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Solve nonlinear curvefitting (datafitting) problems in leastsquares sense
Find coefficients x that solve the problem
given input data xdata, and the observed output ydata, where xdata and ydata are matrices or vectors, and F (x, xdata) is a matrixvalued or vectorvalued function of the same size as ydata.
Optionally, the components of x can have lower and upper bounds lb, and ub. x, lb, and ub can be vectors or matrices; see Matrix Arguments.
The lsqcurvefit function uses the same algorithm as lsqnonlin. lsqcurvefit simply provides a convenient interface for datafitting problems.
x = lsqcurvefit(fun,x0,xdata,ydata)
x = lsqcurvefit(fun,x0,xdata,ydata,lb,ub)
x = lsqcurvefit(fun,x0,xdata,ydata,lb,ub,options)
x = lsqcurvefit(problem)
[x,resnorm] = lsqcurvefit(...)
[x,resnorm,residual] = lsqcurvefit(...)
[x,resnorm,residual,exitflag] = lsqcurvefit(...)
[x,resnorm,residual,exitflag,output]
= lsqcurvefit(...)
[x,resnorm,residual,exitflag,output,lambda]
= lsqcurvefit(...)
[x,resnorm,residual,exitflag,output,lambda,jacobian]
= lsqcurvefit(...)
x = lsqcurvefit(fun,x0,xdata,ydata) starts at x0 and finds coefficients x to best fit the nonlinear function fun(x,xdata) to the data ydata (in the leastsquares sense). ydata must be the same size as the vector (or matrix) F returned by fun.
Note: Passing Extra Parameters explains how to pass extra parameters to fun, if necessary. fun should return fun(x,xdata), and not the sumofsquares sum((fun(x,xdata)ydata).^2). lsqcurvefit implicitly computes the sum of squares of the components of fun(x,xdata)ydata. 
x = lsqcurvefit(fun,x0,xdata,ydata,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.
x = lsqcurvefit(fun,x0,xdata,ydata,lb,ub,options) minimizes with the optimization options specified in options. Use optimoptions to set these options. Pass empty matrices for lb and ub if no bounds exist.
x = lsqcurvefit(problem) finds the minimum for problem, where problem is a structure described in Input Arguments.
Create the problem structure by exporting a problem from Optimization app, as described in Exporting Your Work.
[x,resnorm] = lsqcurvefit(...) returns the value of the squared 2norm of the residual at x: sum((fun(x,xdata)ydata).^2).
[x,resnorm,residual] = lsqcurvefit(...) returns the value of the residual fun(x,xdata)ydata at the solution x.
[x,resnorm,residual,exitflag] = lsqcurvefit(...) returns a value exitflag that describes the exit condition.
[x,resnorm,residual,exitflag,output] = lsqcurvefit(...) returns a structure output that contains information about the optimization.
[x,resnorm,residual,exitflag,output,lambda] = lsqcurvefit(...) returns a structure lambda whose fields contain the Lagrange multipliers at the solution x.
[x,resnorm,residual,exitflag,output,lambda,jacobian] = lsqcurvefit(...) returns the Jacobian of fun at the solution x.
Function Arguments contains general descriptions of arguments passed into lsqcurvefit. This section provides functionspecific details for fun, options, and problem:
The function you want to fit. fun is a function that takes two inputs: a vector or matrix x, and a vector or matrix xdata. fun returns a vector or matrix F, the objective function evaluated at x and xdata. The function fun can be specified as a function handle for a function file: x = lsqcurvefit(@myfun,x0,xdata,ydata) where myfun is a MATLAB^{®} function such as function F = myfun(x,xdata) F = ... % Compute function values at x, xdata fun can also be a function handle for an anonymous function. f = @(x,xdata)x(1)*xdata.^2+x(2)*sin(xdata); x = lsqcurvefit(f,x0,xdata,ydata); lsqcurvefit internally converts matrix x or F to vectors using linear indexing.
If the Jacobian can also be computed and the Jacobian option is 'on', set by options = optimoptions('lsqcurvefit','Jacobian','on') then the function fun must return, in a second output argument, 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,xdata) F = ... % objective function values at x if nargout > 1 % two output arguments J = ... % Jacobian of the function evaluated at x end If fun returns a vector (matrix) of m components and x has length n, where n is the length of x0, then the Jacobian J is an mbyn 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.) For more information, see Writing Vector and Matrix Objective Functions.  
options  Options provides the functionspecific details for the options values.  
problem  objective  Objective function of x and xdata  
x0  Initial point for x, active set algorithm only  
xdata  Input data for objective function  
ydata  Output data to be matched by objective function  
lb  Vector of lower bounds  
ub  Vector of upper bounds  
solver  'lsqcurvefit'  
options  Options created with optimoptions 
Function Arguments contains general descriptions of arguments returned by lsqcurvefit. This section provides functionspecific details for exitflag, lambda, and output:
exitflag  Integer identifying the reason the algorithm terminated. The following lists the values of exitflag and the corresponding reasons the algorithm terminated:  
1  Function converged to a solution x.  
2  Change in x was less than the specified tolerance.  
3  Change in the residual was less than the specified tolerance.  
4  Magnitude of search direction smaller than the specified tolerance.  
0  Number of iterations exceeded options.MaxIter or number of function evaluations exceeded options.MaxFunEvals.  
1  Output function terminated the algorithm.  
2  Problem is infeasible: the bounds lb and ub are inconsistent.  
4  Optimization could not make further progress.  
lambda  Structure containing the Lagrange multipliers at the solution x (separated by constraint type). The fields of the structure are  
lower  Lower bounds lb  
upper  Upper bounds ub  
output  Structure containing information about the optimization. The fields of the structure are  
firstorderopt  Measure of firstorder optimality (trustregionreflective algorithm, [ ] for others).  
iterations  Number of iterations taken  
funcCount  Number of function evaluations  
cgiterations  Total number of PCG iterations (trustregionreflective algorithm, [ ] for others)  
algorithm  Optimization algorithm used  
stepsize  Final displacement in x (LevenbergMarquardt algorithm).  
message  Exit message 
Optimization options used by lsqcurvefit. Some options apply to all algorithms, some are only relevant when using the trustregionreflective algorithm, and others are only relevant when you are using the LevenbergMarquardt algorithm. Use optimoptions to set or change options. See Algorithm Options for detailed information.
The Algorithm option specifies a preference for which algorithm to use. It is only a preference, because certain conditions must be met to use the trustregionreflective or LevenbergMarquardt algorithm. For the trustregionreflective 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. Furthermore, only the trustregionreflective algorithm handles bound constraints.
Both algorithms use the following option:
Algorithm  Choose between 'trustregionreflective' (default) and 'levenbergmarquardt'. Set the initial LevenbergMarquardt parameter λ by setting Algorithm to a cell array such as {'levenbergmarquardt',.005}. The default λ = 0.01. The Algorithm option specifies a preference for which algorithm to use. It is only a preference, because certain conditions must be met to use each algorithm. For the trustregionreflective 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. The LevenbergMarquardt algorithm does not handle bound constraints. For more information on choosing the algorithm, see Choosing the Algorithm. 
DerivativeCheck  Compare usersupplied derivatives (gradients of objective or constraints) to finitedifferencing derivatives. The choices are 'on' or the default 'off'. 
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 finitedifference gradients (a positive scalar). The default is Inf. 
DiffMinChange  Minimum change in variables for finitedifference gradients (a positive scalar). The default is 0. 
Display  Level of display:

FinDiffRelStep  Scalar or vector step size factor. When you set FinDiffRelStep to a vector v, forward finite differences delta are delta = v.*sign(x).*max(abs(x),TypicalX); and central finite differences are delta = v.*max(abs(x),TypicalX); Scalar FinDiffRelStep expands to a vector. The default is sqrt(eps) for forward finite differences, and eps^(1/3) for central finite differences. 
FinDiffType  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. 
FunValCheck  Check whether function values are valid. 'on' displays an error when the function returns a value that is complex, Inf, or NaN. The default 'off' displays no error. 
Jacobian  If 'on', lsqcurvefit uses a userdefined Jacobian (defined in fun), or Jacobian information (when using JacobMult), for the objective function. If 'off' (default), lsqcurvefit approximates the Jacobian using finite differences. 
MaxFunEvals  Maximum number of function evaluations allowed, a positive integer. The default is 100*numberOfVariables. 
MaxIter  Maximum number of iterations allowed, a positive integer. The default is 400. 
OutputFcn  Specify one or more userdefined functions that an optimization function calls at each iteration, either as a function handle or as a cell array of function handles. The default is none ([]). See Output Function. 
PlotFcns  Plots various measures of progress while the algorithm executes, select from predefined plots or write your own. Pass a function handle or a cell array of function handles. The default is none ([]):
For information on writing a custom plot function, see Plot Functions. 
TolFun  Termination tolerance on the function value, a positive scalar. The default is 1e6. 
TolX  Termination tolerance on x, a positive scalar. The default is 1e6. 
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). lsqcurvefit uses TypicalX for scaling finite differences for gradient estimation. 
The trustregionreflective algorithm uses the following options:
JacobMult  Function handle for Jacobian multiply function. For largescale 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 the matrix used to compute J*Y (or J'*Y, or J'*(J*Y)). The first argument Jinfo must be the same as 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:
In each case, J is not formed explicitly. lsqcurvefit uses Jinfo to compute the preconditioner. See Passing Extra Parameters for information on how to supply values for any additional parameters jmfun needs. See Minimization with Dense Structured Hessian, Linear Equalities and Jacobian Multiply Function with Linear Least Squares for similar examples.  
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). lsqcurvefit 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 lsqcurvefit computes a full finitedifference 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 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.  
TolPCG  Termination tolerance on the PCG iteration, a positive scalar. The default is 0.1. 
The LevenbergMarquardt algorithm uses the following options:
ScaleProblem  'Jacobian' can sometimes improve the convergence of a poorlyscaled problem; the default is 'none'. 
Given vectors of data xdata and ydata, suppose you want to find coefficients x to find the best fit to the exponential decay equation
That is, you want to minimize
where m is the length of xdata and ydata, the function F is defined by
F(x,xdata) = x(1)*exp(x(2)*xdata);
and the starting point is x0 = [100; 1];.
First, write a file to return the value of F (F has n components).
function F = myfun(x,xdata) F = x(1)*exp(x(2)*xdata);
Next, invoke an optimization routine:
% Assume you determined xdata and ydata experimentally 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]; x0 = [100; 1] % Starting guess [x,resnorm] = lsqcurvefit(@myfun,x0,xdata,ydata);
At the time that lsqcurvefit is called, xdata and ydata are assumed to exist and are vectors of the same size. They must be the same size because the value F returned by fun must be the same size as ydata.
After 27 function evaluations, this example gives the solution
x,resnorm x = 498.8309 0.1013 resnorm = 9.5049
There may be a slight variation in the number of iterations and the value of the returned x, depending on the platform and release.
The trustregionreflective method does not allow equal upper and lower bounds. For example, if lb(2)==ub(2), lsqcurvefit gives the error
Equal upper and lower bounds not permitted.
lsqcurvefit does not handle equality constraints, which is another way to formulate equal bounds. If equality constraints are present, use fmincon, fminimax, or fgoalattain for alternative formulations where equality constraints can be included.
The function to be minimized must be continuous. lsqcurvefit might only give local solutions.
lsqcurvefit can solve complexvalued problems directly with the levenbergmarquardt algorithm. However, this algorithm does not accept bound constraints. For a complex problem with bound constraints, split the variables into real and imaginary parts, and use the trustregionreflective algorithm. See Fit a Model to ComplexValued Data.
Note: The Statistics Toolbox™ function nlinfit has more statisticsoriented outputs that are useful, for example, in finding confidence intervals for the coefficients. It also comes with the nlintool GUI for visualizing the fitted function. The lsqnonlin function has more outputs related to how well the optimization performed. It can put bounds on the parameters, and it accepts many options to control the optimization algorithm. 
The trustregionreflective algorithm for lsqcurvefit 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, the LevenbergMarquardt algorithm is used instead.
The preconditioner computation used in the preconditioned conjugate gradient part of the trustregionreflective method forms J^{T}J (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 J^{T}J, can lead to a costly solution process for large problems.
If components of x have no upper (or lower) bounds, then lsqcurvefit prefers that the corresponding components of ub (or lb) be set to inf (or inf for lower bounds) as opposed to an arbitrary but very large positive (or negative for lower bounds) number.
TrustRegionReflective Problem Coverage and Requirements
For Large Problems 


The LevenbergMarquardt algorithm does not handle bound constraints.
Since the trustregionreflective algorithm does not handle underdetermined systems and the LevenbergMarquardt does not handle bound constraints, problems with both these characteristics cannot be solved by lsqcurvefit.
[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. 418445, 1996.
[2] Coleman, T.F. and Y. Li, "On the Convergence of Reflective Newton Methods for LargeScale Nonlinear Minimization Subject to Bounds," Mathematical Programming, Vol. 67, Number 2, pp. 189224, 1994.
[3] Dennis, J. E. Jr., "Nonlinear LeastSquares," State of the Art in Numerical Analysis, ed. D. Jacobs, Academic Press, pp. 269312, 1977.
[4] Levenberg, K., "A Method for the Solution of Certain Problems in LeastSquares," Quarterly Applied Math. 2, pp. 164168, 1944.
[5] Marquardt, D., "An Algorithm for LeastSquares Estimation of Nonlinear Parameters," SIAM Journal Applied Math., Vol. 11, pp. 431441, 1963.
[6] More, J. J., "The LevenbergMarquardt Algorithm: Implementation and Theory," Numerical Analysis, ed. G. A. Watson, Lecture Notes in Mathematics 630, Springer Verlag, pp. 105116, 1977.
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