parameters: (emp_cov, precision_, alpha)
return_statement: return gap
docstring: Expression of the dual gap convergence criterion
  
      The specific definition is given in Duchi "Projected Subgradient Methods
      for Learning Sparse Gaussians".
docstring_summary: Expression of the dual gap convergence criterion
docstring_tokens: Expression of the dual gap convergence criterion
function: def _dual_gap(emp_cov, precision_, alpha):
      """Expression of the dual gap convergence criterion
  
      The specific definition is given in Duchi "Projected Subgradient Methods
      for Learning Sparse Gaussians".
      """
      gap = np.sum(emp_cov * precision_)
      gap -= precision_.shape[0]
      gap += alpha * (np.abs(precision_).sum()
                      - np.abs(np.diag(precision_)).sum())
      return gap
function_tokens: def _dual_gap ( emp_cov , precision_ , alpha ) : gap = np . sum ( emp_cov * precision_ ) gap -= precision_ . shape [ 0 ] gap += alpha * ( np . abs ( precision_ ) . sum ( ) - np . abs ( np . diag ( precision_ ) ) . sum ( ) ) return gap