title: R optim(){fExtremes} gets 0 hessian matrix
body: <p>I am using R {fExtremes} to find best parameters of GEV distribution for my data (a vector). but get the following error message</p>
  
  <blockquote>
    <p>Error in solve.default(fit$hessian) : Lapack routine dgesv: system is exactly singular: U[1,1] = 0</p>
  </blockquote>
  
  <p>I traced back to fit$hessian, found my hessian matrix is a sigular matrix, all of the elements are 0s. The source code (<a href="https://github.com/cran/fExtremes/blob/master/R/GevFit.R" rel="nofollow">https://github.com/cran/fExtremes/blob/master/R/GevFit.R</a>) of gevFit()  shows fit$hessian is calculated by optim(). The output parameters are exactly the same value as the initial parameters. I am wondering what could be the problems of my data that cause this problem? I copied my code here </p>
  
  <pre><code>&gt; min(sample);
  [1] 5.240909
  
  &gt; max(sample)
  [1] 175.8677
  
  &gt; length(sample)
  [1] 6789
  
  &gt; mean(sample)
  [1] 78.04107
  
  &gt;para&lt;-gevFit(sample, type = "mle")
  Error in solve.default(fit$hessian) : 
    Lapack routine dgesv: system is exactly singular: U[1,1] = 0
  
  fit = optim(theta, .gumLLH, hessian = TRUE, ..., tmp = data)
  &gt; fit
  
     $par
  
  xi   -0.3129225
  mu   72.5542497 
  beta  16.4450897 
  
  $value
  [1] 1e+06
  
  $counts
  function gradient 
         4       NA 
  
  $convergence
  [1] 0
  
  $message
  NULL
  
  
  
  $hessian
  
       xi  mu beta
  
  xi    0    0     0
  
  mu    0    0      0
  
  beta  0     0      0
  </code></pre>
  
  <p>I updated my dataset on google docs: 
  <a href="https://docs.google.com/spreadsheets/d/1IRRpjmdrrJPhNmfiLism_P0efV_Ot4HlEsa6kwMnljc/edit?usp=sharing" rel="nofollow">https://docs.google.com/spreadsheets/d/1IRRpjmdrrJPhNmfiLism_P0efV_Ot4HlEsa6kwMnljc/edit?usp=sharing</a></p>