Gurobi network simplex

Answered

Rosario Scatamacchia

February 02, 2024 09:21

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Hi, 

I am testing instances of the transportation problem with the Gurobi's network simplex algorithm.

I can write down the corresponding LP models and then set the parameter NetworkAlg to 1. However, I have memory issues in very large instances due to the LP models.

Is there a way to launch the network simplex with Gurobi without specifically building an LP model (using only the input information about transportation costs, supplies and demands)? 

Thank you, 

Best regards

Rosario

0

• Official comment

  

  Simranjit Kaur

  • Gurobi Staff

  November 13, 2026 01:30

  This post is more than two years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support.
• 

  David Torres Sanchez

  • Gurobi Staff

  February 02, 2024 15:05 Edited

  Hi Rosario,

  For the command line we only accept the following Model File Formats.

  Alternatively, you can load the instances to another interface (e.g. a graph modelling tool) and use the Gurobi API to formulate the model and solve it (this won't require you to write any new file).
  I did recently for the min-cost flow DIMACs instances so I share the code below (no guarantees). Usage \(\texttt{python3 script_name.py path/to/dimacs.min}\)

  Cheers, 
  David

  import time
  import sys
  from pathlib import Path
  
  import gurobipy as gp
  from networkx import DiGraph
  
  
  def read_dimacs_flow(fname):
      g = DiGraph()
  
      ecount = 0
      with open(fname) as f:
          lines = f.readlines()
          for l in lines:
              if "c " in l:
                  continue
              if "n " in l:
                  if "min" in l:
                      continue
                  s = l.split(" ")
                  nid = int(s[1])
                  nsupply = int(s[2])
                  g.add_node(nid, demand=nsupply)
              if "a " in l:
                  s = l.split(" ")
                  source_id = int(s[1])
                  target_id = int(s[2])
                  low = int(s[3])
                  cap = int(s[4])
                  cost = int(s[5])
                  g.add_edge(source_id, target_id, capacity=cap, weight=cost)
                  ecount += 1
      print(f"Loaded graph with")
      print(f"Num of nodes: {g.number_of_nodes()}")
      print(f"Num of arcs:  {g.number_of_edges()} {ecount=}")
      return g
  
  
  def solve(g, name):
      with gp.Env(params={"NetworkAlg": 1}) as env, gp.Model(name, env=env) as model:
          edges, capacities, costs = gp.multidict(
              {(i, j): [d["capacity"], d["weight"]] for i, j, d in g.edges(data=True)}
          )
          nodes = list(g.nodes(data=True))
          x = {
              (i, j): model.addVar(
                  name=f"flow[{i},{j}]",
                  ub=capacities[i, j],
                  obj=costs[i, j],
              )
              for i, j in edges
          }
  
          flow_constrs = {}
          for n, data in nodes:
              dmd = 0
              if "demand" in data:
                  dmd = data["demand"]
              flow_constrs[n] = model.addConstr(
                  (
                      gp.quicksum(x[n, j] for j in g.successors(n))
                      - gp.quicksum(x[j, n] for j in g.predecessors(n))
                      == dmd
                  ),
                  name=f"flow_balance[{n}]",
              )
  
          model.write(f"{name}.mps.bz2")
          t = time.time()
          model.optimize()
          print(f"Using Gurobi\t{model.objval}\ttaken {time.time() - t}")
  
  
  def get_name(s):
      return str(Path(s).stem)
  
  
  g = read_dimacs_flow(sys.argv[1])
  solve(g, get_name(sys.argv[1]))

   

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• 

  Rosario Scatamacchia

  February 02, 2024 15:37

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  Hi David, 

  thank you for your fast reply and your advice!

  Cheers,

  Rosario

   

   

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