class_docstr: Barrier optimization problem:
      minimize fun(x) - barrier_parameter*sum(log(s))
      subject to: constr_eq(x)     = 0
                constr_ineq(x) + s = 0
code: def _compute_function(self, f, c_ineq, s):
      # Use technique from Nocedal and Wright book, ref [3]_, p.576,
      # to guarantee constraints from `enforce_feasibility`
      # stay feasible along iterations.
      s[self.enforce_feasibility] = -c_ineq[self.enforce_feasibility]
      log_s = [np.log(s_i) if s_i > 0 else -np.inf for s_i in s]
      # Compute barrier objective function
      return f - self.barrier_parameter * np.sum(log_s)
  
imports: import scipy.sparse
  import numpy
  from .equality_constrained_sqp import equality_constrained_sqp
  from scipy.sparse.linalg import LinearOperator
label_desc: Use image node_id 6 for calling the BarrierSubproblem obj's underlying member method code with example usage: obj._compute_function(f, c_ineq, s) and returns: unknown