Solve nonlinear least-squares (nonlinear data-fitting) problems
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Syntax
x = lsqnonlin(fun,x0)
x = lsqnonlin(fun,x0,lb,ub)
x = lsqnonlin(fun,x0,lb,ub,A,b,Aeq,beq)
x = lsqnonlin(fun,x0,lb,ub,A,b,Aeq,beq,nonlcon)
x = lsqnonlin(fun,x0,lb,ub,options)
x = lsqnonlin(fun,x0,lb,ub,A,b,Aeq,beq,nonlcon,options)
x = lsqnonlin(problem)
[x,resnorm]= lsqnonlin(___)
[x,resnorm,residual,exitflag,output]= lsqnonlin(___)
[x,resnorm,residual,exitflag,output,lambda,jacobian]= lsqnonlin(___)
Description
Nonlinear least-squares solver
Solves nonlinear least-squares curve fitting problems of the form
subject to the constraints
x, lb
, and ub
can be vectors or matrices; see Matrix Arguments.
Do not specify the objective function as the scalar value (the sum of squares). lsqnonlin
requires the objective function to be the vector-valued function
example
x = lsqnonlin(fun,x0)
starts at the point x0
and finds a minimum of the sum of squares of the functions described in fun
. The function fun
should return a vector (or array) of values and not the sum of squares of the values. (The algorithm implicitly computes the sum of squares of the components of fun(x)
.)
Note
Passing Extra Parameters explains how to pass extra parameters to the vector function fun(x)
, if necessary.
example
x = lsqnonlin(fun,x0,lb,ub)
defines a set of lower and upper bounds on the design variables in x
, so that the solution is always in the range lb
≤x
≤ub
. You can fix the solution component x(i)
by specifying lb(i)=ub(i)
.
Note
If the specified input bounds for a problem are inconsistent, the output x
is x0
and the outputs resnorm
and residual
are []
.
Components of x0
that violate the bounds lb≤x≤ub
are reset to the interior of the box defined by the bounds. Components that respect the bounds are not changed.
example
x = lsqnonlin(fun,x0,lb,ub,A,b,Aeq,beq)
constrains the solution to satisfy the linear constraints
A x ≤ b
Aeq x = beq.
example
x = lsqnonlin(fun,x0,lb,ub,A,b,Aeq,beq,nonlcon)
constrain the solution to satisfy the nonlinear constraints in the nonlcon
(x
) function. nonlcon
returns two outputs, c
and ceq
. The solver attempts to satisfy the constraints
c ≤ 0
ceq
= 0.
example
x = lsqnonlin(fun,x0,lb,ub,options)
and
minimizes with the optimization options specified in x
= lsqnonlin(fun
,x0
,lb
,ub
,A,b,Aeq,beq,nonlcon,options
)options
. Use optimoptions to set these options. Pass empty matrices for lb
and ub
and for other input arguments if the arguments do not exist.
x = lsqnonlin(problem)
finds the minimum for problem
, a structure described in problem.
example
[x,resnorm]= lsqnonlin(___)
, for any input arguments, returns the value of the squared 2-norm of the residual at x
: sum(fun(x).^2)
.
example
[x,resnorm,residual,exitflag,output]= lsqnonlin(___)
additionally returns the value of the residual fun(x)
at the solution x
, a value exitflag
that describes the exit condition, and a structure output
that contains information about the optimization process.
[x,resnorm,residual,exitflag,output,lambda,jacobian]= lsqnonlin(___)
additionally returns a structure lambda
whose fields contain the Lagrange multipliers at the solution x
, and the Jacobian of fun
at the solution x
.
Examples
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Fit a Simple Exponential
Open Live Script
Fit a simple exponential decay curve to data.
Generate data from an exponential decay model plus noise. The model is
with ranging from 0 through 3, and normally distributed noise with mean 0 and standard deviation 0.05.
rng default % for reproducibilityd = linspace(0,3);y = exp(-1.3*d) + 0.05*randn(size(d));
The problem is: given the data (d
, y
), find the exponential decay rate that best fits the data.
Create an anonymous function that takes a value of the exponential decay rate and returns a vector of differences from the model with that decay rate and the data.
fun = @(r)exp(-d*r)-y;
Find the value of the optimal decay rate. Arbitrarily choose an initial guess x0
= 4.
x0 = 4;x = lsqnonlin(fun,x0)
Local minimum possible.lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
x = 1.2645
Plot the data and the best-fitting exponential curve.
plot(d,y,'ko',d,exp(-x*d),'b-')legend('Data','Best fit')xlabel('t')ylabel('exp(-tx)')
Fit a Problem with Bound Constraints
Open Live Script
Find the best-fitting model when some of the fitting parameters have bounds.
Find a centering and scaling that best fit the function
to the standard normal density,
Create a vector t
of data points, and the corresponding normal density at those points.
t = linspace(-4,4);y = 1/sqrt(2*pi)*exp(-t.^2/2);
Create a function that evaluates the difference between the centered and scaled function from the normal y
, with x(1)
as the scaling and x(2)
as the centering .
fun = @(x)x(1)*exp(-t).*exp(-exp(-(t-x(2)))) - y;
Find the optimal fit starting from x0
= [1/2,0]
, with the scaling between 1/2 and 3/2, and the centering between -1 and 3.
lb = [1/2,-1];ub = [3/2,3];x0 = [1/2,0];x = lsqnonlin(fun,x0,lb,ub)
Local minimum possible.lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
Plot the two functions to see the quality of the fit.
plot(t,y,'r-',t,fun(x)+y,'b-')xlabel('t')legend('Normal density','Fitted function')
Least Squares with Linear Constraint
Open Live Script
Consider the following objective function, a sum of squares:
The code for this objective function appears as the myfun
function at the end of this example.
Minimize this function subject to the linear constraint . Write this constraint as .
A = [1 -1/2];b = 0;
Impose the bounds , , , and .
lb = [0 0];ub = [2 4];
Start the optimization process from the point x0 = [0.3 0.4]
.
x0 = [0.3 0.4];
The problem has no linear equality constraints.
Aeq = [];beq = [];
Run the optimization.
x = lsqnonlin(@myfun,x0,lb,ub,A,b,Aeq,beq)
Local minimum found that satisfies the constraints.Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance,and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2 0.1695 0.3389
function F = myfun(x)k = 1:10;F = 2 + 2*k - exp(k*x(1)) - 2*exp(2*k*(x(2)^2));end
Nonlinear Least Squares with Nonlinear Constraint
Open Live Script
Consider the following objective function, a sum of squares:
The code for this objective function appears as the myfun
function at the end of this example.
Minimize this function subject to the nonlinear constraint . The code for this nonlinear constraint function appears as the nlcon
function at the end of this example.
Impose the bounds , , , and .
lb = [0 0];ub = [2 4];
Start the optimization process from the point x0 = [0.3 0.4]
.
x0 = [0.3 0.4];
The problem has no linear constraints.
A = [];b = [];Aeq = [];beq = [];
Run the optimization.
x = lsqnonlin(@myfun,x0,lb,ub,A,b,Aeq,beq,@nlcon)
Local minimum possible. Constraints satisfied.fmincon stopped because the size of the current step is less thanthe value of the step size tolerance and constraints are satisfied to within the value of the constraint tolerance.
x = 1×2 0.2133 0.3266
function F = myfun(x)k = 1:10;F = 2 + 2*k - exp(k*x(1)) - 2*exp(2*k*(x(2)^2));endfunction [c,ceq] = nlcon(x)ceq = [];c = sin(x(1)) - cos(x(2));end
Nonlinear Least Squares with Nondefault Options
Open Live Script
Compare the results of a data-fitting problem when using different lsqnonlin
algorithms.
Suppose that you have observation time data xdata
and observed response data ydata
, and you want to find parameters and to fit a model of the form
Input the observation times and responses.
xdata = ... [0.9 1.5 13.8 19.8 24.1 28.2 35.2 60.3 74.6 81.3];ydata = ... [455.2 428.6 124.1 67.3 43.2 28.1 13.1 -0.4 -1.3 -1.5];
Create a simple exponential decay model. The model computes a vector of differences between predicted values and observed values.
fun = @(x)x(1)*exp(x(2)*xdata)-ydata;
Fit the model using the starting point x0 = [100,-1]
. First, use the default 'trust-region-reflective'
algorithm.
x0 = [100,-1];options = optimoptions(@lsqnonlin,'Algorithm','trust-region-reflective');x = lsqnonlin(fun,x0,[],[],options)
Local minimum possible.lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
x = 1×2 498.8309 -0.1013
See if there is any difference using the 'levenberg-marquardt'
algorithm.
options.Algorithm = 'levenberg-marquardt';x = lsqnonlin(fun,x0,[],[],options)
Local minimum possible.lsqnonlin stopped because the relative size of the current step is less thanthe value of the step size tolerance.
x = 1×2 498.8309 -0.1013
The two algorithms found the same solution. Plot the solution and the data.
plot(xdata,ydata,'ko')hold ontlist = linspace(xdata(1),xdata(end));plot(tlist,x(1)*exp(x(2)*tlist),'b-')xlabel xdataylabel ydatatitle('Exponential Fit to Data')legend('Data','Exponential Fit')hold off
Nonlinear Least Squares Solution and Residual Norm
Open Live Script
Find the that minimizes
,
and find the value of the minimal sum of squares.
Because lsqnonlin
assumes that the sum of squares is not explicitly formed in the user-defined function, the function passed to lsqnonlin
should instead compute the vector-valued function
,
for to (that is, should have components).
The myfun
function, which computes the 10-component vector F, appears at the end of this example.
Find the minimizing point and the minimum value, starting at the point x0 = [0.3,0.4]
.
x0 = [0.3,0.4];[x,resnorm] = lsqnonlin(@myfun,x0)
Local minimum possible.lsqnonlin stopped because the size of the current step is less thanthe value of the step size tolerance.
x = 1×2 0.2578 0.2578
resnorm = 124.3622
The resnorm
output is the squared residual norm, or the sum of squares of the function values.
The following function computes the vector-valued objective function.
function F = myfun(x)k = 1:10;F = 2 + 2*k-exp(k*x(1))-exp(k*x(2));end
Examine the Solution Process
Open Live Script
Examine the solution process both as it occurs (by setting the Display
option to 'iter'
) and afterward (by examining the output
structure).
Suppose that you have observation time data xdata
and observed response data ydata
, and you want to find parameters and to fit a model of the form
Input the observation times and responses.
xdata = ... [0.9 1.5 13.8 19.8 24.1 28.2 35.2 60.3 74.6 81.3];ydata = ... [455.2 428.6 124.1 67.3 43.2 28.1 13.1 -0.4 -1.3 -1.5];
Create a simple exponential decay model. The model computes a vector of differences between predicted values and observed values.
fun = @(x)x(1)*exp(x(2)*xdata)-ydata;
Fit the model using the starting point x0 = [100,-1]
. Examine the solution process by setting the Display
option to 'iter'
. Obtain an output
structure to obtain more information about the solution process.
x0 = [100,-1];options = optimoptions('lsqnonlin','Display','iter');[x,resnorm,residual,exitflag,output] = lsqnonlin(fun,x0,[],[],options);
Norm of First-order Iteration Func-count Resnorm step optimality 0 3 359677 2.88e+04Objective function returned Inf; trying a new point... 1 6 359677 11.6976 2.88e+04 2 9 321395 0.5 4.97e+04 3 12 321395 1 4.97e+04 4 15 292253 0.25 7.06e+04 5 18 292253 0.5 7.06e+04 6 21 270350 0.125 1.15e+05 7 24 270350 0.25 1.15e+05 8 27 252777 0.0625 1.63e+05 9 30 252777 0.125 1.63e+05 10 33 243877 0.03125 7.48e+04 11 36 243660 0.0625 8.7e+04 12 39 243276 0.0625 2e+04 13 42 243174 0.0625 1.14e+04 14 45 242999 0.125 5.1e+03 15 48 242661 0.25 2.04e+03 16 51 241987 0.5 1.91e+03 17 54 240643 1 1.04e+03 18 57 237971 2 3.36e+03 19 60 232686 4 6.04e+03 20 63 222354 8 1.2e+04 21 66 202592 16 2.25e+04 22 69 166443 32 4.05e+04 23 72 106320 64 6.68e+04 24 75 28704.7 128 8.31e+04 25 78 89.7947 140.674 2.22e+04 26 81 9.57381 2.02599 684 27 84 9.50489 0.0619927 2.27 28 87 9.50489 0.000462261 0.0114 Local minimum possible.lsqnonlin stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
Examine the output structure to obtain more information about the solution process.
output
output = struct with fields: firstorderopt: 0.0114 iterations: 28 funcCount: 87 cgiterations: 0 algorithm: 'trust-region-reflective' stepsize: 4.6226e-04 message: 'Local minimum possible....' bestfeasible: [] constrviolation: []
For comparison, set the Algorithm
option to 'levenberg-marquardt'
.
options.Algorithm = 'levenberg-marquardt';[x,resnorm,residual,exitflag,output] = lsqnonlin(fun,x0,[],[],options);
First-order Norm of Iteration Func-count Resnorm optimality Lambda step 0 3 359677 2.88e+04 0.01Objective function returned Inf; trying a new point... 1 13 340761 3.91e+04 100000 0.280777 2 16 304661 5.97e+04 10000 0.373146 3 21 297292 6.55e+04 1e+06 0.0589933 4 24 288240 7.57e+04 100000 0.0645444 5 28 275407 1.01e+05 1e+06 0.0741266 6 31 249954 1.62e+05 100000 0.094571 7 36 245896 1.35e+05 1e+07 0.0133606 8 39 243846 7.26e+04 1e+06 0.0094431 9 42 243568 5.66e+04 100000 0.0082162 10 45 243424 1.61e+04 10000 0.00777935 11 48 243322 8.8e+03 1000 0.0673933 12 51 242408 5.1e+03 100 0.675209 13 54 233628 1.05e+04 10 6.59804 14 57 169089 8.51e+04 1 54.6992 15 60 30814.7 1.54e+05 0.1 196.939 16 63 147.496 8e+03 0.01 129.795 17 66 9.51503 117 0.001 9.96069 18 69 9.50489 0.0714 0.0001 0.080486 19 72 9.50489 5.23e-05 1e-05 5.07043e-05Local minimum possible.lsqnonlin stopped because the relative size of the current step is less thanthe value of the step size tolerance.
The 'levenberg-marquardt'
converged with fewer iterations, but almost as many function evaluations:
output
output = struct with fields: iterations: 19 funcCount: 72 stepsize: 5.0704e-05 cgiterations: [] firstorderopt: 5.2319e-05 algorithm: 'levenberg-marquardt' message: 'Local minimum possible....' bestfeasible: [] constrviolation: []
Input Arguments
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fun
— Function whose sum of squares is minimized
function handle | name of function
Function whose sum of squares is minimized, specified as a function handle or the name of a function. For the 'interior-point'
algorithm, fun
must be a function handle. fun
is a function that accepts an array x
and returns an array F
, the objective function evaluated at x
.
Note
The sum of squares should not be formed explicitly. Instead, your function should return a vector of function values. See Examples.
The function fun
can be specified as a function handle to a file:
x = lsqnonlin(@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 = lsqnonlin(@(x)sin(x.*x),x0);
lsqnonlin
passes x
to your objective function in the shape of the x0 argument. For example, if x0
is a 5-by-3 array, then lsqnonlin
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('lsqnonlin','SpecifyObjectiveGradient',true)
then the function fun
must return a second output argument with the Jacobian value J
(a matrix) at x
. By checking the value of nargout
, the function can avoid computing J
when fun
is called with only one output argument (in the case where the optimization algorithm only needs the value of F
but not J
).
function [F,J] = myfun(x)F = ... % Objective function values at xif nargout > 1 % Two output arguments J = ... % Jacobian of the function evaluated at xend
If fun
returns an array of m
components and x
has n
elements, where n
is the number of elements 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: @(x)cos(x).*exp(-x)
Data Types: char
| function_handle
| string
nonlcon
— Nonlinear constraints
function handle
Nonlinear constraints, specified as a function handle. nonlcon
is a function that accepts a vector or array x
and returns two arrays, c(x)
and ceq(x)
.
c(x)
is the array of nonlinear inequality constraints atx
.lsqnonlin
attempts to satisfyc(x) <= 0
for all entries ofc
.(1) ceq(x)
is the array of nonlinear equality constraints atx
.lsqnonlin
attempts to satisfyceq(x) = 0
for all entries ofceq
.(2)
For example,
x = lsqnonlin(@myfun,x0,lb,ub,A,b,Aeq,beq,@mycon,options)
where mycon
is a MATLAB function such as
function [c,ceq] = mycon(x)c = ... % Compute nonlinear inequalities at x.ceq = ... % Compute nonlinear equalities at x.
If the Jacobians (derivatives) of the constraints can also be computed and the SpecifyConstraintGradient
option is true
, as set by
options = optimoptions('lsqnonlin','SpecifyConstraintGradient',true)
then nonlcon
must also return, in the third and fourth output arguments, GC
, the Jacobian of c(x)
, and GCeq
, the Jacobian of ceq(x)
. The Jacobian G(x) of a vector function F(x) is
GC
and GCeq
can be sparse or dense. If GC
or GCeq
is large, with relatively few nonzero entries, save running time and memory in the 'interior-point'
algorithm by representing them as sparse matrices. For more information, see Nonlinear Constraints.
Data Types: function_handle
options
— Optimization options
output of optimoptions
| structure as optimset
returns
Optimization options, specified as the output of optimoptions
or a structure 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 | |
| Choose between The The For more information on choosing the algorithm, see Choosing the Algorithm. |
CheckGradients | Compare user-supplied derivatives (gradients of objective or constraints) to finite-differencing derivatives. Choices are For The |
Diagnostics | Display diagnostic informationabout the function to be minimized or solved. Choices are |
DiffMaxChange | Maximum change in variables forfinite-difference gradients (a positive scalar). The default is |
DiffMinChange | Minimum change in variables forfinite-difference gradients (a positive scalar). The default is |
| Level of display (see Iterative Display):
|
FiniteDifferenceStepSize | Scalar or vector step size factor for finite differences. When you set
sign′(x) = sign(x) except sign′(0) = 1 . Central finite differences are
FiniteDifferenceStepSize expands to a vector. The default is sqrt(eps) for forward finite differences, and eps^(1/3) for central finite differences. For |
FiniteDifferenceType | Finite differences, used to estimate gradients,are either Thealgorithm is careful to obey bounds when estimating both types offinite differences. So, for example, it could take a backward, ratherthan a forward, difference to avoid evaluating at a point outsidebounds. For |
| Termination tolerance on the function value, a nonnegative scalar. The default is For |
FunValCheck | Check whether function values arevalid. |
| Maximum number of function evaluations allowed, a nonnegative integer. The default is For |
| Maximum number of iterations allowed, a nonnegative integer. The default is For |
OptimalityTolerance | Termination tolerance on the first-order optimality (a nonnegative scalar). The default is Internally,the For |
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 ( |
| Plots various measures of progress while the algorithm executes; select from predefined plots or write your own. Pass a name, a function handle, or a cell array of names or function handles. For custom plot functions, pass function handles. The default is none (
Custom plot functions use the same syntax as output functions. See Output Functions for Optimization Toolbox and Output Function and Plot Function Syntax. For |
SpecifyObjectiveGradient | If For |
| Termination tolerance on For |
| Typical |
UseParallel | When |
Trust-Region-Reflective Algorithm | |
JacobianMultiplyFcn | Jacobian multiply function, specified as a function handle. For large-scale structured problems, this function computes the Jacobian matrix product W = jmfun(Jinfo,Y,flag) where W = jmfun(Jinfo,Y,flag,xdata) where The data [F,Jinfo] = fun(x)% or [F,Jinfo] = fun(x,xdata)
In each case, Note
See Minimization with Dense Structured Hessian, Linear Equalities and Jacobian Multiply Function with Linear Least Squares for similar examples. For |
JacobPattern | Sparsity pattern of the Jacobianfor finite differencing. Set Use Ifthe structure is unknown, do not set |
MaxPCGIter | Maximum number of PCG (preconditionedconjugate gradient) iterations, a positive scalar. The default is |
PrecondBandWidth | Upper bandwidth of preconditionerfor PCG, a nonnegative integer. The default |
SubproblemAlgorithm | Determines how the iteration stepis calculated. The default, |
TolPCG | Termination tolerance on the PCGiteration, a positive scalar. The default is |
Levenberg-Marquardt Algorithm | |
InitDamping | Initial value of the Levenberg-Marquardt parameter, apositive scalar. Default is |
ScaleProblem |
|
Interior-Point Algorithm | |
BarrierParamUpdate | Specifies how
This option can affect the speed and convergence of the solver, but the effect is not easy to predict. |
ConstraintTolerance | Tolerance on the constraint violation, a nonnegative scalar. The default is For |
InitBarrierParam | Initial barrier value, a positive scalar. Sometimes it might help to try a value above the default |
SpecifyConstraintGradient | Gradient for nonlinear constraint functions defined by the user. When set to the default, For |
SubproblemAlgorithm | Determines how the iteration step is calculated. The default, For |
Example: options = optimoptions('lsqnonlin','FiniteDifferenceType','central')
problem
— Problem structure
structure
Problem structure, specified as a structure with the following fields:
Field Name | Entry |
---|---|
| Objective function |
| Initial point for x |
| Matrix for linear inequality constraints |
| Vector for linear inequality constraints |
| Matrix for linear equality constraints |
| Vector for linear equality constraints |
lb | Vector of lower bounds |
ub | Vector of upper bounds |
| Nonlinear constraint function |
| 'lsqnonlin' |
| Options created with optimoptions |
You must supply at least the objective
, x0
, solver
, and options
fields in the problem
structure.
Data Types: struct
Output Arguments
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resnorm
— Squared norm of the residual
nonnegative real
Squared norm of the residual, returned as a nonnegative real. resnorm
is the squared 2-norm of the residual at x
: sum(fun(x).^2)
.
residual
— Value of objective function at solution
array
Value of objective function at solution, returned as an array. In general, residual = fun(x)
.
Limitations
The trust-region-reflective algorithm does not solve underdetermined systems; it requires that the number of equations, i.e., the row dimension of F, be at least as great as the number of variables. In the underdetermined case,
lsqnonlin
uses the Levenberg-Marquardt algorithm.lsqnonlin
can solve complex-valued problems directly. Note that constraints do not make sense for complex values, because complex numbers are not well-ordered; asking whether one complex value is greater or less than another complex value is nonsensical. For a complex problem with bound constraints, split the variables into real and imaginary parts. Do not use the'interior-point'
algorithm with complex data. See Fit a Model to Complex-Valued Data.The preconditioner computation used in the preconditioned conjugate gradient part of the trust-region-reflective method forms JTJ (where J is the Jacobian matrix) before computing the preconditioner. Therefore, a row of J with many nonzeros, which results in a nearly dense product JTJ, can lead to a costly solution process for large problems.
If components of x have no upper (or lower) bounds,
lsqnonlin
prefers that the corresponding components ofub
(orlb
) be set toinf
(or-inf
for lower bounds) as opposed to an arbitrary but very large positive (or negative for lower bounds) number.
You can use the trust-region reflective algorithm in lsqnonlin, lsqcurvefit,and fsolve with small- to medium-scaleproblems without computing the Jacobian in fun
orproviding the Jacobian sparsity pattern. (This also applies to using fmincon or fminunc withoutcomputing the Hessian or supplying the Hessian sparsity pattern.)How small is small- to medium-scale? No absolute answer is available,as it depends on the amount of virtual memory in your computer systemconfiguration.
Suppose your problem has m
equations and n
unknowns.If the command J=sparse(ones(m,n))
causesan Out of memory
error on your machine,then this is certainly too large a problem. If it does not resultin an error, the problem might still be too large. You can find outonly by running it and seeing if MATLAB runs within the amountof virtual memory available on your system.
Algorithms
The Levenberg-Marquardt and trust-region-reflective methods are based on the nonlinear least-squares algorithms also used in fsolve.
The default trust-region-reflective 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-Reflective Least Squares.
The Levenberg-Marquardt method is described in references [4], [5], and [6]. See Levenberg-Marquardt Method.
The 'interior-point'
algorithm uses the fmincon
'interior-point'
algorithm with some modifications. For details, see Modified fmincon Algorithm for Constrained Least Squares.
Alternative Functionality
App
The Optimize Live Editor task provides a visual interface for lsqnonlin
.
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, 1996, pp. 418–445.
[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, 1994, pp. 189–224.
[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, 1944, pp. 164–168.
[5] Marquardt, D. “An Algorithm for Least-squares Estimation of Nonlinear Parameters.” SIAM Journal Applied Mathematics, Vol. 11, 1963, pp. 431–441.
[6] Moré, J. J. “The Levenberg-Marquardt Algorithm: Implementation and Theory.” Numerical Analysis, ed. G. A. Watson, Lecture Notes in Mathematics 630, Springer Verlag, 1977, pp. 105–116.
[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
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
lsqcurvefit
andlsqnonlin
support code generation using either the codegen (MATLAB Coder) function or the MATLAB Coder™ app. You must have a MATLAB Coder license to generate code.The target hardware must support standard double-precision floating-point computations. You cannot generate code for single-precision or fixed-point computations.
Code generation targets do not use the same math kernel libraries as MATLAB solvers. Therefore, code generation solutions can vary from solver solutions, especially for poorly conditioned problems.
All code for generation must be MATLAB code. In particular, you cannot use a custom black-box function as an objective function for
lsqcurvefit
orlsqnonlin
. You can usecoder.ceval
to evaluate a custom function coded in C or C++. However, the custom function must be called in a MATLAB function.Code generation for
lsqcurvefit
andlsqnonlin
currently does not support linear or nonlinear constraints.lsqcurvefit
andlsqnonlin
do not support the problem argument for code generation.[x,fval] = lsqnonlin(problem) % Not supported
You must specify the objective function by using function handles, not strings or character names.
x = lsqnonlin(@fun,x0,lb,ub,options) % Supported% Not supported: lsqnonlin('fun',...) or lsqnonlin("fun",...)
All input matrices
lb
andub
must be full, not sparse. You can convert sparse matrices to full by using the full function.The
lb
andub
arguments must have the same number of entries as thex0
argument or must be empty[]
.If your target hardware does not support infinite bounds, use optim.coder.infbound.
For advanced code optimization involving embedded processors, you also need an Embedded Coder® license.
You must include options for
lsqcurvefit
orlsqnonlin
and specify them using optimoptions. The options must include theAlgorithm
option, set to'levenberg-marquardt'
.options = optimoptions('lsqnonlin','Algorithm','levenberg-marquardt');[x,fval,exitflag] = lsqnonlin(fun,x0,lb,ub,options);
Code generation supports these options:
Algorithm
— Must be'levenberg-marquardt'
FiniteDifferenceStepSize
FiniteDifferenceType
FunctionTolerance
MaxFunctionEvaluations
MaxIterations
SpecifyObjectiveGradient
StepTolerance
TypicalX
Generated code has limited error checking for options. The recommended way to update an option is to use
optimoptions
, not dot notation.opts = optimoptions('lsqnonlin','Algorithm','levenberg-marquardt');opts = optimoptions(opts,'MaxIterations',1e4); % Recommendedopts.MaxIterations = 1e4; % Not recommended
Do not load options from a file. Doing so can cause code generation to fail. Instead, create options in your code.
Usually, if you specify an option that is not supported, the option is silently ignored during code generation. However, if you specify a plot function or output function by using dot notation, code generation can issue an error. For reliability, specify only supported options.
Because output functions and plot functions are not supported, solvers do not return the exit flag –1.
For an example, see Generate Code for lsqcurvefit or lsqnonlin.
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
To run in parallel, set the 'UseParallel'
option to true
.
options = optimoptions('
solvername
','UseParallel',true)
For more information, see Using Parallel Computing in Optimization Toolbox.
Version History
Introduced before R2006a
expand all
R2023b: JacobianMultiplyFcn
accepts any data type
The syntax for the JacobianMultiplyFcn
option is
W = jmfun(Jinfo, Y, flag)
The Jinfo
data, which MATLAB passes to your function jmfun
, can now be of any data type. For example, you can now have Jinfo
be a structure. In previous releases, Jinfo
had to be a standard double array.
The Jinfo
data is the second output of your objective function:
[F,Jinfo] = myfun(x)
R2023b: CheckGradients
option will be removed
The CheckGradients
option will be removed in a future release. To check the first derivatives of objective functions or nonlinear constraint functions, use the checkGradients function.
R2023a: Linear and Nonlinear Constraint Support
lsqnonlin
gains support for both linear and nonlinear constraints. To enable constraint satisfaction, the solver uses the "interior-point"
algorithm from fmincon
.
If you specify constraints but do not specify an algorithm, the solver automatically switches to the
"interior-point"
algorithm.If you specify constraints and an algorithm, you must specify the
"interior-point"
algorithm.
For algorithm details, see Modified fmincon Algorithm for Constrained Least Squares. For an example, see Compare lsqnonlin and fmincon for Constrained Nonlinear Least Squares.
See Also
fsolve | lsqcurvefit | optimoptions | Optimize
Topics
- Nonlinear Least Squares (Curve Fitting)
- Solver-Based Optimization Problem Setup
- Least-Squares (Model Fitting) Algorithms
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