Pyomo 5.3 GAMS求解器插件使用疑问及性能优化咨询

Hey there, let's tackle your questions about the Pyomo-GAMS plugin and the performance issues you're seeing:

1. Does Pyomo translate models to GAMS code, and where are the files stored? #

Yes, Pyomo does translate your model into GAMS code when using the gams solver plugin. By default, these files are created in a temporary directory and deleted automatically after the solver finishes running.

To keep the generated GAMS files for inspection:

• When initializing the solver, set keepfiles=True:

  solver = pe.SolverFactory('gams', keepfiles=True)
  

• The path to the temporary directory (and the .gms file inside) will be printed in the solver's log output. You can also explicitly set a working directory using the workdir parameter to control where files are saved:

  solver = pe.SolverFactory('gams', keepfiles=True, workdir='./gams_output')
  

2. How efficient is model translation, and how to optimize repeated small model solves? #

Model translation for small MIPs is generally fast—your performance bottleneck is almost certainly the overhead of spawning a new GAMS process for each solve (the shell integration approach).

For repeated solves of small models, try these optimizations:

• Use the GAMS Python API integration (more on this in question 3) to avoid restarting GAMS for every solve.
• Pre-export the model to a GAMS file once, then write a GAMS script that handles the parameterized loop (varying MaxWeight directly in GAMS) instead of looping in Python. This eliminates all inter-process communication overhead.
• Enable warmstart if your solver supports it (CPLEX does) to reuse previous solution information for subsequent solves with similar parameters.

3. What's the difference between the shell approach and the GAMS Python API? #

The key differences boil down to performance and flexibility:

• Shell integration: Pyomo calls GAMS as an external command-line tool. This means a new GAMS process is started for every solve, which adds significant overhead (especially for small, fast solves like yours). All communication happens via temporary files, leading to extra IO costs.
• GAMS Python API: This integrates GAMS directly into your Python process using a shared library. You only initialize GAMS once, then can solve multiple models (or parameterized variants) within the same session. This eliminates process startup overhead and allows direct, in-memory data exchange between Pyomo and GAMS, drastically speeding up repeated solves.

To use the API approach with Pyomo, ensure the GAMS Python bindings (included with standard GAMS installations) are available, and configure Pyomo to use the API instead of the shell.

4. Is there any documentation for this plugin? #

Unfortunately, official documentation for the Pyomo-GAMS plugin is quite sparse. Here are your best resources:

• The source code comments in the GAMS.py plugin file contain details on supported parameters and core behavior.
• Pyomo's official solver documentation has a brief section on GAMS integration (look for "GAMS Solver" in the Pyomo docs).
• Community resources: Check Stack Overflow for existing questions tagged pyomo and gams, or ask on the Pyomo mailing list for targeted help from developers and experienced users.
• GAMS's own documentation includes notes on integrating with Pyomo, which can clarify how translation and solver execution work under the hood.

Bonus: Conda Installation Note #

You're right that conda-forge only provides Pyomo 5.3 for specific OS/Python versions. Using pip install pyomo==5.3 is the correct workaround here if you specifically need that version for the GAMS plugin. If you can use a newer Pyomo version, check conda-forge again—newer releases may have better GAMS support and broader compatibility.

Fixes for Your Test Code #

I noticed a couple of small issues in your test code that you might want to fix:

1. Missing import for the time module
2. HTML entities (<=, >) instead of valid Python operators

Here's the corrected snippet:

import pyomo.environ as pe
import time  # Added missing import

# set up the model
model = pe.ConcreteModel()
model.MaxWeight = pe.Param(initialize=0, mutable=True)
model.Item = ['hammer','wrench','screwdriver','towel']
Weight = {'hammer':5,'wrench':7,'screwdriver':4,'towel':3}
Value = {'hammer':8,'wrench':3,'screwdriver':6,'towel':11}
model.x = pe.Var(model.Item, within=pe.Binary)
model.z = pe.Objective(expr=sum(Value[i] * model.x[i] for i in model.Item), sense=pe.maximize)
# Fixed operator from &lt;= to <=
model.constraint = pe.Constraint(expr=sum(Weight[i]*model.x[i] for i in model.Item) <= model.MaxWeight)

# time execution
solver_list = ['cbc', 'ipopt', 'gams', 'glpk']
for i, solver_name in enumerate(solver_list):
    solver = pe.SolverFactory(solver_name)
    print(solver_name)
    tic = time.time()
    for MaxWeight_i in range(0,30):
        model.MaxWeight = MaxWeight_i
        result = solver.solve(model)
        soln_items = list()
        for i in model.x:
            # Fixed operator from &gt; to >
            if pe.value(model.x[i]) > 0.5:
                soln_items.append(i)
        # print("Maximum Weight =", MaxWeight_i, soln_items)
    print("{:7.2f} s".format(time.time()-tic))
    print(" ")

内容的提问来源于stack exchange，提问作者Theo