COUENNE#

The COIN Couenne solver (COUENNE, Convex Over and Under ENvelopes for Nonlinear Estimation) is a spatial branch & bound algorithm that implements linearization, bound reduction, and branching techniques for Mixed-integer, Nonlinear Programming (MINLP) problems. The purpose of Couenne is to find global optima of nonconvex MINLPs.

Learn More | Features | Options

Download COUENNE

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 couenne # install COUENNE

# Activate your license (e.g., free ampl.com/ce or ampl.com/academia licenses):
$ python -m amplpy.modules activate <your-license-uuid>

How to use:

from amplpy import AMPL
ampl = AMPL()
...
ampl.solve(solver="couenne", couenne_options="option1=value1 option2=value2")

Learn more about what we have to offer to implement and deploy Optimization in Python.

AMPL APIs give access to AMPL from a programming language:

ampl: option solver couenne; # change the solver
ampl: option couenne_options 'option1=value1 option2=value2'; # specify options
ampl: solve; # solve the problem

Resources#

Features#

Algorithms#

Global: spatial branch-and-bound

Couenne builds convex relaxations of the nonconvex terms and branches on continuous and integer variables to find a global optimum. Time limit: bonmin.time_limit; Ipopt options apply to its local NLP solves.

Modeling with MP expressions#

MP expressions: logic, counting, complementarity (MP2NL)

Natively, Couenne solves nonlinear models with continuous and integer variables. MP’s expressions (==>, or, atmost, count, alldiff, piecewise-linear terms, …) and complements are not part of its native model type: through MP2NL, MP reformulates them into it first.

Call MP2NL as the solver and name Couenne inside it; MP’s options go into mp2nl_options, Couenne’s own into couenne_options.

MP reformulates logic with binary variables, which Couenne handles as integer variables.

ampl.solve(solver="mp2nl", mp2nl_options="solver=couenne cvt:compl=1")

ampl: option solver mp2nl;
ampl: option mp2nl_options 'solver=couenne cvt:compl=1';
ampl: solve;

More: Meta-driver MP2NL.

Solver options#

Full list of solver options:

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;

COUENNE solve result codes:

Solve result table (AMPL default ranges)
          0- 99 solved: optimal for an optimization problem, feasible for a satisfaction problem
        100-199 solved? solution candidate returned but error likely
        200-299 infeasible
        300-399 unbounded
        400-499 limit
        500-999 failure, no solution returned

Changelog#