Matlab F无解决方案我应该尝试哪些选项？

我正在一个更大的脚本中使用

fsolve

fsolve

返回

未找到解决方案

。我试图在一个for循环中找到一个矩阵的行，它服从某些限制。这对我前面的行

optimoptions（'fsolve'，'Algorithm'，'levenberg-marquardt'）

有帮助。现在，我被困住了，因为我不知道还有哪些选择是有意义的。我没有任何运气尝试不同的选择而不理解它们

这是返回未找到解决方案的代码

options1 = optimoptions('fsolve', 'Algorithm', 'levenberg-marquardt');
[gamma_k_1y, fval, exitflag, ~] = fsolve(@(gamma_k_1y) f_gamma( gamma_k_1y, m, bstar(k), Lstar, min(y,m) ), zeros(min(y,m),1), options1);

这是我想解决的函数

function F = f_gamma( gamma_1y, m, bstar_k, Lstar, y )
%UNTITLED3 Summary of this function goes here
%   Detailed explanation goes here
F(1) = sum( gamma_1y ); % row sum zero
F(2) = sum( gamma_1y.^2 ) - m; % elements squared = m
F(3) = sum( (Lstar(1,1:y).^2) * gamma_1y) - m * bstar_k; % first row squared times new row = m times element od bstar
n_assigned_rows = sum(~isnan(Lstar(:,1)));
for i=1:n_assigned_rows
     F(i+3) = Lstar(i,1:y) * gamma_1y; %element wise multiplication with each row except self zero
end

end

这些是我的变量

% -------------------------------------------------------------------
%  Generated by MATLAB on 18-Jul-2017 11:17:31
%  MATLAB version: 9.2.0.538062 (R2017a)
% -------------------------------------------------------------------

Lstar = ...
  [1.9193221763392923 -1.8182365289779376 -0.10108564736135461 0 0 0 0;
   -0.054995957568300745 0.045658192660796168 -1.8654711092538141 1.8748088741613205 ...
   0 0 0;
   -0.045613771137015049 0.011357535373129623 -1.0703617279866948 -1.0666452698891478 ...
   2.1712632336401225 0 0;
   -0.010566489657722691 -0.0548196464049537 0.78541897633795066 0.78253217221128113 ...
   0.77167395707940156 -2.2742389695659573 0;
   -0.024717877486905307 -0.059891167589263682 0.60794721966412446 0.60565273026541278 ...
   0.59702240468188983 0.62248826258542045 -2.348501572120679;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN;
   NaN NaN NaN NaN NaN NaN NaN];

alpha = [1.9193221763392923 -1.8182365289779376 -0.10108564736135461 0 ...
         0 0 0];

b = [1; 1; 1; 1; 2; 1; 1];

beta = [-0.054995957568300745 0.045658192660796168 -1.8654711092538141 ...
        1.8748088741613205 0 0 0];

beta_14 = [-0.054995957568300745; 0.045658192660796168; -1.8654711092538141; ...
           1.8748088741613205];

bstar = [0.15118578920369091; -0.010101525445522112; -0.020203050891044225; ...
         -0.030304576336566323; -0.040406101782088436; 0.020203050891044225; ...
         0.010101525445522112];

exitflag = -2;

fval = [-4.2566077443698074 -2.221444134718805 1.5553705289078517 -0.64353855088729561 ...
        0.042998187795194528 0.085996469204826287 0.12899460131301721 0.17199288121327205 ...
        ];

gamma_k_1y = [0.050858405964284731; 0.45946005395777284; -0.93242551108612626; ...
              -0.91454430220858973; -0.8706585765149597; -1.0001539165810418; ...
              -1.0491438979011476];

gamma_k_ym = saveVarsMat.gamma_k_ym; % <1x0 double> empty object

j = 7;

k = 6;

m = 7;

n = 20;

omega = ...
  [0.3779644730092272 0.70710678118654746 0 0 0 0 0;
   0.3779644730092272 -0.70710678118654746 0.70710678118654746 0 0 0 0;
   0.3779644730092272 0 -0.70710678118654746 0.70710678118654746 0 0 0;
   0.3779644730092272 0 0 -0.70710678118654746 0.70710678118654746 0 0;
   0.3779644730092272 0 0 0 -0.70710678118654746 0.70710678118654746 0;
   0.3779644730092272 0 0 0 0 -0.70710678118654746 0.70710678118654746;
   0.3779644730092272 0 0 0 0 0 -0.70710678118654746];



x = 1.9193221763392923;

y = 8;

z = -0.10108564736135461;

%-------------------------------------------------------------------
%MATLAB于2017年7月18日11:17:31生成
%MATLAB版本：9.2.0.538062（R2017a）
% -------------------------------------------------------------------
Lstar=。。。
[1.9193221763392923 -1.8182365289779376 -0.10108564736135461 0 0 0 0;
-0.054995957568300745 0.045658192660796168 -1.8654711092538141 1.8748088741613205 ...
0 0 0;
-0.045613771137015049 0.011357535373129623 -1.0703617279866948 -1.0666452698891478 ...
2.1712632336401225 0 0;
-0.010566489657722691 -0.0548196464049537 0.78541897633795066 0.78253217221128113 ...
0.77167395707940156 -2.2742389695659573 0;
-0.024717877486905307 -0.059891167589263682 0.60794721966412446 0.60565273026541278 ...
0.59702240468188983 0.62248826258542045 -2.348501572120679;
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alpha=[1.9193221763392923-1.8182365289779376-0.10108564736135461 0。。。
0 0 0];
b=[1；1；1；1；2；1；1]；
beta=[-0.0549957568300745 0.045658192660796168-1.8654711092538141。。。
1.8748088741613205 0 0 0];
贝塔系数14=[-0.054995957568300745；0.045658192660796168；-1.8654711092538141。。。
1.8748088741613205];
bstar=[0.15118578920369091；-0.01010152544522112；-0.02003050891044225。。。
-0.030304576336566323; -0.040406101782088436; 0.020203050891044225; ...
0.010101525445522112];
exitflag=-2；
fval=[-4.2566077443698074-2.2214441347188051.5553705289078517-0.64353855088729561。。。
0.042998187795194528 0.085996469204826287 0.12899460131301721 0.17199288121327205 ...
];
伽马系数=0.050858405964284731；0.45946005395777284；-0.93242551108612626。。。
-0.91454430220858973; -0.8706585765149597; -1.0001539165810418; ...
-1.0491438979011476];
gamma_k_ym=saveVarsMat.gamma_k_ym；%空对象
j=7；
k=6；
m=7；
n=20；
ω=。。。
[0.3779644730092272 0.70710678118654746 0 0 0 0 0;
0.3779644730092272 -0.70710678118654746 0.70710678118654746 0 0 0 0;
0.3779644730092272 0 -0.70710678118654746 0.70710678118654746 0 0 0;
0.3779644730092272 0 0 -0.70710678118654746 0.70710678118654746 0 0;
0.3779644730092272 0 0 0 -0.70710678118654746 0.70710678118654746 0;
0.3779644730092272 0 0 0 0 -0.70710678118654746 0.70710678118654746;
0.3779644730092272 0 0 0 0 0 -0.70710678118654746];
x=1.9193221763392923；
y=8；
z=-0.10108564736135461；

我知道有一个解决方案，因为R可以用
nleqslev
解决这个问题，同时指定选项
allowSingular=TRUE
也许这没什么，但是您尝试了另一个初始点。这里我看到你设置了x0=0（min（y，m），1）。为什么要从这一点开始呢？我只是问问


可能在循环中尝试x0=unifrnd（a，b）（您选择了a和b），因此它将尝试在每次迭代的不同点开始“fsolve”


希望能有帮助