GCG#
GCG is a generic decomposition solver for mixed-integer programs (MIPs). It automatically performs a Dantzig-Wolfe reformulation and runs a full-fledged branch-price-and-cut algorithm to solve it to optimality. Alternatively, GCG is able to automatically apply a Benders decomposition. No user interaction is necessary, thus GCG provides decomposition-based MIP solving technology to everyone.
Learn More | GCG modeling guide | Features guide | Options | Changes
How to use it#
How to install using amplpy:
# Install Python API for AMPL:
$ python -m pip install amplpy --upgrade
# Install AMPL & solver modules:
$ python -m amplpy.modules install gcg # install GCG
# Activate your license (e.g., free ampl.com/ce or ampl.com/courses licenses):
$ python -m amplpy.modules activate <your-license-uuid>
How to use:
from amplpy import AMPL
ampl = AMPL()
...
ampl.solve(solver="gcg", gcg_options="option1=value1 option2=value2")
Learn more about what we have to offer to implement and deploy Optimization in Python.
AMPL APIs are interfaces that allow developers to access the features of the AMPL interpreter from within a programming language. We have APIs available for:
ampl: option solver gcg; # change the solver
ampl: option gcg_options 'option1=value1 option2=value2'; # specify options
ampl: solve; # solve the problem
Resources#
GCG modeling guide#
GCG can apply Dantzig-Wolfe or Benders decomposition, either automatically or user-controlled, see types of structures GCG can detect and AMPL/GCG Colab examples.
Moreover, AMPL logical and some nonlinear expressions can be automatically linearized by the driver, see MP modeling guide.
Solver options#
Full list of solver options:
More details on solver options: Features guide.
Retrieving solutions#
The outcome of the last optimization is stored in the AMPL parameter solve_result_num and the relative message in
solve_result.
display solve_result_num, solve_result;
GCG solve result codes can be obtained by running gcg -! or ampl: shell "gcg -!";:
0- 99 solved: optimal for an optimization problem, feasible for a satisfaction problem
100-199 solved? solution candidate returned but error likely
150 solved? MP solution check failed (option sol:chk:fail)
200-299 infeasible
300-349 unbounded, feasible solution returned
350-399 unbounded, no feasible solution returned
400-449 limit, feasible: stopped, e.g., on iterations or Ctrl-C
450-469 limit, problem is either infeasible or unbounded
470-499 limit, no solution returned
500-999 failure, no solution returned
550 failure: numeric issue, no feasible solution
For general information, see MP result codes guide.