使用CPLEX Python API时cp.populate_solution_pool()运行耗时久，如何提速？

Got it, I’ve wrestled with this exact slowdown when working on large MILPs with CPLEX. Here are practical, actionable tweaks to cut down the runtime of populate_solution_pool():

• Limit the solution pool scope
  Don’t let CPLEX waste time chasing hundreds of near-identical solutions. Set hard caps on how many solutions to store and how much they can deviate from the best found value:

  # Max number of solutions to keep in the pool
  model.parameters.mip.pool.capacity = 50  
  # Stop searching if new solutions are within 5% of the best found value
  model.parameters.mip.pool.relgap = 0.05  
  

  Adjust these numbers based on how many distinct, meaningful solutions you actually need.

• Tweak the solution pool search intensity
  CPLEX lets you dial back how aggressively it hunts for alternative solutions. Lowering the intensity can drastically speed things up without losing too much quality:

  # 0 = fast mode, 1 = default, 2 = exhaustive search
  model.parameters.mip.pool.intensity = 0  
  # Replace worse solutions with better ones instead of hoarding all
  model.parameters.mip.pool.replace = 1  
  

• Enable parallel computing
  Leverage your CPU cores to split the search workload. Set the thread count to match your available physical cores (hyper-threading often gives diminishing returns here):

  model.parameters.threads = 8  # Adjust based on your machine's core count
  

• Optimize preprocessing and heuristics
  Shrink the problem size upfront and help CPLEX find feasible solutions faster:

  # Maximize preprocessing to reduce problem complexity
  model.parameters.preprocessing.reduce = 3  
  # Increase heuristic frequency to find feasible solutions quicker
  model.parameters.mip.strategy.heuristicfreq = 10  
  

• Set a hard time limit
  If you don’t need every possible solution, cap the runtime and take whatever valid solutions CPLEX finds in that window:

  # Stop after 10 minutes (600 seconds)
  model.parameters.timelimit = 600  
  

• Solve to optimality first, then populate the pool
  Sometimes letting CPLEX find the optimal solution first gives it a better starting point for hunting alternatives, instead of searching blindly:

  # First find the optimal solution to establish a baseline
  model.solve()  
  # Then populate the pool using the optimal solution's context
  model.populate_solution_pool()  
  

• Adjust branch-and-bound strategy
  Switch to a depth-first search to prioritize finding feasible solutions quickly (great if you just need multiple valid solutions, not necessarily all near-optimal ones):

  # 1 = depth-first search, 0 = default hybrid strategy
  model.parameters.mip.strategy.search = 1  
  

A quick note: These settings aren’t one-size-fits-all. Test them incrementally on your specific model—for example, if your problem has lots of symmetry, you might also want to tweak parameters.mip.strategy.symmetry to reduce redundant branching.

内容的提问来源于stack exchange，提问作者Ashkan Rezaee