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optlang - sympy based mathematical programming language

Home Page: http://optlang.readthedocs.org/

License: Apache License 2.0

Python 97.46% Jupyter Notebook 2.51% TeX 0.03%

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update travis config

Hi,

I think it would be helpful if travis could also run the tests for symengine and Python 3.6.

Conditional constraints using big-M method

The indicator_variable and active_when arguments for Constraints provide a straightforward way to define conditional constraints. Currently this is only supported by cplex, which has a native implementation for these.

However the functionality could be extended so other solvers fall back on a general big-M based implementation.

For example a constraint Constraint(expr, ub=a, indicator_variable=y, active_when=0) can be rewritten as Constraint(expr - M * y, ub=a) where M is a sufficiently large constant and y is a binary variable

Many cplex tests failing with Cplex' has no attribute 'solution' (cplex 12.7.1)

When running the nosetests locally many cplex tests fail with cplex 12.7.1.
This is not caught by the continuous integration because this tests are skipped on travis and appveyor.

======================================================================
14) ERROR: test_netlib_check_objective_value_25FV47 (test_netlib_cplex_interface.pyc)
----------------------------------------------------------------------
   Traceback (most recent call last):
    /usr/local/lib/python2.7/dist-packages/nose/case.py line 197 in runTest
      self.test(*self.arg)
    optlang/tests/test_netlib_cplex_interface.py line 60 in check_objval
      if problem.solution.get_status() == cplex.Cplex.solution.status.optimal:
   AttributeError: type object 'Cplex' has no attribute 'solution'
======================================================================
15) ERROR: test_netlib_check_objective_value_80BAU3B (test_netlib_cplex_interface.pyc)
----------------------------------------------------------------------
   Traceback (most recent call last):
    /usr/local/lib/python2.7/dist-packages/nose/case.py line 197 in runTest
      self.test(*self.arg)
    optlang/tests/test_netlib_cplex_interface.py line 60 in check_objval
      if problem.solution.get_status() == cplex.Cplex.solution.status.optimal:
   AttributeError: type object 'Cplex' has no attribute 'solution'
======================================================================
16) ERROR: test_netlib_check_objective_value_ADLITTLE (test_netlib_cplex_interface.pyc)
----------------------------------------------------------------------
   Traceback (most recent call last):
    /usr/local/lib/python2.7/dist-packages/nose/case.py line 197 in runTest
      self.test(*self.arg)
    optlang/tests/test_netlib_cplex_interface.py line 60 in check_objval
      if problem.solution.get_status() == cplex.Cplex.solution.status.optimal:
   AttributeError: type object 'Cplex' has no attribute 'solution'
======================================================================
17) ERROR: test_netlib_check_objective_value_AFIRO (test_netlib_cplex_interface.pyc)
----------------------------------------------------------------------
   Traceback (most recent call last):
    /usr/local/lib/python2.7/dist-packages/nose/case.py line 197 in runTest
      self.test(*self.arg)
    optlang/tests/test_netlib_cplex_interface.py line 60 in check_objval
      if problem.solution.get_status() == cplex.Cplex.solution.status.optimal:
   AttributeError: type object 'Cplex' has no attribute 'solution'
======================================================================
18) ERROR: test_netlib_check_objective_value_AGG2 (test_netlib_cplex_interface.pyc)
----------------------------------------------------------------------
   Traceback (most recent call last):
    /usr/local/lib/python2.7/dist-packages/nose/case.py line 197 in runTest
      self.test(*self.arg)
    optlang/tests/test_netlib_cplex_interface.py line 60 in check_objval
      if problem.solution.get_status() == cplex.Cplex.solution.status.optimal:
   AttributeError: type object 'Cplex' has no attribute 'solution'
======================================================================
19) ERROR: test_netlib_check_objective_value_AGG3 (test_netlib_cplex_interface.pyc)
----------------------------------------------------------------------
   Traceback (most recent call last):
    /usr/local/lib/python2.7/dist-packages/nose/case.py line 197 in runTest
      self.test(*self.arg)
    optlang/tests/test_netlib_cplex_interface.py line 60 in check_objval
      if problem.solution.get_status() == cplex.Cplex.solution.status.optimal:
   AttributeError: type object 'Cplex' has no attribute 'solution'
======================================================================
20) ERROR: test_netlib_check_objective_value_BANDM (test_netlib_cplex_interface.pyc)
----------------------------------------------------------------------
   Traceback (most recent call last):
    /usr/local/lib/python2.7/dist-packages/nose/case.py line 197 in runTest
      self.test(*self.arg)
    optlang/tests/test_netlib_cplex_interface.py line 60 in check_objval
      if problem.solution.get_status() == cplex.Cplex.solution.status.optimal:
   AttributeError: type object 'Cplex' has no attribute 'solution'
======================================================================
21) ERROR: test_netlib_check_objective_value_BEACONFD (test_netlib_cplex_interface.pyc)
----------------------------------------------------------------------
...

Getting primals from unsolved or infeasible models

Currently, _round_primal_to_bounds (called by .primal_values in some interfaces) throws an error if the primal values lie outside the bounds. However, that only makes sense for 'optimal' solutions. If the solver declared the solution as 'infeasible' that means by definition that one of the primal values violates a constraint (equality or inequality) so it is "ok" for infeasible solutions to lie outside of the bounds and no error should be thrown in that case.

Also creates quite a headache for cobrapy code since the Solution object is created regardless of the solver status and thus throws errors for infeasible solutions.

memory leak in glpk interface

swiglpk requires that a problem created with glp_problem is eventually freed with glp_delete_prob. Otherwise only the pointer to the problem will be deleted but not the actual problem matrices.

Iterating over a Container and issuing del container[item.name] should raise RuntimeError: container changed size during iteration

Fast initialization of empty problems

When populating the solver for the first time we can use the matrix initialization in GPLK

Example from https://github.com/opencobra/cobrapy/blob/devel/cobra/solvers/cglpk.pyx#L248
glp_load_matrix(glp, n_values, c_rows, c_cols, c_values).

Make a new release with latest develop changes

Hi all,
could you make a new release with the latest develop changes.
It is much easier to reference a release than the develop branch in setup.py, requirements.txt and tox.ini in depending projects.
Thanks so much
M

Uncaught cplex error for infeasible solutions

Getting the following for an infeasible problem with the cplex interface:

/home/cdiener/.local/lib/python3.5/site-packages/optlang/interface.py in primal(self)
    290         """The primal of variable (None if no solution exists)."""
    291         if self.problem:
--> 292             primal = self._get_primal()
    293             if primal is not None:
    294                 if self.type in ("integer", "binary"):

/home/cdiener/.local/lib/python3.5/site-packages/optlang/cplex_interface.py in _get_primal(self)
    170 
    171     def _get_primal(self):
--> 172         primal_from_solver = self.problem.problem.solution.get_values(self.name)
    173         return primal_from_solver
    174 

/home/cdiener/Downloads/ibm/cplex/python/3.5/x86-64_linux/cplex/_internal/_subinterfaces.py in get_values(self, *args)
   7566             return CPX_PROC.getx(self._env._e, self._cplex._lp, a, b)
   7567         return apply_freeform_two_args(
-> 7568             getx, self._cplex.variables.get_indices, args)
   7569 
   7570     def get_reduced_costs(self, *args):

/home/cdiener/Downloads/ibm/cplex/python/3.5/x86-64_linux/cplex/_internal/_aux_functions.py in apply_freeform_two_args(fn, convert, args)
     67         conarg0 = con(args[0])
     68         if isinstance(conarg0, six.integer_types):
---> 69             return fn(conarg0, conarg0)[0]
     70         else:
     71             raise TypeError("expecting name or index")

/home/cdiener/Downloads/ibm/cplex/python/3.5/x86-64_linux/cplex/_internal/_subinterfaces.py in getx(a, b)
   7564         """
   7565         def getx(a, b = self._cplex.variables.get_num() - 1):
-> 7566             return CPX_PROC.getx(self._env._e, self._cplex._lp, a, b)
   7567         return apply_freeform_two_args(
   7568             getx, self._cplex.variables.get_indices, args)

/home/cdiener/Downloads/ibm/cplex/python/3.5/x86-64_linux/cplex/_internal/_procedural.py in getx(env, lp, begin, end)
   1510     x    = _safeDoubleArray(xlen)
   1511     status = CR.CPXXgetx(env, lp, x, begin, end)
-> 1512     check_status(env, status)
   1513     return LAU.array_to_list(x, xlen)
   1514 

/home/cdiener/Downloads/ibm/cplex/python/3.5/x86-64_linux/cplex/_internal/_procedural.py in __call__(self, env, status, from_cb)
    222                 else:
    223                     error_string = geterrorstring(env, status)
--> 224             raise CplexSolverError(error_string, env, status)
    225 
    226 check_status = StatusChecker()

CplexSolverError: CPLEX Error  1217: No solution exists.

Actually it is a bit weird, because it happens after solving once and modifying the model by changing bounds. In some instances that seems to cause model.problem to reset.

Feel free to close if it is not reproducible.

cplex interface pretty much broken for QP problems with cplex 12.7

Getting lots of errors with Cplex 12.7:

self = <optlang.cplex_interface.Configuration object at 0x7fc07085c320>, value = 'auto'

    @solution_target.setter
    def solution_target(self, value):
        if self.problem is not None:
            if value is None:
                self.problem.problem.parameters.solutiontarget.reset()
            else:
                try:
                    solution_target = _SOLUTION_TARGETS.index(value)
                except ValueError:
                    raise ValueError(
                        "%s is not a valid solution target. Choose between %s" % (value, str(_SOLUTION_TARGETS)))
>               self.problem.problem.parameters.solutiontarget.set(solution_target)
E               AttributeError: 'RootParameterGroup' object has no attribute 'solutiontarget'

KeyError after renaming reactions

Hey Optlang-Devs,
I had to rename a few reactions in the model I am working on, and then realized that I had to remove one of the renamed reactions. This lead to the following error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-49-97e3d9b3721c> in <module>()
----> 1 print model.solve().f

/Users/clie/.virtualenvs/modeling/lib/python2.7/site-packages/cameo/core/solver_based_model.pyc in solve(self, solution_type, *args, **kwargs)
    493         Solution or LazySolution
    494         """
--> 495         solution = self.optimize(solution_type=solution_type, *args, **kwargs)
    496         if solution.status is not 'optimal':
    497             raise exceptions._OPTLANG_TO_EXCEPTIONS_DICT.get(solution.status, SolveError)(

/Users/clie/.virtualenvs/modeling/lib/python2.7/site-packages/cameo/core/solver_based_model.pyc in optimize(self, objective_sense, solution_type, **kwargs)
    476             self.objective.direction = original_direction
    477         else:
--> 478             self.solver.optimize()
    479         solution = solution_type(self)
    480         self.solution = solution

/Users/clie/.virtualenvs/modeling/lib/python2.7/site-packages/optlang/interface.pyc in optimize(self)
   1343             Solution status.
   1344         """
-> 1345         self.update()
   1346         status = self._optimize()
   1347         if status != OPTIMAL and self.configuration.presolve == "auto":

/Users/clie/.virtualenvs/modeling/lib/python2.7/site-packages/optlang/interface.pyc in update(self, callback)
   1319         rm_var = self._pending_modifications.rm_var
   1320         if len(rm_var) > 0:
-> 1321             self._remove_variables(rm_var)
   1322             self._pending_modifications.rm_var = []
   1323         callback()

/Users/clie/.virtualenvs/modeling/lib/python2.7/site-packages/optlang/cplex_interface.pyc in _remove_variables(self, variables)
    739         self.problem.variables.delete([variable.name for variable in variables])
    740         for variable in variables:
--> 741             del self._variables_to_constraints_mapping[variable.name]
    742             variable.problem = None
    743             del self._variables[variable.name]

KeyError: u'PAH'

Changing constraint bounds to a lower value if lb == ub

I found this bug:

from cameo.util import ProblemCache
from cameo.flux_analysis import moma
model = models.bigg.iJO1366
ref = model.solve().fluxes
model.reactions.PGI.knock_out()
cache = ProblemCache(model)
res = moma(model, reference=ref, cache=cache)  # first run is fine
ref['ACALD'] = -1
res = moma(model, reference=ref, cache=cache)  # second run is boom

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-879c5ea1306a> in <module>()
      8 res = moma(model, reference=ref, cache=cache)  # first run is fine
      9 ref['ACALD'] = -1
---> 10 res = moma(model, reference=ref, cache=cache)  # second run is boom

/Users/joao/Documents/repos/cameo/cameo/flux_analysis/simulation.py in moma(model, reference, cache, reactions, *args, **kwargs)
    186             reaction = model.reactions.get_by_id(rid)
    187             cache.add_constraint(constraint_id, create_constraint, update_constraint,
--> 188                                  cache.variables[var_id], reaction, flux_value)
    189 
    190         def create_objective(model, variables):

/Users/joao/Documents/repos/cameo/cameo/util.py in add_constraint(self, constraint_id, create, update, *args, **kwargs)
    145         if constraint_id in self.constraints:
    146             if update is not None:
--> 147                 update(self.model, self.constraints[constraint_id], *args, **kwargs)
    148         else:
    149             self.time_machine(

/Users/joao/Documents/repos/cameo/cameo/flux_analysis/simulation.py in update_constraint(model, constraint, var, reaction, flux_value)
    180             def update_constraint(model, constraint, var, reaction, flux_value):
    181                 if constraint.lb != flux_value:
--> 182                     constraint.ub = flux_value
    183                     constraint.lb = flux_value
    184 

/Users/joao/.virtualenvs/marsi/lib/python3.4/site-packages/optlang-0.6.3-py3.4.egg/optlang/cplex_interface.py in ub(self, value)
    271     @interface.Constraint.ub.setter
    272     def ub(self, value):
--> 273         self._check_valid_upper_bound(value)
    274         if getattr(self, 'problem', None) is not None:
    275             if self.indicator_variable is not None:

/Users/joao/.virtualenvs/marsi/lib/python3.4/site-packages/optlang-0.6.3-py3.4.egg/optlang/interface.py in _check_valid_upper_bound(self, value)
    594             raise TypeError("Constraint bounds must be numeric or None, not {}".format(type(value)))
    595         if value is not None and getattr(self, "lb", None) is not None and value < self.lb:
--> 596             raise ValueError("Cannot set an upper bound that is less than the lower bound.")
    597 
    598     @classmethod

ValueError: Cannot set an upper bound that is less than the lower bound.

I tried to change the ub and lb the other way around and I still get the same problem.

Constraint with constant offset

When constructing a constraint with an expression that ~~adds or~~ subtracts a constant (i.e. x-1), I get an error when setting the lower and upper bounds. I think this is probably due to converting the expression to standard form and not correctly modifying the upper/lower bounds in the correct order, but I haven't been able to pin it down.

Below is a simple example with the error. While c1, c2, and c3 are equivalent. c1 and c2 work. c3 throws an error.

>>> from optlang import *
>>> v=Variable('v')
>>> c1=Constraint(v,lb=5,ub=5)
>>> c2=Constraint(-v+5,lb=0,ub=0)
>>> c3=Constraint(v-5,lb=0,ub=0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/long/cobrapy/optlang/optlang/glpk_interface.py", line 151, in __init__
    super(Constraint, self).__init__(expression, sloppy=sloppy, *args, **kwargs)
  File "/home/long/cobrapy/optlang/optlang/interface.py", line 678, in __init__
    super(Constraint, self).__init__(expression, *args, **kwargs)
  File "/home/long/cobrapy/optlang/optlang/interface.py", line 431, in __init__
    self._expression = self._canonicalize(expression)
  File "/home/long/cobrapy/optlang/optlang/interface.py", line 749, in _canonicalize
    self.lb = self.lb - float(coeff)
  File "/home/long/cobrapy/optlang/optlang/glpk_interface.py", line 173, in lb
    super(Constraint, Constraint).lb.fset(self, value)
  File "/home/long/cobrapy/optlang/optlang/interface.py", line 691, in lb
    self._check_valid_lower_bound(value)
  File "/home/long/cobrapy/optlang/optlang/interface.py", line 631, in _check_valid_lower_bound
    raise ValueError("Cannot set a lower bound that is greater than the upper bound.")
ValueError: Cannot set a lower bound that is greater than the upper bound.

Run in Python 3.4.3 on Linux64 with optlang.version.==1.1.2

symbolic bounds

Done: When symbolic bounds are merged into optlang devel for the glpk interface.

Use the new helper functions from swiglpk

Just to keep track: would be awesome if optlang would use the new helper functions from swiglpk to get primals/duals and create intArrays/doubleArrays. This should give some speed boost.

Shadow Price Directionality

There seems to be some issue with the sign of the dual for constraints.

If the definition of the dual is the change in the objective that would occur with a one unit increase in the right hand side of the constraint, then for the following problem:

from optlang import *
m=Model()
v=Variable('v')
c=Constraint(v,lb=0,ub=0)
m.add([v,c])

c.dual should be +1 when maximizing v and should be -1 when minimizing v. Instead, I get +1 in both instances. Here's the rest of the code with output:

m.objective=Objective(v,direction='max')
m.optimize()
# c.dual == 1
m.objective=Objective(v,direction='min')
m.optimize()
# c.dual == 1

This is a very simplified example, but I am having trouble figuring out the sign for the shadow price in more complicated examples.

Information about supported solver versions missing

From @matthiaskoenig on March 11, 2017 23:18

I just tried to use the cobrapy 0.6.0a2 with cplex and got the following errors on cobra import with cplex (see below).
I am using cplex 12.6.0. I assume that the new optlang implementation uses some features which are in cplex 12.7.0. The old 0.5.* cobrapy versions worked without problems with cplex 12.6.0.

I could not find any information which solver versions are supported by the new 0.6.0. I.e. which cplex, glpk and gurobi work with 0.6.0? It would be nice to have this information in the installation information or the documentation.

import cobra
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-1-da884c934071> in <module>()
----> 1 import cobra

/home/mkoenig/git/cobrapy/cobra/__init__.py in <module>()
      9 from os.path import dirname as _dirname
     10 
---> 11 from cobra import design, flux_analysis, io
     12 from cobra.core import (
     13     DictList, Gene, Metabolite, Model, Object, Reaction, Species)

/home/mkoenig/git/cobrapy/cobra/design/__init__.py in <module>()
      3 from __future__ import absolute_import
      4 
----> 5 from cobra.design.design_algorithms import *

/home/mkoenig/git/cobrapy/cobra/design/design_algorithms.py in <module>()
      3 from __future__ import absolute_import
      4 
----> 5 from cobra.exceptions import DefunctError
      6 
      7 __all__ = ("set_up_optknock", "dual_problem")

/home/mkoenig/git/cobrapy/cobra/exceptions.py in <module>()
      3 from __future__ import absolute_import, print_function
      4 
----> 5 import optlang.interface
      6 
      7 

/usr/local/lib/python2.7/dist-packages/optlang/__init__.pyc in <module>()
     31 # Load classes from preferred solver interface
     32 if available_solvers['CPLEX']:
---> 33     from optlang.cplex_interface import Model, Variable, Constraint, Objective
     34 elif available_solvers["GUROBI"]:
     35     from optlang.gurobi_interface import Model, Variable, Constraint, Objective

/usr/local/lib/python2.7/dist-packages/optlang/cplex_interface.py in <module>()
    108     cplex.Cplex.solution.status.solution_limit: interface.SPECIAL,
    109     cplex.Cplex.solution.status.unbounded: interface.UNBOUNDED,
--> 110     cplex.Cplex.solution.status.relaxation_unbounded: interface.UNBOUNDED,
    111     # 102: interface.OPTIMAL # The same as cplex.Cplex.solution.status.optimal_tolerance
    112 }

AttributeError: SolutionStatus instance has no attribute 'relaxation_unbounded'

Copied from original issue: opencobra/cobrapy#453

catching solver-specific errors and re-raising with a common optlang error

My current problem in cobrapy is that when Reaction.flux is accessed before optimization, I get solver-specific errors, like CplexSolverError or GurobiError. These are raised when accessing optlang.Variable.primal, for example. I was wondering whether optlang, which is aware of the solvers available, could catch these and raise a unified optlang.SolverError (or similar) instead.

The problem is that cobrapy is not explicitly aware of the solvers available so I cannot import the errors in order to catch them specifically.

pip install error

I tried install optlang using pip and I got an error about "requirements.txt" missing. This is the error:

Downloading/unpacking optlang
    Downloading optlang-0.0.2.tar.gz
    Running setup.py (path:/tmp/pip_build_root/optlang/setup.py) egg_info for package optlang
        No handlers could be found for logger "optlang"
        Traceback (most recent call last):
            File "<string>", line 17, in <module>
            File "/tmp/pip_build_root/optlang/setup.py", line 27, in <module>
                with open('requirements.txt') as fhandle:
        IOError: [Errno 2] No such file or directory: 'requirements.txt'
        Complete output from command python setup.py egg_info:
        No handlers could be found for logger "optlang"

Traceback (most recent call last):

    File "<string>", line 17, in <module>

    File "/tmp/pip_build_root/optlang/setup.py", line 27, in <module>

        with open('requirements.txt') as fhandle:

IOError: [Errno 2] No such file or directory: 'requirements.txt'

Why are OrderedDicts used in the model interface?

Hi all,
I have to run a lot of FBA simulations and looking for places to speed them up. I am mainly interested in getting the reduced_costs, constraint_values, shadow_prices, ... as fast as possible from the model.

I had look at the interface.Model and wondered myself why are there OrderedDicts used for this values (in comparison to simple dict). This creates unnecessary overhead, costs performance and I don't see any benefits to it.
Or are there any algorithms relying on the order of the variables or constraint ids ?
If not all the OrderedDicts should be replaced with simple dicts in the interface.

In addition you should hash the names of your variables, constraints, ...
On every query of reduced costs, ... you reiterate and recalculate for instance the list of variable names
(variable.name for variable in self.variables)
There should be some property/field
variable_names, constraint_names
on the Model class which are just updated when new variables are added/removed.

    @property
    def reduced_costs(self):
        if self.is_integer:
            raise ValueError("Dual values are not well-defined for integer problems")
        try:
            return collections.OrderedDict(
                zip((variable.name for variable in self.variables), self.problem.solution.get_reduced_costs()))
        except CplexSolverError as err:
            raise SolverError(str(err))

    @property
    def constraint_values(self):
        try:
            return collections.OrderedDict(
                zip((constraint.name for constraint in self.constraints), self.problem.solution.get_activity_levels()))
        except CplexSolverError as err:
            raise SolverError(str(err))

[JOSS Review] Installation issues

On the first try installing I get a print statement that says "No installers available." on module import:

moorepants@garuda:~$ conda create -n optlang-review ipython
Fetching package metadata ...........
Solving package specifications: ..........

Package plan for installation in environment /home/moorepants/miniconda3/envs/optlang-review:

The following NEW packages will be INSTALLED:

    decorator:        4.0.10-py35_1
    ipython:          5.1.0-py35_0 
    ipython_genutils: 0.1.0-py35_0 
    openssl:          1.0.2j-0     
    path.py:          8.2.1-py35_0 
    pexpect:          4.0.1-py35_0 
    pickleshare:      0.7.4-py35_0 
    pip:              9.0.1-py35_1 
    prompt_toolkit:   1.0.9-py35_0 
    ptyprocess:       0.5.1-py35_0 
    pygments:         2.1.3-py35_0 
    python:           3.5.2-0      
    readline:         6.2-2        
    setuptools:       27.2.0-py35_0
    simplegeneric:    0.8.1-py35_1 
    six:              1.10.0-py35_0
    sqlite:           3.13.0-0     
    tk:               8.5.18-0     
    traitlets:        4.3.1-py35_0 
    wcwidth:          0.1.7-py35_0 
    wheel:            0.29.0-py35_0
    xz:               5.2.2-0      
    zlib:             1.2.8-3      

Linking packages ...
[      COMPLETE      ]|############################################################################| 100%
#
# To activate this environment, use:
# > source activate optlang-review
#
# To deactivate this environment, use:
# > source deactivate optlang-review
#

moorepants@garuda:~$ act optlang-review 
(optlang-review) moorepants@garuda:~$ pip install optlang
Collecting optlang
  Using cached optlang-1.0.0-py2.py3-none-any.whl
Requirement already satisfied: six>=1.9.0 in ./miniconda3/envs/optlang-review/lib/python3.5/site-packages (from optlang)
Collecting sympy>=1.0.0 (from optlang)
Collecting mpmath>=0.19 (from sympy>=1.0.0->optlang)
Installing collected packages: mpmath, sympy, optlang
Successfully installed mpmath-0.19 optlang-1.0.0 sympy-1.0
(optlang-review) moorepants@garuda:~$ ipython -c "import optlang"
No solvers available.
(optlang-review) moorepants@garuda:~$ ipython
Python 3.5.2 |Continuum Analytics, Inc.| (default, Jul  2 2016, 17:53:06) 
Type "copyright", "credits" or "license" for more information.

IPython 5.1.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import optlang
No solvers available.

In [2]:                                                                                                  
Do you really want to exit ([y]/n)? 
(optlang-review) moorepants@garuda:~$ python -c "import optlang"
No solvers available.

I'd suggest having all required dependencies installed automatically via the pip dependency resolution. Also, shouldn't import optlang raise and ImportError if the dependencies are not installed?

Gurobi interface gives incorrect primal for constraints

In the following model (zipped JSON) you have a constraint "test" whose primal will have the wrong sign after optimizing.

File: bad.zip

Basically you get stuff like this when having constraints with only a lower bound:

In [95]: mod.solver.optimize()
'optimal'

In [96]: print(mod.constraints.test)
test: 0.0 <= -1.0*Biomass_Ecoli_core_reverse_2cdba + 1.0*Biomass_Ecoli_core

In [97]: mod.constraints.test.primal
Out[97]: -0.8739215069684309

I think the problem is that optlang returns the slack variable from the associated optlang interface, however those change their sign depending on the type of constraint (negative for >= and positive for <=)...

Inconsistencies when adding a constant to the objective

So the following code will raise an "invalid expression" error with the GLPK interface but run fine with CPLEX and give a wrong result.

In [11]: model.objective.expression
Out[11]: -1.0*Biomass_Ecoli_core_reverse_2cdba + 1.0*Biomass_Ecoli_core

In [13]: model.optimize()
Out[13]: 'optimal'

In [15]: model.objective.value
Out[15]: 0.8739215069684304

In [17]: model.objective += 1.0  # error with glpk_interface

In [18]: model.optimize()
Out[18]: 'optimal'

In [19]: model.objective.value
Out[19]: 0.8739215069684304

In [20]: model.objective.expression
Out[20]: -1.0*Biomass_Ecoli_core_reverse_2cdba + 1.0*Biomass_Ecoli_core + 1.0

What is the motivation to prohibit adding constants to expressions in optlang? The case here is pretty obvious but there are some more subtle ones like optimizing distances to a reference solution. For instance
minimizing (x_known - x)**2) won't work in optlang since the expanded expression includes adding a constant (this objective is used in the method of global criterion for instance).

Efficient way of getting linear coefficients for constraints and objective

Like there are set_linear_coefficients it might be useful to have a get_linear_coefficients method.

Cplex integer status not correctly reset

So adding integer variables to a Cplex Model and removing them afterwards kind of breaks the optlang Model:

In [26]: mod.solver.is_integer
Out[26]: False

In [27]: mod.solver.optimize()
Out[27]: 'optimal'

In [28]: mod.solver.reduced_costs
---------------------------------------------------------------------------
CplexSolverError                          Traceback (most recent call last)
~/.local/lib/python3.6/site-packages/optlang/cplex_interface.py in _get_reduced_costs(self)
    758         try:
--> 759             return self.problem.solution.get_reduced_costs()
    760         except CplexSolverError as err:

~/.local/lib/python3.6/site-packages/cplex/_internal/_subinterfaces.py in get_reduced_costs(self, *args)
   7715         return apply_freeform_two_args(
-> 7716             getdj, self._cplex.variables.get_indices, args)
   7717

~/.local/lib/python3.6/site-packages/cplex/_internal/_aux_functions.py in apply_freeform_two_args(fn, convert, args)
    116     elif len(args) == 0:
--> 117         return fn(0)
    118     else:

~/.local/lib/python3.6/site-packages/cplex/_internal/_subinterfaces.py in getdj(a, b)
   7713         def getdj(a, b = self._cplex.variables.get_num() - 1):
-> 7714             return CPX_PROC.getdj(self._env._e, self._cplex._lp, a, b)
   7715         return apply_freeform_two_args(

~/.local/lib/python3.6/site-packages/cplex/_internal/_procedural.py in getdj(env, lp, begin, end)
   1602     status = CR.CPXXgetdj(env, lp, dj, begin, end)
-> 1603     check_status(env, status)
   1604     return LAU.array_to_list(dj, djlen)

~/.local/lib/python3.6/site-packages/cplex/_internal/_procedural.py in __call__(self, env, status, from_cb)
    269                     error_string = geterrorstring(env, status)
--> 270             raise CplexSolverError(error_string, env, status)
    271

CplexSolverError: CPLEX Error  1017: Not available for mixed-integer problems.

During handling of the above exception, another exception occurred:

SolverError                               Traceback (most recent call last)
<ipython-input-28-4f8266bcf3ca> in <module>()
----> 1 mod.solver.reduced_costs

~/.local/lib/python3.6/site-packages/optlang/interface.py in reduced_costs(self)
   1229         """
   1230         return collections.OrderedDict(
-> 1231             zip(self._get_variables_names(), self._get_reduced_costs())
   1232         )
   1233

~/.local/lib/python3.6/site-packages/optlang/cplex_interface.py in _get_reduced_costs(self)
    759             return self.problem.solution.get_reduced_costs()
    760         except CplexSolverError as err:
--> 761             raise SolverError(str(err))
    762
    763     def _get_constraint_values(self):

SolverError: CPLEX Error  1017: Not available for mixed-integer problems.

Bi-level MILPs

To support bi-level MILPs like OptKnock, it would be nice to add a convenience method for taking the dual of a problem and combining it with the original problem.

See here for COBRApy implementation:

https://github.com/opencobra/cobrapy/blob/a57e4d6c58f99a948c78280e0db11138b7676881/cobra/design/design_algorithms.py#L175

Doing this in COBRApy, I would up with a bug that prevents me from implementing the 3-level optimization RobustKnock, and I gave up on it. Maybe OptLang provides a core library that will be easier to debug.

Error creating quadratic objectives

Hi!

I'm stuck with a "maximum recursion depth exceeded in comparison" when creating a quadratic objective.

Code:

from cameo import models
from cameo.flux_analysis import moma
model = models.bigg.iJO1366
ref = model.solve().fluxes
model.reactions.PGI.knock_out()
res = moma(model, reference=ref)

Error:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-8-fecd89f0a3e4> in <module>()
----> 1 res = moma(model, reference=ref)

/Users/joao/Documents/repos/cameo/cameo/flux_analysis/simulation.py in moma(model, reference, cache, reactions, *args, **kwargs)
    193                                                     sloppy=True)
    194 
--> 195         cache.add_objective(create_objective, None, cache.variables.values())
    196 
    197         solution = model.solve()

/Users/joao/Documents/repos/cameo/cameo/util.py in add_objective(self, create, update, *args)
    184     def add_objective(self, create, update, *args):
    185         if self.objective is None:
--> 186             self.objective = create(self.model, *args)
    187             self.time_machine(
    188                 do=partial(setattr, self.model, 'objective', self.objective),

/Users/joao/Documents/repos/cameo/cameo/flux_analysis/simulation.py in create_objective(model, variables)
    191             return model.solver.interface.Objective(Add(*[FloatOne * var ** 2 for var in variables]),
    192                                                     direction="min",
--> 193                                                     sloppy=True)
    194 
    195         cache.add_objective(create_objective, None, cache.variables.values())

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/optlang/cplex_interface.py in __init__(self, *args, **kwargs)
    302         super(Objective, self).__init__(*args, **kwargs)
    303         self._expression_expired = False
--> 304         if not (self.is_Linear or self.is_Quadratic):
    305             raise ValueError("Cplex only supports linear and quadratic objectives.")
    306 

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/optlang/interface.py in is_Linear(self)
    464             return True
    465         else:
--> 466             poly = self.expression.as_poly(*self.variables)
    467             if poly is not None:
    468                 return poly.is_linear

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/sympy/core/basic.py in as_poly(self, *gens, **args)
    695 
    696         try:
--> 697             poly = Poly(self, *gens, **args)
    698 
    699             if not poly.is_Poly:

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/sympy/polys/polytools.py in __new__(cls, rep, *gens, **args)
     87                 return cls._from_poly(rep, opt)
     88             else:
---> 89                 return cls._from_expr(rep, opt)
     90 
     91     @classmethod

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/sympy/polys/polytools.py in _from_expr(cls, rep, opt)
    198         """Construct a polynomial from an expression. """
    199         rep, opt = _dict_from_expr(rep, opt)
--> 200         return cls._from_dict(rep, opt)
    201 
    202     def _hashable_content(self):

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/sympy/polys/polytools.py in _from_dict(cls, rep, opt)
    147                 rep[monom] = domain.convert(coeff)
    148 
--> 149         return cls.new(DMP.from_dict(rep, level, domain), *gens)
    150 
    151     @classmethod

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/sympy/polys/polyclasses.py in from_dict(cls, rep, lev, dom)
    257     def from_dict(cls, rep, lev, dom):
    258         """Construct and instance of ``cls`` from a ``dict`` representation. """
--> 259         return cls(dmp_from_dict(rep, lev, dom), dom, lev)
    260 
    261     @classmethod

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/sympy/polys/densebasic.py in dmp_from_dict(f, u, K)
   1013 
   1014         if coeff is not None:
-> 1015             h.append(dmp_from_dict(coeff, v, K))
   1016         else:
   1017             h.append(dmp_zero(v))

... last 1 frames repeated, from the frame below ...

/Users/joao/.virtualenvs/cameo-py3/lib/python3.4/site-packages/sympy/polys/densebasic.py in dmp_from_dict(f, u, K)
   1013 
   1014         if coeff is not None:
-> 1015             h.append(dmp_from_dict(coeff, v, K))
   1016         else:
   1017             h.append(dmp_zero(v))

RuntimeError: maximum recursion depth exceeded in comparison

Use weakrefs for Variable/Constraint/Configuration/Objective -> Model references

Otherwise there will be cyclic referencing. Adding __del__ methods to handle solver-specific cleanup routines will prevent the garbage collector from collecting these cycles.

Should solve #125 and other problems.

Gurobi can not solve MIP model

Hi,

the appended MIP problem solves fine with the glpk or cplex interface but is declared infeasible when using the gurobi interface. Tested Gurobi version was 7.02.

Model in JSON (renamed to .txt for upload)

Why force bounds to be integers when the variable type is an integer?

This seems like a perfectly valid problem setup:

Maximize x
subject to:
x<= 3.5
x must be integer

Of course the solution is 3... but optlang will tell you it is not a valid setup. perhaps this should be changed to a warning?

Best,
Josh

MOSEK support

I don't see a suggestive branch name but has there been any preliminary work to support the MOSEK Python API? Otherwise I figure there's no better way for me to get to know the optlang interface than implementing one myself 🙂

Recursion error with gurobi interface

Hi,

I am regularly getting the following errors:

---------------------------------------------------------------------------
RecursionError                            Traceback (most recent call last)
<timed exec> in <module>()

~/code/micom/micom/media.py in minimal_medium(community, community_growth, min_growth, minimize_components, open_exchanges)
    159                 medium[rxn.id] = -flux
    160             elif not export and flux > 0:
--> 161                 medium[rxn.id] = flux
    162 
    163     return medium

~/code/cobrapy/cobra/core/model.py in __exit__(self, type, value, traceback)
    971         """Pop the top context manager and trigger the undo functions"""
    972         context = self._contexts.pop()
--> 973         context.reset()
    974 
    975     def merge(self, right, prefix_existing=None, inplace=True,

~/code/cobrapy/cobra/util/context.py in reset(self)
     34         while self._history:
     35             entry = self._history.pop()
---> 36             entry()
     37 
     38 

~/code/cobrapy/cobra/util/solver.py in reset()
    157     if context:
    158         def reset():
--> 159             model.solver.objective = reverse_value
    160             model.solver.objective.direction = reverse_value.direction
    161 

~/.local/lib/python3.6/site-packages/optlang/gurobi_interface.py in objective(self, value)
    551     @objective.setter
    552     def objective(self, value):
--> 553         super(Model, self.__class__).objective.fset(self, value)
    554         expression = self._objective._expression
    555 

~/.local/lib/python3.6/site-packages/optlang/interface.py in objective(self, value)
   1180     @objective.setter
   1181     def objective(self, value):
-> 1182         self.update()
   1183         # try:  # Is this except ever needed?
   1184         for atom in sorted(value.expression.atoms(Variable), key=lambda v: v.name):

~/.local/lib/python3.6/site-packages/optlang/gurobi_interface.py in update(self)
    581 
    582     def update(self):
--> 583         super(Model, self).update(callback=self.problem.update)
    584 
    585     def _optimize(self):

~/.local/lib/python3.6/site-packages/optlang/interface.py in update(self, callback)
   1441         rm_var = self._pending_modifications.rm_var
   1442         if len(rm_var) > 0:
-> 1443             self._remove_variables(rm_var)
   1444             self._pending_modifications.rm_var = []
   1445         callback()

~/.local/lib/python3.6/site-packages/optlang/gurobi_interface.py in _remove_variables(self, variables)
    611     def _remove_variables(self, variables):
    612         # Not calling parent method to avoid expensive variable removal from sympy expressions
--> 613         self.objective._expression = self.objective.expression.xreplace({var: 0 for var in variables})
    614         for variable in variables:
    615             internal_variable = variable._internal_variable

~/.local/lib/python3.6/site-packages/optlang/interface.py in expression(self)
    453     def expression(self):
    454         """The mathematical expression defining the objective/constraint."""
--> 455         return self._get_expression()
    456 
    457     @property

~/.local/lib/python3.6/site-packages/optlang/gurobi_interface.py in _get_expression(self)
    363             terms = []
    364             for i in range(grb_obj.size()):
--> 365                 terms.append(grb_obj.getCoeff(i) * self.problem.variables[grb_obj.getVar(i).getAttr('VarName')])
    366             expression = sympy.Add._from_args(terms)
    367             # TODO implement quadratic objectives

~/.local/lib/python3.6/site-packages/optlang/interface.py in variables(self)
   1199     def variables(self):
   1200         """The model variables."""
-> 1201         self.update()
   1202         return self._variables
   1203 

... last 6 frames repeated, from the frame below ...

~/.local/lib/python3.6/site-packages/optlang/gurobi_interface.py in update(self)
    581 
    582     def update(self):
--> 583         super(Model, self).update(callback=self.problem.update)
    584 
    585     def _optimize(self):

RecursionError: maximum recursion depth exceeded while calling a Python object

Happens when adding MIP variables and constraints/objectives to a model followed by removing all of those again after solving.

optlang/GLPK determinism is broken

There seems to be an issue regarding the order of rows/cols provided for GLPK.

Running the exakt same input twice, GLPK guarantees to give the exact same output. This applies to the objective value as well as the variable values.

Anyway, feeding optlang/GLPK with the exact same data twice does in fact result in the same objective value, but the variable values differ over runs if there is more than one combination of variable values leading to an optimal objective value.

See this gist as an example binary problem where any combination of '1's in a matrix (subject to any row's sum being <= 1) leads to an optimal solution. Running this sample code several times shows how the result matrix differs.

I think, regarding determinism, optlang should provide the same output given the same input across runs.
Any thoughts on this?

Scaling in cplex interface

Hi, I experienced that modifying the objective in the cplex interface scales badly.

For a model with about variables setting the objective takes about half of the time when optimizing (see "cumtime" column which is the time the function call and all sub-calls takes):

 1174604 function calls (1050578 primitive calls) in 0.698 seconds

   Ordered by: cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.698    0.698 {built-in method builtins.exec}
        1    0.000    0.000    0.698    0.698 <string>:1(<module>)
        1    0.000    0.000    0.698    0.698 parsimonious.py:27(pfba)
        1    0.003    0.003    0.698    0.698 parsimonious.py:138(_pfba_optlang)
        1    0.000    0.000    0.471    0.471 parsimonious.py:102(add_pfba)
        2    0.000    0.000    0.415    0.207 cplex_interface.py:713(objective)
        4    0.000    0.000    0.295    0.074 _subinterfaces.py:3852(set_linear)

However for a model with ~200.000 variables it now takes 95% of the time.

         40679344 function calls (36037663 primitive calls) in 257.649 seconds

   Ordered by: cumulative time

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000  257.649  257.649 {built-in method builtins.exec}
        1    0.000    0.000  257.649  257.649 <string>:1(<module>)
        1    0.000    0.000  257.649  257.649 community.py:275(optimize)
        2    0.015    0.007  249.575  124.788 cplex_interface.py:713(objective)
        4    0.009    0.002  244.498   61.125 _subinterfaces.py:3852(set_linear)

I did not observe the same effect with the other solver interfaces.

optlang not working with latest cplex 12.7.1

Hi all,
I just installed the latest cplex 12.7.1.
Somehow optlang does not work with it. Looks like some attributes changed on the Cplex object.

In [2]: import optlang
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-2-b90a99bbabb7> in <module>()
----> 1 import optlang

/usr/local/lib/python2.7/dist-packages/optlang/__init__.pyc in <module>()
     31 # Load classes from preferred solver interface
     32 if available_solvers['CPLEX']:
---> 33     from optlang.cplex_interface import Model, Variable, Constraint, Objective
     34 elif available_solvers["GUROBI"]:
     35     from optlang.gurobi_interface import Model, Variable, Constraint, Objective

/usr/local/lib/python2.7/dist-packages/optlang/cplex_interface.py in <module>()
    116 _CPLEX_STATUS_TO_STATUS = {}
    117 for status_name, optlang_status in _STATUS_MAP.items():
--> 118     cplex_status = getattr(cplex.Cplex.solution.status, status_name, None)
    119     if cplex_status is not None:
    120         _CPLEX_STATUS_TO_STATUS[cplex_status] = optlang_status

AttributeError: type object 'Cplex' has no attribute 'solution'

py2.7, linux x64, optlang 1.1.3

The best
Matthias

"optlang": Naming conflict

Are you guys aware of the project

https://github.com/niessner/Opt

with the same name?

get_linear_coefficients does not behave the same for Objectives and Constraints

For objectives it returns zero coefficients for the passed variables for Constraints it does not. I think the problem is that glp_get_mat_row does not return indices or values for zero entries.

In [20]: model.constraints[0].get_linear_coefficients(model.variables)
Out[20]: 
{0 <= PGK <= 1000.0: 1.0,
 0 <= GAPD_reverse_459c1 <= 1000.0: -1.0,
 0 <= PGK_reverse_02696 <= 1000.0: -1.0,
 0 <= GAPD <= 1000.0: 1.0}

In [21]: model.objective.get_linear_coefficients(model.variables)
Out[21]: 
{0.0 <= PFL <= 1000.0: 0.0,
 0 <= ATPS4r <= 1000.0: 0.0,
 ...,
 0 <= D_LACt2 <= 1000.0: 0.0}

Test epic

Add appveyor as CI

Things that work on Linux and Mac might fail on Windows :)

model.objective.set_linear_coefficients chokes if no prior objective is set

Swap sympy with symengine

It seems like symengine can be installed quite easily with conda now.

Binary variables cannot be fixed using bounds

Good day optlang people !

I stumbled upon something that I might call an issue, but it might as well be a design choice from your side. I use MILPs, which contain binary variables. Sometimes, I need to constrain my model by assigning the value of the binary variable to either 1, or 0. However, in optlang.interface, lines 111-134, the methods __test_valid_lower_bound and __test_valid_upper_bound forbid such a thing by throwing a ValueError.

So far, my fix has been to use integer variables in the stead of binary variables, but I fear it might have an impact on the solver behavior, as some heuristics are specifically tailored to binary problems.

What say you, would it be a design choice or a small issue to fix ?

(That being said, I really love your software!)
Cheers,
Pierre

Sets

Are there any plans to implement sets over which parameters, variables and constraints can be defined? E.g. as implemented in Pyomo or GAMS.

I think this would be nice - but likely quite a substantial change to the codebase?

Accumulation of tmp files (.sav)

Hello,

I started using cplex with cameo.py two month ago, and pickled a lot of objects. It seems that cplex_interface.py use the functions getstate and setstate to transfer a cplex problem to/from bites, creating temp files .sav in the process. But these .sav files are not deleted afterward and using cameo for two month get me 200 000 files. sav, taking a total space of 409 Go in my hard drive.

Unless I misunderstand the use of these temp files, you should probably erase them after use?

check that cplex is functional

when having cplex in site-packages on py35,36 optlang tries to create a cplex interface but fails and throws exception

Traceback (most recent call last):
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/_pytest/config.py", line 325, in _getconftestmodules
    return self._path2confmods[path]
KeyError: local('/home/travis/build/biosustain/cameo/tests')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/_pytest/config.py", line 356, in _importconftest
    return self._conftestpath2mod[conftestpath]
KeyError: local('/home/travis/build/biosustain/cameo/tests/conftest.py')
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/_pytest/config.py", line 362, in _importconftest
    mod = conftestpath.pyimport()
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/py/_path/local.py", line 650, in pyimport
    __import__(modname)
  File "<frozen importlib._bootstrap>", line 969, in _find_and_load
  File "<frozen importlib._bootstrap>", line 958, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 664, in _load_unlocked
  File "<frozen importlib._bootstrap>", line 634, in _load_backward_compatible
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/_pytest/assertion/rewrite.py", line 216, in load_module
    py.builtin.exec_(co, mod.__dict__)
  File "/home/travis/build/biosustain/cameo/tests/conftest.py", line 3, in <module>
    import cobra.test
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/cobra/__init__.py", line 11, in <module>
    from cobra import design, flux_analysis, io
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/cobra/design/__init__.py", line 5, in <module>
    from cobra.design.design_algorithms import *
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/cobra/design/design_algorithms.py", line 5, in <module>
    from cobra.exceptions import DefunctError
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/cobra/exceptions.py", line 5, in <module>
    import optlang.interface
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/optlang/__init__.py", line 33, in <module>
    from optlang.cplex_interface import Model, Variable, Constraint, Objective
  File "/home/travis/virtualenv/python3.5.2/lib/python3.5/site-packages/optlang/cplex_interface.py", line 118, in <module>
    cplex_status = getattr(cplex.Cplex.solution.status, status_name, None)
AttributeError: module 'cplex' has no attribute 'Cplex'
ERROR: could not load /home/travis/build/biosustain/cameo/tests/conftest.py

Better behaviour would be to not make the cplex interface at all

_round_primal_to_bounds does not respect solver tolerances

Reproduce:

Create any model and set a variable upper bound very large (for instance 1e6) and maximize that variable.
try to get the primal_values and observe the error

Problem:

_round_primal_to_bounds uses its own absolute tolerance of 1e-5 but the solvers usually use a relative and absolute tolerance around 1e-9. For the case of large upper bounds the solver tolerance will be rtol*ub + atol which is 1e-9 * 1e6 + 1e-9 ~ 1e-3 > 1e-5. However the solution is completely feasible given the set solver tolerances.

_round_primal_to_bounds should either respect the solver tolerances or simply not check bounds feasibility since this is done by all lower level solver interfaces anyways.

scipy interface gives inconsistent solutions

With appended model the scipy interface declares the problem as infeasible even if all other interfaces find the correct optimal solution (0.874).

Reproduce:

Download the model in JSON

import json
import optlang

with open("textbook.json") as f:
    m = optlang.scipy_interface.Model.from_json(json.load(f))

m.optimize()

implement esolver support

esolver is an exact solver and cobrapy currently supports an interface to solve metabolic models using it. Since we are shifting to only using optlang interfaces in cobrapy, I raise this issue to help people comment and upvote this issue if they feel they would benefit from esolver in optlang as well.

float values output for integer variables

When formulating a problem with Variable objects of type integer, optimized outputted values for those variables are returned as floating point representations of the integer results. Is this behavior expected (as opposed to outputting integer values)?
Thanks!
~ Matej

gurobi objects can not be pickled

Caused by the tolerances which contain lambda functions (lambdas can never be pickled in Python).

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