)) - 0.5;

Solve the nonlinear system starting from the point [0,0] and observe the solution process.

fun = @root2d;
x0 = [0,0];
x = fsolve(fun,x0,options)

Figure Optimization Plot Function contains an axes object. The axes object with title First-order Optimality: 2.02322e-07, xlabel Iteration, ylabel First-order optimality contains an object of type scatter.

x = 1×2

    0.3532    0.6061

You can parameterize equations as described in the topic Passing Extra Parameters. For example, the paramfun helper function at the end of this example creates the following equation system parameterized by c:

2x1+x2=exp(cx1)-x1+2x2=exp(cx2).

To solve the system for a particular value, in this case c=-1, set c in the workspace and create an anonymous function in x from paramfun.

c = -1;
fun = @(x)paramfun(x,c);

Solve the system starting from the point x0 = [0 1].

x0 = [0 1];
x = fsolve(fun,x0)
Equation solved.

fsolve completed because the vector of function values is near zero
as measured by the value of the function tolerance, and
the problem appears regular as measured by the gradient.
x = 1×2

    0.1976    0.4255

To solve for a different value of c, enter c in the workspace and create the fun function again, so it has the new c value.

c = -2;
fun = @(x)paramfun(x,c); % fun now has the new c value
x = fsolve(fun,x0)
Equation solved.

fsolve completed because the vector of function values is near zero
as measured by the value of the function tolerance, and
the problem appears regular as measured by the gradient.
x = 1×2

    0.1788    0.3418

Helper Function

This code creates the paramfun helper function.

function F = paramfun(x,c)
F = [ 2*x(1) + x(2) - exp(c*x(1))
      -x(1) + 2*x(2) - exp(c*x(2))];
end

Create a problem structure for fsolve and solve the problem.

Solve the same problem as in Solution with Nondefault Options, but formulate the problem using a problem structure.

Set options for the problem to have no display and a plot function that displays the first-order optimality, which should converge to 0 as the algorithm iterates.

problem.options = optimoptions('fsolve','Display','none','PlotFcn',@optimplotfirstorderopt);

The equations in the nonlinear system are

e-e-(x1+x2)=x2(1+x12)x1cos(x2)+x2sin(x1)=12.

Convert the equations to the form F(x)=0.

e-e-(x1+x2)-x2(1+x12)=0x1cos(x2)+x2sin(x1)-12=0.

The root2d function computes the left-hand side of these two equations.

type root2d
function F = root2d(x)

F(1) = exp(-exp(-(x(1)+x(2)))) - x(2)*(1+x(1)^2);
F(2) = x(1)*cos(x(2)) + x(2)*sin(x(1)) - 0.5;

Create the remaining fields in the problem structure.

problem.objective = @root2d;
problem.x0 = [0,0];
problem.solver = 'fsolve';

Solve the problem.

x = fsolve(problem)

Figure Optimization Plot Function contains an axes object. The axes object with title First-order Optimality: 2.02322e-07, xlabel Iteration, ylabel First-order optimality contains an object of type scatter.

x = 1×2

    0.3532    0.6061

This example returns the iterative display showing the solution process for the system of two equations and two unknowns

2x1-x2=e-x1-x1+2x2=e-x2.

Rewrite the equations in the form F(x) = 0:

2x1-x2-e-x1=0-x1+2x2-e-x2=0.

Start your search for a solution at x0 = [-5 -5].

First, write a function that computes F, the values of the equations at x.

F = @(x) [2*x(1) - x(2) - exp(-x(1));
         -x(1) + 2*x(2) - exp(-x(2))];

Create the initial point x0.

x0 = [-5;-5];

Set options to return iterative display.

options = optimoptions('fsolve','Display','iter');

Solve the equations.

[x,fval] = fsolve(F,x0,options)
                                             Norm of      First-order   Trust-region
 Iteration  Func-count     ||f(x)||^2           step       optimality         radius
     0          3             47071.2                        2.29e+04              1
     1          6             12003.4              1         5.75e+03              1
     2          9             3147.02              1         1.47e+03              1
     3         12             854.452              1              388              1
     4         15             239.527              1              107              1
     5         18             67.0412              1             30.8              1
     6         21             16.7042              1             9.05              1
     7         24             2.42788              1             2.26              1
     8         27            0.032658       0.759511            0.206            2.5
     9         30         7.03149e-06       0.111927          0.00294            2.5
    10         33         3.29525e-13     0.00169132         6.36e-07            2.5

Equation solved.

fsolve completed because the vector of function values is near zero
as measured by the value of the function tolerance, and
the problem appears regular as measured by the gradient.
x = 2×1

    0.5671
    0.5671

fval = 2×1
10-6 ×

   -0.4059
   -0.4059

The iterative display shows f(x), which is the square of the norm of the function F(x). This value decreases to near zero as the iterations proceed. The first-order optimality measure likewise decreases to near zero as the iterations proceed. These entries show the convergence of the iterations to a solution. For the meanings of the other entries, see Iterative Display.

The fval output gives the function value F(x), which should be zero at a solution (to within the FunctionTolerance tolerance).

Find a matrix X that satisfies

X*X*X=[1234],

starting at the point x0 = [1,1;1,1]. Create an anonymous function that calculates the matrix equation and create the point x0.

fun = @(x)x*x*x - [1,2;3,4];
x0 = ones(2);

Set options to have no display.

options = optimoptions('fsolve','Display','off');

Examine the fsolve outputs to see the solution quality and process.

[x,fval,exitflag,output] = fsolve(fun,x0,options)
x = 2×2

   -0.1291    0.8602
    1.2903    1.1612

fval = 2×2
10-9 ×

   -0.2742    0.1258
    0.1876   -0.0864

exitflag = 
1
output = struct with fields:
       iterations: 11
        funcCount: 52
        algorithm: 'trust-region-dogleg'
    firstorderopt: 4.0197e-10
          message: 'Equation solved....'

The exit flag value 1 indicates that the solution is reliable. To verify this manually, calculate the residual (sum of squares of fval) to see how close it is to zero.

sum(sum(fval.*fval))
ans = 
1.3367e-19

This small residual confirms that x is a solution.

You can see in the output structure how many iterations and function evaluations fsolve performed to find the solution.

Input Arguments

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Nonlinear equations to solve, specified as a function handle or function name. fun is a function that accepts a vector x and returns a vector F, the nonlinear equations evaluated at x. The equations to solve are F = 0 for all components of F. The function fun can be specified as a function handle for a file

x = fsolve(@myfun,x0)

where myfun is a MATLAB® function such as

function F = myfun(x)
F = ...            % Compute function values at x

fun can also be a function handle for an anonymous function.

x = fsolve(@(x)sin(x.*x),x0);

fsolve passes x to your objective function in the shape of the x0 argument. For example, if x0 is a 5-by-3 array, then fsolve passes x to fun as a 5-by-3 array.

If the Jacobian can also be computed and the 'SpecifyObjectiveGradient' option is true, set by

options = optimoptions('fsolve','SpecifyObjectiveGradient',true)

the function fun must return, in a second output argument, the Jacobian value J, a matrix, at x.

If fun returns a vector (matrix) of m components and x has length n, where n is the length of x0, the Jacobian J is an m-by-n matrix where J(i,j) is the partial derivative of F(i) with respect to x(j). (The Jacobian J is the transpose of the gradient of F.)

Example: fun = @(x)x*x*x-[1,2;3,4]

Data Types: char | function_handle | string

Initial point, specified as a real vector or real array. fsolve uses the number of elements in and size of x0 to determine the number and size of variables that fun accepts.

Example: x0 = [1,2,3,4]

Data Types: double

Optimization options, specified as the output of optimoptions or a structure such as optimset returns.

Some options apply to all algorithms, and others are relevant for particular algorithms. See Optimization Options Reference for detailed information.

Some options are absent from the optimoptions display. These options appear in italics in the following table. For details, see View Optimization Options.

All Algorithms
Algorithm

Choose between 'trust-region-dogleg' (default), 'trust-region', and 'levenberg-marquardt'.

The Algorithm option specifies a preference for which algorithm to use. It is only a preference because for the trust-region algorithm, the nonlinear system of equations cannot be underdetermined; that is, the number of equations (the number of elements of F returned by fun) must be at least as many as the length of x. Similarly, for the trust-region-dogleg algorithm, the number of equations must be the same as the length of x. fsolve uses the Levenberg-Marquardt algorithm when the selected algorithm is unavailable. For more information on choosing the algorithm, see Choosing the Algorithm.

To set some algorithm options using optimset instead of optimoptions:

  • Algorithm — Set the algorithm to 'trust-region-reflective' instead of 'trust-region'.

  • InitDamping — Set the initial Levenberg-Marquardt parameter λ by setting Algorithm to a cell array such as {'levenberg-marquardt',.005}.

CheckGradients

Compare user-supplied derivatives (gradients of objective or constraints) to finite-differencing derivatives. The choices are true or the default false.

For optimset, the name is DerivativeCheck and the values are 'on' or 'off'. See Current and Legacy Option Names.

The CheckGradients option will be removed in a future release. To check derivatives, use the checkGradients function.

Diagnostics

Display diagnostic information about the function to be minimized or solved. The choices are 'on' or the default 'off'.

DiffMaxChange

Maximum change in variables for finite-difference gradients (a positive scalar). The default is Inf.

DiffMinChange

Minimum change in variables for finite-difference gradients (a positive scalar). The default is 0.

Display

Level of display (see Iterative Display):

  • 'off' or 'none' displays no output.

  • 'iter' displays output at each iteration, and gives the default exit message.

  • 'iter-detailed' displays output at each iteration, and gives the technical exit message.

  • 'final' (default) displays just the final output, and gives the default exit message.

  • 'final-detailed' displays just the final output, and gives the technical exit message.

FiniteDifferenceStepSize

Scalar or vector step size factor for finite differences. When you set FiniteDifferenceStepSize to a vector v, the forward finite differences delta are

delta = v.*sign′(x).*max(abs(x),TypicalX);

where sign′(x) = sign(x) except sign′(0) = 1. Central finite differences are

delta = v.*max(abs(x),TypicalX);

A scalar FiniteDifferenceStepSize expands to a vector. The default is sqrt(eps) for forward finite differences, and eps^(1/3) for central finite differences.

For optimset, the name is FinDiffRelStep. See Current and Legacy Option Names.

FiniteDifferenceType

Finite differences, used to estimate gradients, are either 'forward' (default), or 'central' (centered). 'central' takes twice as many function evaluations, but should be more accurate.

The algorithm is careful to obey bounds when estimating both types of finite differences. So, for example, it could take a backward, rather than a forward, difference to avoid evaluating at a point outside bounds.

For optimset, the name is FinDiffType. See Current and Legacy Option Names.

FunctionTolerance

Termination tolerance on the function value, a nonnegative scalar. The default is 1e-6. See Tolerances and Stopping Criteria.

For optimset, the name is TolFun. See Current and Legacy Option Names.

FunValCheck

Check whether objective function values are valid. 'on' displays an error when the objective function returns a value that is complex, Inf, or NaN. The default, 'off', displays no error.

MaxFunctionEvaluations

Maximum number of function evaluations allowed, a nonnegative integer. The default is 100*numberOfVariables for the 'trust-region-dogleg' and 'trust-region' algorithms, and 200*numberOfVariables for the 'levenberg-marquardt' algorithm. See Tolerances and Stopping Criteria and Iterations and Function Counts.

For optimset, the name is MaxFunEvals. See Current and Legacy Option Names.

MaxIterations

Maximum number of iterations allowed, a nonnegative integer. The default is 400. See Tolerances and Stopping Criteria and Iterations and Function Counts.

For optimset, the name is MaxIter. See Current and Legacy Option Names.

OptimalityTolerance

Termination tolerance on the first-order optimality (a nonnegative scalar). The default is 1e-6. See First-Order Optimality Measure.

Internally, the 'levenberg-marquardt' algorithm uses an optimality tolerance (stopping criterion) of 1e-4 times FunctionTolerance and does not use OptimalityTolerance.

OutputFcn

Specify one or more user-defined functions that an optimization function calls at each iteration. Pass a function handle or a cell array of function handles. The default is none ([]). See Output Function and Plot Function Syntax.

PlotFcn

Plots various measures of progress while the algorithm executes; select from predefined plots or write your own. Pass a built-in plot function name, a function handle, or a cell array of built-in plot function names or function handles. For custom plot functions, pass function handles. The default is none ([]):

  • 'optimplotx' plots the current point.

  • 'optimplotfunccount' plots the function count.

  • 'optimplotfval' plots the function value.

  • 'optimplotstepsize' plots the step size.

  • 'optimplotfirstorderopt' plots the first-order optimality measure.

Custom plot functions use the same syntax as output functions. See Output Functions for Optimization Toolbox and Output Function and Plot Function Syntax.

For optimset, the name is PlotFcns. See Current and Legacy Option Names.

SpecifyObjectiveGradient

If true, fsolve uses a user-defined Jacobian (defined in fun), or Jacobian information (when using JacobianMultiplyFcn), for the objective function. If false (default), fsolve approximates the Jacobian using finite differences.

For optimset, the name is Jacobian and the values are 'on' or 'off'. See Current and Legacy Option Names.

StepTolerance

Termination tolerance on x, a nonnegative scalar. The default is 1e-6. See Tolerances and Stopping Criteria.

For optimset, the name is TolX. See Current and Legacy Option Names.

TypicalX

Typical x values. The number of elements in TypicalX is equal to the number of elements in x0, the starting point. The default value is ones(numberofvariables,1). fsolve uses TypicalX for scaling finite differences for gradient estimation.

The trust-region-dogleg algorithm uses TypicalX as the diagonal terms of a scaling matrix.

UseParallel

When true, fsolve estimates gradients in parallel. Disable by setting to the default, false. See Parallel Computing.

trust-region Algorithm
JacobianMultiplyFcn

Jacobian multiply function, specified as a function handle. For large-scale structured problems, this function computes the Jacobian matrix product J*Y, J'*Y, or J'*(J*Y) without actually forming J. The function is of the form

W = jmfun(Jinfo,Y,flag)

where Jinfo contains data used to compute J*Y (or J'*Y, or J'*(J*Y)). The first argument Jinfo is the second argument returned by the objective function fun, for example, in

[F,Jinfo] = fun(x)

Y is a matrix that has the same number of rows as there are dimensions in the problem. flag determines which product to compute:

  • If flag == 0, W = J'*(J*Y).

  • If flag > 0, W = J*Y.

  • If flag < 0, W = J'*Y.

In each case, J is not formed explicitly. fsolve uses Jinfo to compute the preconditioner. See Passing Extra Parameters for information on how to supply values for any additional parameters jmfun needs.

Note

'SpecifyObjectiveGradient' must be set to true for fsolve to pass Jinfo from fun to jmfun.

See Minimization with Dense Structured Hessian, Linear Equalities for a similar example.

For optimset, the name is JacobMult. See Current and Legacy Option Names.

JacobPattern

Sparsity pattern of the Jacobian for finite differencing. Set JacobPattern(i,j) = 1 when fun(i) depends on x(j). Otherwise, set JacobPattern(i,j) = 0. In other words, JacobPattern(i,j) = 1 when you can have ∂fun(i)/∂x(j) ≠ 0.

Use JacobPattern when it is inconvenient to compute the Jacobian matrix J in fun, though you can determine (say, by inspection) when fun(i) depends on x(j). fsolve can approximate J via sparse finite differences when you give JacobPattern.

In the worst case, if the structure is unknown, do not set JacobPattern. The default behavior is as if JacobPattern is a dense matrix of ones. Then fsolve computes a full finite-difference approximation in each iteration. This can be very expensive for large problems, so it is usually better to determine the sparsity structure.

MaxPCGIter

Maximum number of PCG (preconditioned conjugate gradient) iterations, a positive scalar. The default is max(1,floor(numberOfVariables/2)). For more information, see Equation Solving Algorithms.

PrecondBandWidth

Upper bandwidth of preconditioner for PCG, a nonnegative integer. The default PrecondBandWidth is Inf, which means a direct factorization (Cholesky) is used rather than the conjugate gradients (CG). The direct factorization is computationally more expensive than CG, but produces a better quality step towards the solution. Set PrecondBandWidth to 0 for diagonal preconditioning (upper bandwidth of 0). For some problems, an intermediate bandwidth reduces the number of PCG iterations.

SubproblemAlgorithm

Determines how the iteration step is calculated. The default, 'factorization', takes a slower but more accurate step than 'cg'. See Trust-Region Algorithm.

TolPCG

Termination tolerance on the PCG iteration, a positive scalar. The default is 0.1.

Levenberg-Marquardt Algorithm
InitDamping

Initial value of the Levenberg-Marquardt parameter, a positive scalar. Default is 1e-2. For details, see Levenberg-Marquardt Method.

ScaleProblem

'jacobian' can sometimes improve the convergence of a poorly scaled problem. The default is 'none'.

Example: options = optimoptions('fsolve','FiniteDifferenceType','central')

Problem structure, specified as a structure with the following fields:

Field NameEntry

objective

Objective function

x0

Initial point for x

solver

'fsolve'

options

Options created with optimoptions

Data Types: struct

Output Arguments

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Solution, returned as a real vector or real array. The size of x is the same as the size of x0. Typically, x is a local solution to the problem when exitflag is positive. For information on the quality of the solution, see When the Solver Succeeds.

Objective function value at the solution, returned as a real vector. Generally, fval = fun(x).

Reason fsolve stopped, returned as an integer.

1

Equation solved. First-order optimality is small.

2

Equation solved. Change in x smaller than the specified tolerance, or Jacobian at x is undefined.

3

Equation solved. Change in residual smaller than the specified tolerance.

4

Equation solved. Magnitude of search direction smaller than specified tolerance.

0

Number of iterations exceeded options.MaxIterations or number of function evaluations exceeded options.MaxFunctionEvaluations.

-1

Output function or plot function stopped the algorithm.

-2

Equation not solved. The exit message can have more information.

-3

Equation not solved. Trust region radius became too small (trust-region-dogleg algorithm).

Information about the optimization process, returned as a structure with fields:

iterations

Number of iterations taken

funcCount

Number of function evaluations

algorithm

Optimization algorithm used

cgiterations

Total number of PCG iterations ('trust-region' algorithm only)

stepsize

Final displacement in x (not in 'trust-region-dogleg')

firstorderopt

Measure of first-order optimality

message

Exit message

Jacobian at the solution, returned as a real matrix. jacobian(i,j) is the partial derivative of fun(i) with respect to x(j) at the solution x.

For problems with active constraints at the solution, jacobian is not useful for estimating confidence intervals.

Limitations

  • The function to be solved must be continuous.

  • When successful, fsolve only gives one root.

  • The default trust-region dogleg method can only be used when the system of equations is square, i.e., the number of equations equals the number of unknowns. For the Levenberg-Marquardt method, the system of equations need not be square.

More About

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Enhanced Exit Messages

The next few items list the possible enhanced exit messages from fsolve. Enhanced exit messages give a link for more information as the first sentence of the message.

Equation Solved

The solver found a point where the sum of squares of function values is less than the square root of the FunctionTolerance tolerance. The gradient of the sum of squares is also less than OptimalityTolerance (1e-4*OptimalityTolerance for the Levenberg-Marquardt algorithm).

For suggestions on how to proceed, see When the Solver Succeeds.

Equation Solved at Initial Point

The initial point seems to be a solution of the equation, because the sum of squares of function values is less than the square root of the FunctionTolerance tolerance. The size of the gradient of the sum of squares is also less than OptimalityTolerance (1e-4*OptimalityTolerance for the Levenberg-Marquardt algorithm).

For suggestions on how to proceed, see Final Point Equals Initial Point.

Equation Solved, Solver Stalled

The solver found a point where the sum of squares of function values is less than the square root of the FunctionTolerance tolerance. However, the last step was less than the StepTolerance tolerance, indicating the function may be changing rapidly, or that the function is not smooth near the final point. This is the meaning of stalled.

For suggestions on how to proceed, see Local Minimum Possible.

Equation Solved, Inaccuracy Possible

The solver found a point where the sum of squares of function values is less than the square root of the FunctionTolerance tolerance. However, the sum of squares changed very little in the last step, even though the gradient of the sum was larger than OptimalityTolerance (1e-4*OptimalityTolerance for the Levenberg-Marquardt algorithm). This can indicate that the reported point is not near a solution.

For suggestions on how to proceed, see Local Minimum Possible.

No Solution Found

The solver is unable to further reduce the sum of squares of function values, but this sum exceeds the square root of the FunctionTolerance tolerance.

For suggestions on how to proceed, see fsolve Could Not Solve Equation.

Definitions for Exit Messages

The next few items contain definitions for terms in the fsolve exit messages.

Solution method

To solve a system of equations F(x) = 0, the solver generally attempts to minimize the sum of squared function values r = Σ(Fi(x))2. Both r and ∇r should be zero at a solution.

tolerance

Generally, a tolerance is a threshold which, if crossed, stops the iterations of a solver. For more information on tolerances, see Tolerances and Stopping Criteria.

OptimalityTolerance

The tolerance called OptimalityTolerance relates to the first-order optimality measure. Iterations end when the first-order optimality measure is less than OptimalityTolerance.

The first-order optimality measure is the size of the gradient of the sum of squares of function values. This should be zero at the root of a smooth function.

FunctionTolerance

The function tolerance called FunctionTolerance relates to the size of the latest change in sum of squares of function values.

StepTolerance

StepTolerance is a tolerance for the size of the last step, meaning the size of the change in location where fsolve was evaluated.

Appears to be Regular

The problem appears to be regular means the size of the gradient of the sum of squares of function values is less than the OptimalityTolerance tolerance (1e-4*OptimalityTolerance for the Levenberg-Marquardt algorithm).

Last Step Was Ineffective

The solver was unable to reduce the sum of squares of function values to below the square root of the FunctionTolerance tolerance. Its last iteration did not reduce the sum of squares enough to warrant further attempts.

For suggestions on how to proceed, see fsolve Could Not Solve Equation.

Locally Singular

The trust region is too small to continue. This could be because the sum of squares of function values is not close to a quadratic model. For more information, see Trust-Region-Dogleg Algorithm.

Trust-Region Radius

The trust region is too small to continue. This could be because the sum of squares of function values is not close to a quadratic model. For more information, see Trust-Region-Dogleg Algorithm.

Regularization Parameter

The Levenberg-Marquardt regularization parameter is related to the inverse of a trust-region radius. It becomes large when the sum of squares of function values is not close to a quadratic model. For more information, see Levenberg-Marquardt Method.

Tips

  • For large problems, meaning those with thousands of variables or more, save memory (and possibly save time) by setting the Algorithm option to 'trust-region' and the SubproblemAlgorithm option to 'cg'.

Algorithms

The Levenberg-Marquardt and trust-region methods are based on the nonlinear least-squares algorithms also used in lsqnonlin. Use one of these methods if the system may not have a zero. The algorithm still returns a point where the residual is small. However, if the Jacobian of the system is singular, the algorithm might converge to a point that is not a solution of the system of equations (see Limitations).

  • By default fsolve chooses the trust-region dogleg algorithm. The algorithm is a variant of the Powell dogleg method described in [8]. It is similar in nature to the algorithm implemented in [7]. See Trust-Region-Dogleg Algorithm.

  • The trust-region algorithm is a subspace trust-region method and is based on the interior-reflective Newton method described in [1] and [2]. Each iteration involves the approximate solution of a large linear system using the method of preconditioned conjugate gradients (PCG). See Trust-Region Algorithm.

  • The Levenberg-Marquardt method is described in references [4], [5], and [6]. See Levenberg-Marquardt Method.

Alternative Functionality

App

The Optimize Live Editor task provides a visual interface for fsolve.

References

[1] Coleman, T.F. and Y. Li, “An Interior, Trust Region Approach for Nonlinear Minimization Subject to Bounds,” SIAM Journal on Optimization, Vol. 6, pp. 418-445, 1996.

[2] Coleman, T.F. and Y. Li, “On the Convergence of Reflective Newton Methods for Large-Scale Nonlinear Minimization Subject to Bounds,” Mathematical Programming, Vol. 67, Number 2, pp. 189-224, 1994.

[3] Dennis, J. E. Jr., “Nonlinear Least-Squares,” State of the Art in Numerical Analysis, ed. D. Jacobs, Academic Press, pp. 269-312.

[4] Levenberg, K., “A Method for the Solution of Certain Problems in Least-Squares,” Quarterly Applied Mathematics 2, pp. 164-168, 1944.

[5] Marquardt, D., “An Algorithm for Least-squares Estimation of Nonlinear Parameters,” SIAM Journal Applied Mathematics, Vol. 11, pp. 431-441, 1963.

[6] Moré, J. J., “The Levenberg-Marquardt Algorithm: Implementation and Theory,” Numerical Analysis, ed. G. A. Watson, Lecture Notes in Mathematics 630, Springer Verlag, pp. 105-116, 1977.

[7] Moré, J. J., B. S. Garbow, and K. E. Hillstrom, User Guide for MINPACK 1, Argonne National Laboratory, Rept. ANL-80-74, 1980.

[8] Powell, M. J. D., “A Fortran Subroutine for Solving Systems of Nonlinear Algebraic Equations,” Numerical Methods for Nonlinear Algebraic Equations, P. Rabinowitz, ed., Ch.7, 1970.

Extended Capabilities

Version History

Introduced before R2006a

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