26 用lsqnonlin求解最小二乘问题（matlab程序）

1. 简述

函数语法

x = lsqnonlin(fun,x0)

函数用于：

解决非线性最小二乘(非线性数据拟合)问题

解决非线性最小二乘曲线拟合问题的形式

变量x的约束上下限为ub和lb，

x = lsqnonlin(fun,x0)从x0点开始，找到fun中描述的函数的最小平方和。函数fun应该返回一个向量(或数组)，而不是值的平方和。(该算法隐式地计算了fun(x)元素的平方和。)

2. 代码

主程序：

%% 用lsqnonlin求解最小二乘问题

clear all

x0 = [0.3 0.4]; % 初值点

[x,resnorm] = lsqnonlin(@f1211,x0) % 调用最优化函数求 x 和平方和残差

子程序：

function [xCurrent,Resnorm,FVAL,EXITFLAG,OUTPUT,LAMBDA,JACOB] = lsqnonlin(FUN,xCurrent,LB,UB,options,varargin)

%LSQNONLIN solves non-linear least squares problems.

% LSQNONLIN attempts to solve problems of the form:

% min sum {FUN(X).^2} where X and the values returned by FUN can be

% X vectors or matrices.

% LSQNONLIN implements two different algorithms: trust region reflective

% and Levenberg-Marquardt. Choose one via the option Algorithm: for

% instance, to choose Levenberg-Marquardt, set

% OPTIONS = optimoptions('lsqnonlin', 'Algorithm','levenberg-marquardt'),

% and then pass OPTIONS to LSQNONLIN.

% X = LSQNONLIN(FUN,X0) starts at the matrix X0 and finds a minimum X to

% the sum of squares of the functions in FUN. FUN accepts input X

% and returns a vector (or matrix) of function values F evaluated

% at X. NOTE: FUN should return FUN(X) and not the sum-of-squares

% sum(FUN(X).^2)). (FUN(X) is summed and squared implicitly in the

% algorithm.)

% X = LSQNONLIN(FUN,X0,LB,UB) defines a set of lower and upper bounds on

% the design variables, X, so that the solution is in the range LB <= X

% <= UB. Use empty matrices for LB and UB if no bounds exist. Set LB(i)

% = -Inf if X(i) is unbounded below; set UB(i) = Inf if X(i) is

% unbounded above.

% X = LSQNONLIN(FUN,X0,LB,UB,OPTIONS) minimizes with the default

% optimization parameters replaced by values in OPTIONS, an argument

% created with the OPTIMOPTIONS function. See OPTIMOPTIONS for details.

% Use the SpecifyObjectiveGradient option to specify that FUN also

% returns a second output argument J that is the Jacobian matrix at the

% point X. If FUN returns a vector F of m components when X has length n,

% then J is an m-by-n matrix where J(i,j) is the partial derivative of

% F(i) with respect to x(j). (Note that the Jacobian J is the transpose

% of the gradient of F.)

% X = LSQNONLIN(PROBLEM) solves the non-linear least squares problem

% defined in PROBLEM. PROBLEM is a structure with the function FUN in

% PROBLEM.objective, the start point in PROBLEM.x0, the lower bounds in

% PROBLEM.lb, the upper bounds in PROBLEM.ub, the options structure in

% PROBLEM.options, and solver name 'lsqnonlin' in PROBLEM.solver. Use

% this syntax to solve at the command line a problem exported from

% OPTIMTOOL.

% [X,RESNORM] = LSQNONLIN(FUN,X0,...) returns

% the value of the squared 2-norm of the residual at X: sum(FUN(X).^2).

% [X,RESNORM,RESIDUAL] = LSQNONLIN(FUN,X0,...) returns the value of the

% residual at the solution X: RESIDUAL = FUN(X).

% [X,RESNORM,RESIDUAL,EXITFLAG] = LSQNONLIN(FUN,X0,...) returns an

% EXITFLAG that describes the exit condition. Possible values of EXITFLAG

% and the corresponding exit conditions are listed below. See the

% documentation for a complete description.

% 1 LSQNONLIN converged to a solution.

% 2 Change in X too small.

% 3 Change in RESNORM too small.

% 4 Computed search direction too small.

% 0 Too many function evaluations or iterations.

% -1 Stopped by output/plot function.

% -2 Bounds are inconsistent.

% [X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT] = LSQNONLIN(FUN,X0,...) returns a

% structure OUTPUT with the number of iterations taken in

% OUTPUT.iterations, the number of function evaluations in

% OUTPUT.funcCount, the algorithm used in OUTPUT.algorithm, the number

% of CG iterations (if used) in OUTPUT.cgiterations, the first-order

% optimality (if used) in OUTPUT.firstorderopt, and the exit message in

% OUTPUT.message.

% [X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT,LAMBDA] = LSQNONLIN(FUN,X0,...)

% returns the set of Lagrangian multipliers, LAMBDA, at the solution:

% LAMBDA.lower for LB and LAMBDA.upper for UB.

% [X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT,LAMBDA,JACOBIAN] = LSQNONLIN(FUN,

% X0,...) returns the Jacobian of FUN at X.

% Examples

% FUN can be specified using @:

% x = lsqnonlin(@myfun,[2 3 4])

% where myfun is a MATLAB function such as:

% function F = myfun(x)

% F = sin(x);

% FUN can also be an anonymous function:

% x = lsqnonlin(@(x) sin(3*x),[1 4])

% If FUN is parameterized, you can use anonymous functions to capture the

% problem-dependent parameters. Suppose you want to solve the non-linear

% least squares problem given in the function myfun, which is

% parameterized by its second argument c. Here myfun is a MATLAB file

% function such as

% function F = myfun(x,c)

% F = [ 2*x(1) - exp(c*x(1))

% -x(1) - exp(c*x(2))

% x(1) - x(2) ];

% To solve the least squares problem for a specific value of c, first

% assign the value to c. Then create a one-argument anonymous function

% that captures that value of c and calls myfun with two arguments.

% Finally, pass this anonymous function to LSQNONLIN:

% c = -1; % define parameter first

% x = lsqnonlin(@(x) myfun(x,c),[1;1])

% See also OPTIMOPTIONS, LSQCURVEFIT, FSOLVE, @, INLINE.

% ------------Initialization----------------

defaultopt = struct(...

'Algorithm','trust-region-reflective',...

'DerivativeCheck','off',...

'Diagnostics','off',...

'DiffMaxChange',Inf,...

'DiffMinChange',0,...

'Display','final',...

'FinDiffRelStep', [], ...

'FinDiffType','forward',...

'FunValCheck','off',...

'InitDamping', 0.01, ...

'Jacobian','off',...

'JacobMult',[],...

'JacobPattern','sparse(ones(Jrows,Jcols))',...

'MaxFunEvals',[],...

'MaxIter',400,...

'MaxPCGIter','max(1,floor(numberOfVariables/2))',...

'OutputFcn',[],...

'PlotFcns',[],...

'PrecondBandWidth',Inf,...

'ScaleProblem','none',...

'TolFun', 1e-6,...

'TolFunValue', 1e-6, ...

'TolPCG',0.1,...

'TolX',1e-6,...

'TypicalX','ones(numberOfVariables,1)',...

'UseParallel',false );

% If just 'defaults' passed in, return the default options in X

if nargin==1 && nargout <= 1 && strcmpi(FUN,'defaults')

xCurrent = defaultopt;

return

end

if nargin < 5

options = [];

if nargin < 4

UB = [];

if nargin < 3

LB = [];

end

problemInput = false;

if nargin == 1

if isa(FUN,'struct')

problemInput = true;

[FUN,xCurrent,LB,UB,options] = separateOptimStruct(FUN);

else % Single input and non-structure.

error(message('optim:lsqnonlin:InputArg'));

end

% No options passed. Set options directly to defaultopt after

allDefaultOpts = isempty(options);

% Prepare the options for the solver

options = prepareOptionsForSolver(options, 'lsqnonlin');

% Set options to default if no options were passed.

if allDefaultOpts

% Options are all default

options = defaultopt;

end

if nargin < 2 && ~problemInput

error(message('optim:lsqnonlin:NotEnoughInputs'))

end

% Check for non-double inputs

msg = isoptimargdbl('LSQNONLIN', {'X0','LB','UB'}, ...

xCurrent,LB, UB);

if ~isempty(msg)

error('optim:lsqnonlin:NonDoubleInput',msg);

end

caller = 'lsqnonlin';

[funfcn,mtxmpy,flags,sizes,~,xstart,lb,ub,EXITFLAG,Resnorm,FVAL,LAMBDA, ...

JACOB,OUTPUT,earlyTermination] = lsqnsetup(FUN,xCurrent,LB,UB,options,defaultopt, ...

allDefaultOpts,caller,nargout,length(varargin));

if earlyTermination

return % premature return because of problem detected in lsqnsetup()

end

xCurrent(:) = xstart; % reshape back to user shape before evaluation

% Catch any error in user objective during initial evaluation only

switch funfcn{1}

case 'fun'

try

initVals.F = feval(funfcn{3},xCurrent,varargin{:});

catch userFcn_ME

optim_ME = MException('optim:lsqnonlin:InvalidFUN', ...

getString(message('optim:lsqnonlin:InvalidFUN')));

userFcn_ME = addCause(userFcn_ME,optim_ME);

rethrow(userFcn_ME)

end

initVals.J = [];

case 'fungrad'

try

[initVals.F,initVals.J] = feval(funfcn{3},xCurrent,varargin{:});

catch userFcn_ME

optim_ME = MException('optim:lsqnonlin:InvalidFUN', ...

getString(message('optim:lsqnonlin:InvalidFUN')));

userFcn_ME = addCause(userFcn_ME,optim_ME);

rethrow(userFcn_ME)

end

case 'fun_then_grad'

try

initVals.F = feval(funfcn{3},xCurrent,varargin{:});

catch userFcn_ME

optim_ME = MException('optim:lsqnonlin:InvalidFUN', ...

getString(message('optim:lsqnonlin:InvalidFUN')));

userFcn_ME = addCause(userFcn_ME,optim_ME);

rethrow(userFcn_ME)

end

try

initVals.J = feval(funfcn{4},xCurrent,varargin{:});

catch userFcn_ME

optim_ME = MException('optim:lsqnonlin:InvalidFUN', ...

getString(message('optim:lsqnonlin:InvalidJacobFun')));

userFcn_ME = addCause(userFcn_ME,optim_ME);

rethrow(userFcn_ME)

end

otherwise

error(message('optim:lsqnonlin:UndefCallType'))

end

% Check for non-double data typed values returned by user functions

if ~isempty( isoptimargdbl('LSQNONLIN', {'F','J'}, initVals.F, initVals.J) )

error('optim:lsqnonlin:NonDoubleFunVal',getString(message('optimlib:commonMsgs:NonDoubleFunVal','LSQNONLIN')));

end

% Flag to determine whether to look up the exit msg.

flags.makeExitMsg = logical(flags.verbosity) || nargout > 4;

[xCurrent,Resnorm,FVAL,EXITFLAG,OUTPUT,LAMBDA,JACOB] = ...

lsqncommon(funfcn,xCurrent,lb,ub,options,defaultopt,allDefaultOpts,caller,...

initVals,sizes,flags,mtxmpy,varargin{:});

3. 运行结果