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.
[Read More] [GCG modeling guide] [Options] [Changes] [Download GCG]
How to use it#
ampl: option solver gcg; # change the solver
ampl: option gcg_options 'option1=value1 option2=value2'; # specify options
ampl: solve; # solve the problem
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.
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.