class_docstr: Hessian update strategy with full dimensional internal representation.
      
class_docstr_tok: ["Hessian","update","strategy","with","full","dimensional","internal","representation","."]
docstr: Compute the product of the internal matrix with the given vector.
  
  Parameters
  ----------
  p : array_like
      1-D array representing a vector.
  
  Returns
  -------
  Hp : array
      1-D represents the result of multiplying the approximation matrix
      by vector p.
docstr_tok: ["Compute","the","product","of","the","internal","matrix","with","the","given","vector",".","Parameters","--","--","--","--","--","p",":","array_like","1-D","array","representing","a","vector",".","Returns","--","--","--","-","Hp",":","array","1-D","represents","the","result","of","multiplying","the","approximation","matrix","by","vector","p","."]
code: def dot(self, p):
      """Compute the product of the internal matrix with the given vector.
  
      Parameters
      ----------
      p : array_like
          1-D array representing a vector.
  
      Returns
      -------
      Hp : array
          1-D represents the result of multiplying the approximation matrix
          by vector p.
      """
      if self.approx_type == "hess":
          return self._symv(1, self.B, p)
      else:
          return self._symv(1, self.H, p)
  
imports: import numpy
  from numpy.linalg import norm
  from scipy.linalg import get_blas_funcs
  from warnings import warn
label_desc: Use image node_id 6 for calling the FullHessianUpdateStrategy obj's underlying member method code with example usage: obj.dot(p) and returns: self, self