class_docstr: Instance of the pricing problem.
  
  For more details, see:
  
  Dash, S., Gunluk, O., and Wei, D. (2018). Boolean decision rules via column generation.
  In Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa- Bianchi, N.,
  and Garnett, R., editors, Advances in Neural Information Processing Systems 31,
  pages 4660–4670. Curran Associates, Inc.
docstr: Compute lower bound on higher-order solutions
docstr_tok: ["Compute","lower","bound","on","higher-order","solutions"]
code: def compute_LB(self, lambda1):
      """Compute lower bound on higher-order solutions"""
      Rp0 = self.rp.sum()
      self.LB = np.minimum(np.cumsum(np.sort(self.Rp)[::-1])[1:], Rp0)
      self.LB += np.sort(self.Rn)[-2::-1]
      self.LB -= lambda1 * np.arange(2, len(self.Rp) + 1)
      self.LB = self.v0 - self.LB
  
      # Lower bound specific to each singleton solution
      self.LB1 = self.v1 + self.Rp - Rp0 + lambda1
      if len(self.LB):
          self.LB1[self.LB1 < self.LB.min()] = self.LB.min()
  
imports: import numpy
  import pandas
label_desc: Use image node_id 3 for calling the PricingInstance obj's underlying member method code with example usage: obj.compute_LB(lambda1) without return types