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Python-MIP: collection of Python tools for the modeling and solution of Mixed-Integer Linear programs

License: Eclipse Public License 2.0

Python 92.48% Shell 4.36% Batchfile 0.02% BASIC 3.14%

integer-programming mip linear-programming optimization modeling optimization-tools optimization-algorithms optimization-framework

python-mip's Introduction

Python MIP (Mixed-Integer Linear Programming) Tools

Package website: http://python-mip.com

Python MIP is a collection of Python tools for the modeling and solution of Mixed-Integer Linear programs (MIPs). MIP syntax was inspired by Pulp. Just like CyLP it also provides access to advanced solver features like cut generation, lazy constraints, MIPstarts and solution Pools. Porting Pulp and Gurobi models should be quite easy.

Some of the main features of MIP are:

high level modeling: write your MIP models in Python as easily as in high level languages such as MathProg: operator overloading makes it easy to write linear expressions in Python;
full featured:
- cut generators and lazy constraints: work with strong formulations with a large number of constraints by generating only the required inequalities during the branch and cut search;
- solution pool: query the elite set of solutions found during the search;
- MIPStart: use a problem dependent heuristic to generate initial feasible solutions for the MIP search.
fast: the Python MIP package calls directly the native dynamic loadable library of the installed solver using the modern python CFFI module; models are efficiently stored and optimized by the solver and MIP transparently handles all communication with your Python code; it is also compatible with the Pypy just in time compiler, meaning that you can have a much better performance, up to 25 times faster for the creation of large MIPs, than the official Gurobi python interface which only runs on CPython;
multi solver: Python MIP was written to be deeply integrated with the C libraries of the open-source COIN-OR Branch-&-Cut CBC solver and the commercial solver Gurobi; all details of communicating with different solvers are handled by Python-MIP and you write only one solver independent code;
written in modern typed Python 3 (requires Python 3.8 or newer).

Examples

Many Python-MIP examples are documented at https://docs.python-mip.com/en/latest/examples.html

The code of these examples and additional ones (published in tutorials) can be downloaded at https://github.com/coin-or/python-mip/tree/master/examples

Documentation

The full Python-MIP documentation is available at https://docs.python-mip.com/en/latest/

A PDF version is also available: https://python-mip.readthedocs.io/_/downloads/en/latest/pdf/

Mailing list

Questions, suggestions and feature request can be posted at Discussions.

Build status

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python-mip's Issues

Gurobi does not use the max_mip_gap value

Hi,

I've set the max_mip_gap property to 1e-10
m.max_mip_gap = 1e-10
but Gurobi does not seem to use it as it still quits with

Optimal solution found (tolerance 1.00e-04)
Best objective 7.999600000000e+04, best bound 8.000000000000e+04, gap 0.0050%

Using CBC it does find an optimal solution (with value 80000)
Should I set it in a different way?

MIPStart is ignored by CBC?

I'm having some trouble with MIPStart. For the following toy model

import mip
from random import randint

class DelayedConstraintGenerator(mip.ConstrsGenerator):
    def generate_constrs(self, model):
        print("generating_constraints", model.status)
        model += model.vars[9] == 1

m = mip.Model(sense=mip.MAXIMIZE)
vs = [m.add_var(str(x), obj=1, var_type=mip.BINARY) for x in range(10)]

m.lazy_constrs_generator = DelayedConstraintGenerator()

m += mip.xsum(vs[0::2]) <= 3
m += vs[6] + vs[7] <= 1

L = []
for v in vs[:3]:
    L.append((v, 1))

m.start = L
m.validate_mip_start()
    
status = m.optimize()
print(status, m.objective_value)

I get the following output:

Checking feasibility of MIPStart
Model LP relaxation bound is 8.0
	fixing 0 to 1 ... ok, obj now: 8.0
	fixing 1 to 1 ... ok, obj now: 8.0
	fixing 2 to 1 ... ok, obj now: 8.0
Linear Programming relaxation of model with fixations from MIPStart is feasible.
MIP model may still be infeasible.
generating_constraints OptimizationStatus.OPTIMAL
generating_constraints OptimizationStatus.OPTIMAL
generating_constraints OptimizationStatus.OPTIMAL
generating_constraints OptimizationStatus.OPTIMAL
OptimizationStatus.OPTIMAL 8.0

but the output from CBC warns that it ignores my start values:

...
Starting MIP optimization
Option for preprocess changed from sos to off
Option for heuristicsOnOff changed from on to off
maxSavedSolutions was changed from 0 to 10
integerTolerance was changed from 1e-06 to 1e-06
Continuous objective value is 8 - 0.00 seconds
Cutoff increment increased from 1e-05 to 0.9999
Cbc0045I Warning: MIPstart solution is not valid, column names do not match, ignoring it.
Coin0506I Presolve 0 (-2) rows, 0 (-10) columns and 0 (-7) elements
Clp1000I sum of infeasibilities 0 - average nan, 0 fixed columns
Coin0506I Presolve 0 (0) rows, 0 (0) columns and 0 (0) elements
Clp0000I Optimal - objective value 8
...

I notice that the validate_mip_start method uses constraints to add the values onto the model, so it might not be checking the way MIPStart is done in CBC. I tried adding MIPStarts with smaller models and it worked, so I'm not sure why this particular use case is triggering this comment. Could you please look into this? Thank you!

CBC not found

I just managed to install mip for plain python 3.8.1 on windows (via pip install mip). Trying a simple test yields the following error:

C:\Users\user>python
Python 3.8.1 (tags/v3.8.1:1b293b6, Dec 18 2019, 22:39:24) [MSC v.1916 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from mip import *
Using Python-MIP package version 1.7.2
>>> m = Model()
cbc not found
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\user\AppData\Local\Programs\Python\Python38-32\lib\site-packages\mip\model.py", line 119, in __init__
    from mip.cbc import SolverCbc
  File "C:\Users\user\AppData\Local\Programs\Python\Python38-32\lib\site-packages\mip\cbc.py", line 455, in <module>
    Osi_getNumCols = cbclib.Osi_getNumCols
NameError: name 'cbclib' is not defined

I assumed that CBC is included with mip, as stated in

The default installation includes pre-compiled libraries of the MIP Solver CBC for Windows, Linux and MacOS. If you have the commercial solver Gurobi installed in your computer, Python-MIP will automatically use it as long as it finds the Gurobi dynamic loadable library. Gurobi is free for academic use and has an outstanding performance for solving MIPs. Instructions to make it accessible on different operating systems are included bellow.

I have no idea why this happens, and got no hint to solve this problem. What's wrong?

Get slack from constraint after optimization

I'm playing around with the simple binary knapsack demo, and want to compute the total knapsack weight (and slack) at the end of optimization. It seems this should be available, as the constr object has a slack attribute. However, when I execute m.constrs[0].slack (m is my model object), I get this:
`---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
in
----> 1 m.constrs[0].slack

d:\Continuum\anaconda3\lib\site-packages\mip\model.py in slack(self)
85 """
86
---> 87 return self.__model.solver.constr_get_slack(self)
88
89 @Property

d:\Continuum\anaconda3\lib\site-packages\mip\cbc.py in constr_get_slack(self, constr)
991 rhs = float(cbclib.Cbc_getRowRHS(self._model, constr.idx))
992 activity = float(pac[constr.idx])
--> 993 if constr.sense == "<":
994 return rhs - activity
995 elif constr.sense == ">":

AttributeError: 'Constr' object has no attribute 'sense'`

The error is confirmed, as I get AttributeError: 'Constr' object has no attribute 'sense' if I execute m.constrs[0].sense. This seems odd, as the documentation states that a constr is a linexpr object with the sense attribute, as opposed to an objective: "Note that the only difference of adding a constraint is the lack of a sense and a rhs.".

What am I missing?

Feature request: elastic constraints

Hi,

I'd like to suggest an elastic constraints feature, like the one pulp has:

https://pythonhosted.org/PuLP/pulp.html#elastic-constraints

Cheers,
Nick

Shadow prices

Hi,

PuLP provides the access to the restriction shadow prices.

Is it possible to access then with python-mip? If so, an example would be nice.

BR,
Santiago

1.6.0 does not install with python3.5

The minimum python version in the new version of setup.py is 3.6:

    python_requires='>3.6.0',

This means that version 1.6.0 cannot be installed using pip on python3.5. If I change the above line on a local copy of the repository all the tests pass.

Are there any features from python 3.6 which are needed? Python 3.5 is still widely used.

Include source code for CBC libraries

The CBC libraries in the git repository are pre-built. This makes it hard to debug any issues occuring within the library since we don't have the source.

It also makes the git repository very large (it is currently over 200mb). Git is designed with text in mind. It will store a diff of text files, but for binary files it stores a copy of every version. Here is some more info.

Fortunately it is usually not too difficult to build a shared library from source. It can be specified in the setup.py file. If you put the CBC source files somewhere I can try to do it.

AttributeError: 'SolverCbc' object has no attribute 'vars'

I recently updated the mip version from 1.3.15 to 1.4.3. I had a working code before I updated the package and when running the code with the updated package I get the following error:

Traceback (most recent call last):
File "optimization.py", line 48, in
status = optimization("cbc")
File "/home/xxxx/projects/xxxx/xxx/optimizer_helper.py", line 887, in optimization
opt.objective += (coeff * my_var[i])
File "/home/xxxx/anaconda3/lib/python3.6/site-packages/mip/model.py", line 942, in objective
return self.solver.get_objective()
File "/home/xxxx/anaconda3/lib/python3.6/site-packages/mip/cbc.py", line 409, in get_objective
return xsum(obj[j]*self.vars[j] for j in range(self.num_cols())
File "/home/xxxx/anaconda3/lib/python3.6/site-packages/mip/model.py", line 1944, in xsum
for term in terms:
File "/home/xxxx/anaconda3/lib/python3.6/site-packages/mip/cbc.py", line 410, in
if abs(obj[j]) >= 1e-15)
AttributeError: 'SolverCbc' object has no attribute 'vars'

I can however access opt.vars explicitly and print the number of variables (which matches the number I was getting when the code was working correctly on version 1.3.15). Any idea what might be the issue?

Slow extraction of variable values

Generally when I optimize a model, I need to afterwards extract the values of the variables. This can take quite long: often much longer than the actual optimization.

Yesterday I solved a version of the assignment problem with python-mip. The model has about 500,000 variables, all binary, and about 100,000 constraints. Optimizing the model takes 3 seconds. Obtaining the values of all binary variables, using the same method (var.x for each variable var) as in the examples in the documentation, takes nearly 4 minutes.

This is consistent with other python-mip programs I have written, and a small variety of systems on which I have run them. (If the details matter I can provide them.)

Is there perhaps a faster way to extract the values of the variables than reading var.x for each variable separately?

Issue with ConstrsGenerator

Hi,

I'm trying to implement lazy constraint generation by extending the ConstrsGenerator class. I've been following the docs here https://python-mip.readthedocs.io/en/latest/custom.html and the example here https://github.com/coin-or/python-mip/blob/master/examples/queens-lazy.py, but I can't seem to get it to work.

I have this small toy example that is failing and I'm not sure why.

import mip

class DelayedConstraintGenerator(mip.ConstrsGenerator):
    def generate_constrs(self, model):
        print("generating_constraints")
        for v in model.vars:
            print(v, v.x)
            model += v >= 5

m = mip.Model()

x = m.add_var('x', var_type=mip.INTEGER)
y = m.add_var('y', var_type=mip.INTEGER)

m += x + y, "objective"
m += x + y <= 10

m.lazy_constrs_generator = DelayedConstraintGenerator()
status = m.optimize()

print(status)
for v in m.vars:
    print(v, v.x)

Output from the example is:

generating_constraints
x 0.0
y 0.0
generating_constraints
x 0.0
y 0.0
OptimizationStatus.INFEASIBLE
x None
y None

I'm on Python-MIP 1.6.2 and Python 3.6.9.

Feature Request: max_seconds_no_change

Is it possible to have an additional stop-condition that stops if for x seconds the solution didn't improve? It would be also good to pass in the same time a minimum and a maximum seconds.

Tests, workflow, continuous integration

I appreciate all the work that is going into "python-mip" and more generally the coin-or project.

I haven't looked at the code in detail, but from skimming the repo I'm hesitant to use this library, since it seems a bit far removed from best practices regarding testing, working with continuous integration, and so forth. Here's a list of observations that make me a bit nervous:

Almost no tests. There are only 12 tests. Furthermore it seems that commits are being done directly to master, so there is no guarantee that the master is in a state where tests pass.
Examples not working. There's a folder with examples, but many of these fail when I try to run them (or run for a long time). The examples could be programmatically run as smoketests by Travis, ensuring that documented examples always work.
Code style. Most projects have some code style enforced by Travis or some other CI service, e.g. "black" or "flake8". This makes it easier to enter a project.
Committing to master. This is mentioned above, but there are commits in the history that are one-word changes. Every change should go through a PR so tests/codestyle/examples/etc can be run. Single-word commits can be squashed to a larger commit, so every commit in the main history is meaningful (as opposed to hundreds of very small changes).

I can help set up tests, code style, running examples, etc - but it's of not use if commits are performed directly to the master branch (since it bypasses Travis). Thoughts on the above? Want some help?

Support for sparse algebra and bulk constraint addition

Hi,

I integrated and heavily modified PuLP for my program GridCal.
Doing so, I made this module to handle sparse algebra. This way I can model algebraic equations (A @ b < c) with no for-loops in the program itself.

This feature is something these modelling frameworks often lack and they make models far more maintainable.

Support for Scipy linprog

Scipy has a LP solver

It would be nice to have integration with it. Even nicer would be to be able to generate the simplex matrices so that research would be immediate.

BR,
Santiago

Use logging module instead of print

Currently there are various random print function calls. For example:

python-mip/mip/model.py

Line 1277 in a836a71

print("Using Python-MIP package version {}".format(VERSION))

It would be great if it could be migrated to some logging module (i.e. https://docs.python.org/3/library/logging.html) so that package output could be managed/configured/suppressed easily.

cbclib not loading after version 1.6.0

Hi,

I'm getting this error when I run version 1.6.0 or newer

Osi_getNumCols = cbclib.Osi_getNumCols

NameError: name 'cbclib' is not defined

but when I dug past a try except I found the underlying issue was coming from this line

python-mip/mip/cbc.py

Line 47 in b1d9afe

cbclib = ffi.dlopen(libfile)

which was giving this error

{OSError}cannot load library <path to/cbc-c-windowsx86-64.dll>: error 0x7e

Any thoughts on what my be causing this?

Cheers,
Nick

Incremental Solving not supported?

Hello, I've been trying to learn how to do boolean modelling in Mixed Integer Programming, and I would like to get all solutions to the problem. Here is a simple example model where blocks with ports need to be connected, with some symmetry breaking on the blocks and ports (should have 3 solutions):

import mip

mm = mip.Model(solver_name="cbc")
mm.verbose = False

# Blocks
bus = mm.add_var("bus", 0, 1, var_type=mip.BINARY)
battery_1 = mm.add_var("battery_1", 0, 1, var_type=mip.BINARY)
battery_2 = mm.add_var("battery_2", 0, 1, var_type=mip.BINARY)

# Ports
bus_p1 = mm.add_var("bus_p1", 0, 1, var_type=mip.BINARY)
bus_p2 = mm.add_var("bus_p2", 0, 1, var_type=mip.BINARY)
battery_1_p = mm.add_var("battery_1_p", 0, 1, var_type=mip.BINARY)
battery_2_p = mm.add_var("battery_2_p", 0, 1, var_type=mip.BINARY)

# Constraints
mm.add_constr(battery_1 + battery_2 >= 0)
mm.add_constr(battery_1 + battery_2 <= 2)
mm.add_constr(bus == 1)
mm.add_constr(battery_1 >= battery_2)
mm.add_constr(battery_1 == battery_1_p)
mm.add_constr(battery_2 == battery_2_p)
mm.add_constr(bus >= bus_p1)
mm.add_constr(bus >= bus_p2)
mm.add_constr(bus_p1 <= bus_p2)
mm.add_constr(bus_p1 + bus_p2 == battery_1_p + battery_2_p)

count = 0
for i in range(0, 10):
    status = mm.optimize()
    if status == status.FEASIBLE or status == status.OPTIMAL:
        print(f"{bus}: {bus.x}")
        print(f"{battery_1}: {battery_1.x}")
        print(f"{battery_2}: {battery_2.x}")
    else:
        print("INFEASIBLE")
        break
    count += 1
    # or(a != a.x, b != b.x, c != by)
    flip = [1 - v if v.x else v for v in (bus, battery_1, battery_2)]
    mm.add_constr(mip.xsum(flip) >= 1)
print(f"CBC (Python-MIP) found {count} solutions")

However this code only gives me 1 solution, 10 times.

If I do the same with google Or-Tools, it works as expected:

from ortools.linear_solver import pywraplp as or_tools

om = or_tools.Solver("cbc", problem_type=or_tools.Solver.CBC_MIXED_INTEGER_PROGRAMMING)

# Instances
bus = om.BoolVar("bus")
battery_1 = om.BoolVar("battery_1")
battery_2 = om.BoolVar("battery_2")

# Ports
bus_p1 = om.BoolVar("bus_p1")
bus_p2 = om.BoolVar("bus_p2")
battery_1_p = om.BoolVar("battery_1_p")
battery_2_p = om.BoolVar("battery_2_p")

# Constraints
om.Add(battery_1 + battery_2 >= 0)
om.Add(battery_1 + battery_2 <= 2)
om.Add(bus == 1)
om.Add(battery_1 >= battery_2)
om.Add(battery_1 == battery_1_p)
om.Add(battery_2 == battery_2_p)
om.Add(bus >= bus_p1)
om.Add(bus >= bus_p2)
om.Add(bus_p1 <= bus_p2)
om.Add(bus_p1 + bus_p2 == battery_1_p + battery_2_p)

count = 0
# Limit number of solutions
for i in range(0, 10):
    status = om.Solve()
    if status == om.FEASIBLE or status == om.OPTIMAL:
        print(f"{bus}: {bus.solution_value()}")
        print(f"{battery_1}: {battery_1.solution_value()}")
        print(f"{battery_2}: {battery_2.solution_value()}")
    else:
        print("INFEASIBLE")
        break
    count += 1
    # or(a != a.x, b != b.x, c != by)
    flip = [1 - v if v.solution_value() else v for v in (bus, battery_1, battery_2)]
    om.Add(sum(flip) >= 1)
print(f"CBC (Or-Tools) found {count} solutions")

This gives the expected 3 solutions.

Is there anything I'm missing, like a different way to restrict solutions, or is python-mip just not intended to be used like this?

The constructor freezes

I have a situation where my program freezes when I call mip.Model(sense=mip.MAXIMIZE)

The program just freezes and becomes unresponsive. I receive no error message.

This happens after I have already run a number of models beforehand. The error is reproducible for me: It always freezes at exactly the same point, after having done dozens of other optimizations without issue. I can not give you a code example, because it's proprietary code. I also don't know how to reproduce it on a toy example. I have no idea why this one method call suddenly makes everything freeze. Especially because this happens in the constructor, so it can't have anything to do with the constraints or optimization function.

Also, using importlib.reload(mip) does not help.

Fails to work on Raspberry Pi

I am trying to use the MIP package on raspian buster but it fails with the error

Osi_getNumCols = cbclib.Osi_getNumCols
NameError: name 'cbclib' is not defined

Which is expected since only linux, windows and mac osx binaries are included by default.

However cbc is available in the debian repositories and can be installed using

sudo apt-get install coinor-cbc

which will place cbc binary in path linking to following so's

        /usr/lib/arm-linux-gnueabihf/libarmmem-${PLATFORM}.so => /usr/lib/arm-linux-gnueabihf/libarmmem-v7l.so (0x76fa9000)
        libCbcSolver.so.3 => /usr/lib/arm-linux-gnueabihf/libCbcSolver.so.3 (0x76ecc000)
        libCbc.so.3 => /usr/lib/arm-linux-gnueabihf/libCbc.so.3 (0x76dd4000)
        libpthread.so.0 => /lib/arm-linux-gnueabihf/libpthread.so.0 (0x76daa000)
        librt.so.1 => /lib/arm-linux-gnueabihf/librt.so.1 (0x76d93000)
        libCgl.so.1 => /usr/lib/arm-linux-gnueabihf/libCgl.so.1 (0x76c9a000)
        libOsiClp.so.1 => /usr/lib/arm-linux-gnueabihf/libOsiClp.so.1 (0x76c4c000)
        libClpSolver.so.1 => /usr/lib/arm-linux-gnueabihf/libClpSolver.so.1 (0x76bec000)
        libClp.so.1 => /usr/lib/arm-linux-gnueabihf/libClp.so.1 (0x76a7a000)
        libOsi.so.1 => /usr/lib/arm-linux-gnueabihf/libOsi.so.1 (0x76a24000)
        libCoinUtils.so.3 => /usr/lib/arm-linux-gnueabihf/libCoinUtils.so.3 (0x7690c000)
        libbz2.so.1.0 => /lib/arm-linux-gnueabihf/libbz2.so.1.0 (0x768ec000)
        libz.so.1 => /lib/arm-linux-gnueabihf/libz.so.1 (0x768c1000)
        liblapack.so.3 => /usr/lib/arm-linux-gnueabihf/liblapack.so.3 (0x76376000)
        libblas.so.3 => /usr/lib/arm-linux-gnueabihf/libblas.so.3 (0x76310000)
        libstdc++.so.6 => /usr/lib/arm-linux-gnueabihf/libstdc++.so.6 (0x761c9000)
        libm.so.6 => /lib/arm-linux-gnueabihf/libm.so.6 (0x76147000)
        libgcc_s.so.1 => /lib/arm-linux-gnueabihf/libgcc_s.so.1 (0x7611a000)
        libc.so.6 => /lib/arm-linux-gnueabihf/libc.so.6 (0x75fcc000)
        /lib/ld-linux-armhf.so.3 (0x76fbe000)
        libgfortran.so.5 => /usr/lib/arm-linux-gnueabihf/libgfortran.so.5 (0x75ef3000)

Is it possible to use these to get MIP working on buster?

Thanks!

any plan to support cplex?

Sir, do you have any plan to support cplex solver?

CBC interface only partially reassigns objective

Minimal example:

from mip import *
m = Model(sense=MAXIMIZE, solver_name=CBC)
v1 = m.add_var(var_type=BINARY)
v2 = m.add_var(var_type=BINARY)
m.add_constr(v1 + v2 == 1)

m.objective = 500 * v1
m.optimize()
> <OptimizationStatus.OPTIMAL: 0>
v1.x, v2.x
> (1, 0)

m.objective = v2
m.optimize()
> <OptimizationStatus.OPTIMAL: 0>
v1.x, v2.x
> (1, 0)

What I would have expected: (0, 1) as a result of the last call, because this assignment maximizes the updated objective v2. This is the behavior for gurobi, but not for CBC.

Suspected reason: In https://github.com/coin-or/python-mip/blob/master/mip/cbc.py#L1561-L1564 , it seems that, for variables not appearing in the new objective, their coefficients are not reset to 0. In the example above, the second optimization tries to maximize 500 v1 + v2 rather than v2. Possibly, the line

python-mip/mip/gurobi.py

Line 783 in b1edfec

self.flush_cols()

ensures that the same problem does not appear using Gurobi?

Versions: I am using mip version 1.7.2 (from pip) on Python 3.7.6 and OSX 10.15.3.

For the time being, is there a work-around that does not require creating a new model? I was thinking of setting m.objective = 0 * v1 + v2, but the 0 coefficient seems to get optimized away before set_objective().

Cbc solver returns INFEASIBLE instead of NO_SOLUTION_FOUND when timeout expires

I am able to reproduce this by imposing a limit of 1 second in line 37 of the queens.py example.
I believe the problem is in line 663 of mip/cbc.py, where it should return NO_SOLUTION_FOUND instead of INFEASIBLE?

Fail to access variable in lazy_constrs_generator callback

After seeing "Reduced cost fixing - 45 rows, 117 columns - restarting search" message, variable access in lazy_constrs_generator callback will fail. All variable names are changed.

I see that both heuristics and preprocess are turned off at the beginning. Why it still triggers mini B&B?

The strangest thing is that, this only happens when using PyPy3.6. The same program ran just fine under CPython3.6. I'm using PyPy because it's about 30% faster than CPython for my program.

Any clue or workaround? The full program with data is a little bit long, but I can provide that upon request.

Starting MIP optimization
Option for heuristicsOnOff changed from on to off
Option for preprocess changed from sos to off
Option for timeMode changed from cpu to elapsed
maxSavedSolutions was changed from 0 to 10
integerTolerance was changed from 1e-06 to 1e-06
Option for mergeCliques changed from off to after
Option for cliqueCuts changed from ifmove to off
Option for bkcliqueCuts changed from off to ifmove
Option for oddholewcCuts changed from off to ifmove
Continuous objective value is 52.3333 - 0.00 seconds
Cgl0015I Clique merge extended 0 cliques, 0 were dominated
Cutoff increment increased from 1e-05 to 0.9999
Cbc0013I At root node, 0 cuts changed objective from 52.333333 to 52.333333 in 1 passes
Cbc0014I Cut generator 0 (LazyConstraints) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is 1
Cbc0014I Cut generator 1 (Probing) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 2 (Gomory) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 3 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 4 (BKClique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 5 (OddHoleWC) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 6 (MixedIntegerRounding2) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 7 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 8 (TwoMirCuts) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 9 (ZeroHalf) - 0 row cuts average 0.0 elements, 0 column cuts (0 active)  in 0.000 seconds - new frequency is -100
Cbc0016I Integer solution of 92 found by strong branching after 182 iterations and 37 nodes (0.30 seconds)
Cbc0038I Full problem 228 rows 182 columns, reduced to 45 rows 117 columns
Cbc0044I Reduced cost fixing - 45 rows, 117 columns - restarting search
Cbc0012I Integer solution of 83 found by Previous solution after 0 iterations and 0 nodes (0.38 seconds)
From cffi callback <function SolverCbc.optimize.<locals>.cbc_cut_callback at 0x00007fffef4d52e0>:
Traceback (most recent call last):
  File "/localworkspaces/pypy3.6-v7.3.0-linux64/site-packages/mip/cbc.py", line 763, in cbc_cut_callback
    self.model.lazy_constrs_generator.generate_constrs(osi_model)
  File "/localworkspaces/pysandbox/stp14.py", line 142, in generate_constrs
    selected = [n for n, nv in nsv.items() if nv.x >= 0.99]
  File "/localworkspaces/pysandbox/stp14.py", line 142, in <listcomp>
    selected = [n for n, nv in nsv.items() if nv.x >= 0.99]
AttributeError: 'NoneType' object has no attribute 'x'

how to set mip.Model to use primal simplex in CBC?

I have a model (LP) with 3 million rows and almost 6 million cols. From Gurobi primal simplex seems to be a better choice, how can I set the model to use primal simplex if use CBC?

also, in this case I have to iteratively update the model (both rows and objective function), previously we reset the model if it would change too much compared to the previous solved model. In Gurobi I managed to find way to call .reset() from python-mip, how can I do this if use CBC?

Visual Code Python debugging tool crashes when trying to access "Model" object attributes before optimization

Consider the following code to model the maximum independent set problem to solve the maximum clique using MIP and NetworkX:

import mip
import networkx

n = 2 ** 3
g = networkx.binomial_tree(n)
networkx.add_star(g, [i for i in range(n)])
g1 = networkx.complement(g)

model = mip.Model("Independent Set")
x = [model.add_var(var_type=mip.BINARY) for _ in range(len(g1.nodes))]
model.objective = mip.maximize(mip.xsum(x[i] for i in range(len(g1.nodes))))
for (i, j) in g1.edges:
	model += x[i] + x[j] <= 1
model.optimize()
selected = [i for i in range(len(g1.nodes)) if x[i].x >= 0.99]
print(selected)
g2 = g.subgraph(selected)

If I try to access the model variables in the watch section, it gives he following message on terminal:

Information not available, model was not optimized yet.

And then, the debugger dies. I believe this is not what should happen, some help with this?

Can variable index on tuple set ?

Hi. I wonder if we can define variable indexing on tuple set? Like the following:
tuple set Arc = {<1,1>,<1,2>, <1,3>, <2,3>};

float+ var x[a in Arc]; //variable on tuple set Arc

Find N solutions

Is it possible to force optimization to continue until N integer solutions are found?

Example:
Let's say N=3 and there are 2 optimal solutions with the same value of objective function, like 10.0. Since I need 3 solutions, the third one would be a suboptimal one, where objective function is worse, like 11.0.

Is it possible to achieve with mip package?

cbc not loading win10 and pypy

Hi guys,

I get this error
"
Osi_getNumCols = cbclib.Osi_getNumCols
NameError: name 'cbclib' is not defined
"
if I try to solve any problem using PyPy3.6 v7.3.0.
Python-MIP version is 1.7.2 and win10
Is this because of x86/x64 ?
If it's the case, can you suggest me an older version working with pypy?
Thanks!

Fabio

use other solvers to initialize mip.Model

mip.Model.init provides a param solver to initialize the model instance, but it has not been used yet. The attribute self.solver is created by solver_name, i.e., Gurobi or CBC.

Can we switch to something like self.solver = solver to make user specified solver possible? I actually want to try another interior-point solver and already implemented the Solver class.

[QUESTION] conda-forge release?

I was wondering if there are any plans to create a release for mip on conda-forge?

Feature Request: Indicator Constraints

Following up from my boolean modelling issue, one of the nicest features of Gurobi, CPLEX and SCIP are indicator constraints. They make modelling complex boolean constraints extremely easy, in fact Gurobi and CPLEX also include boolean AND and OR constraints which are just lovely, though those are not particularly hard to model once you have indicator constraints.

Indicator constraints can be implemented using Big-M, Convex Hull, or SOS constraints. The Big-M formulation is the simplest but variables need tight bounds for it to work nicely. Convex Hull needs less tight bounds and has better numerical properties but is more complicated and needs more additional variables and constraints. I have no idea how they are encoded with SOS constraints.

Are there any plans to expose the indicator constraint features of Gurobi? And if so, would they be replicated in Python-MIP for CBC by using one of the encoding tricks above?

Editing the RHS of a constraint

I am porting a model from Gurobi to python-mip.
In Gurobipy I can add a constraint with a dummy right-hand-side:

constraint = model.addConstr( sum(x.values()) == -1 )

and then update the value later:

constraint.RHS = 123

I do this so that I can build my model once, and then solve it multiple times with different parameters.

Can I set the RHS directly with python-mip? At the moment I am deleting the constraint and replacing it with a new one. This works fine with the Gurobi solver but with CBC it fails (see #27).

Exit code -1073741819

Hi Folks,

Has anyone come across this error before, the last line printed out (i.e. not a stack trace) is

Process finished with exit code -1073741819 (0xC0000005)

The last few lines of print out are below. Any thoughts on whats going on would be appreciated, apologies for not having a minimum workable example, can't seem to create one yet.

Cbc0038I Pass 78: suminf. 0.13329 (50) obj. -1.31505e+07 iterations 17
Cbc0038I Pass 79: suminf. 13.12313 (67) obj. -1.31505e+07 iterations 130
Cbc0038I Pass 80: suminf. 2.27872 (14) obj. -1.31505e+07 iterations 77
Cbc0038I Pass 81: suminf. 2.07194 (53) obj. -1.31505e+07 iterations 44
Cbc0038I Pass 82: suminf. 4.74232 (33) obj. -1.31505e+07 iterations 57
Cbc0038I Pass 83: suminf. 8.89919 (22) obj. -1.31505e+07 iterations 63
Cbc0038I Pass 84: suminf. 6.20734 (57) obj. -1.31505e+07 iterations 52
Cbc0038I No solution found this major pass
Cbc0038I Before mini branch and bound, 4 integers at bound fixed and 1518 continuous
Cbc0038I Full problem 1541 rows 2534 columns, reduced to 1025 rows 1011 columns - 40 fixed gives 919, 918 - still too large
Cbc0038I Full problem 1541 rows 2534 columns, reduced to 797 rows 810 columns - too large
Cbc0038I Mini branch and bound did not improve solution (1.27 seconds)
Cbc0038I After 1.27 seconds - Feasibility pump exiting with objective of -1.31235e+07 - took 0.91 seconds
Cbc0012I Integer solution of -13123518 found by feasibility pump after 0 iterations and 0 nodes (1.29 seconds)
Cbc0012I Integer solution of -13179616 found by DiveCoefficient after 0 iterations and 0 nodes (1.53 seconds)
Cbc0012I Integer solution of -13183290 found by DiveCoefficient after 571 iterations and 0 nodes (2.42 seconds)
Cbc0031I 77 added rows had average density of 151.66234
Cbc0013I At root node, 77 cuts changed objective from -13213517 to -13213517 in 6 passes
Cbc0014I Cut generator 0 (Probing) - 2 row cuts average 51.0 elements, 7 column cuts (7 active) in 0.025 seconds - new frequency is -100
Cbc0014I Cut generator 1 (Gomory) - 401 row cuts average 109.2 elements, 0 column cuts (0 active) in 0.087 seconds - new frequency is -100
Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.015 seconds - new frequency is -100
Cbc0014I Cut generator 3 (BKClique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.001 seconds - new frequency is -100
Cbc0014I Cut generator 4 (OddHoleWC) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 5 (MixedIntegerRounding2) - 26 row cuts average 61.9 elements, 0 column cuts (0 active) in 0.168 seconds - new frequency is -100
Cbc0014I Cut generator 6 (FlowCover) - 1 row cuts average 33.0 elements, 0 column cuts (0 active) in 0.023 seconds - new frequency is -100
Cbc0014I Cut generator 7 (TwoMirCuts) - 465 row cuts average 104.5 elements, 0 column cuts (0 active) in 0.157 seconds - new frequency is -100
Cbc0010I After 0 nodes, 1 on tree, -13183290 best solution, best possible -13213516 (2.69 seconds)
Cbc0012I Integer solution of -13186937 found by DiveCoefficient after 978 iterations and 5 nodes (2.93 seconds)
Cbc0012I Integer solution of -13187594 found by DiveCoefficient after 1009 iterations and 6 nodes (3.11 seconds)
Cbc0012I Integer solution of -13190791 found by DiveCoefficient after 1055 iterations and 7 nodes (3.17 seconds)
Cbc0012I Integer solution of -1
Process finished with exit code -1073741819 (0xC0000005)

work with numpy

I test with the latest 1.6.6:

model = mip.Model()                                                                                                                    
x = model.add_var('x')                                                                                                                                                                                                                                                 
import numpy                                                                                                                           
print(x * numpy.int64(1))

This will produce the AssertionError, while numpy.int64(1) * x is OK, maybe we need a new way to assert numeric values?

Optimization time: can multiprocessing be used

I have a model with ~300,000 rows x 40,000 columns. Is it possible to use multiprocessing similar to speed up the optimization?

Coin versions

Since the CBS binary is distributed with the framework it is unclear exactly what version of CBC, CLP, CoinUtils etc is being used. I am assuming the master branch?

Would it be possible to get a python-mip version based on stable Coin libs? I now a lot have been happening with CBC C interface and I guess getting that to a stable version is the bottleneck?

Can variable index on string set?

for example,
productSet = ["a" , "b"]
var x[p in product]

Model without constraints + upper bounds

This is a valid MIP model that doesn't work with python-mip:

ub = 14  # any positive integer
m = Model("upper_bounds")
x = m.add_var(var_type=INTEGER, ub=ub)
m.objective = maximize(x)
m.optimize()
assert m.status == OptimizationStatus.OPTIMAL
assert round(m.objective_value) == ub

It fails on two levels:

The model class complains that there are no constraints. The upper bound is a constraint though.
The upper bound functionality of the variables doesn't appear to work properly.

Unable to return the objective as a linear expression after reading a .mps file

I think I've found a bug in version 1.3.13 of the PyPI MIP package. I'm reading a MIP from a .mps file and trying to see what the objective function is. But, rather than returning the linear expression I'm expecting, I'm getting an error message. I'm running this code in Python 3.7:

m = Model(sense=MINIMIZE, solver_name="cbc")
m.read('q_consol_4.mps')
m.objective

And I'm getting back:

Traceback (most recent call last):
File "/usr/local/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3296, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 1, in
m.objective
File "/usr/local/lib/python3.7/site-packages/mip/model.py", line 933, in objective
return self.solver.get_objective()
File "/usr/local/lib/python3.7/site-packages/mip/cbc.py", line 409, in get_objective
return xsum(obj[j]*self.vars[j] for j in range(self.num_cols)
TypeError: 'method' object cannot be interpreted as an integer

If I chase the error into the get_objective function, I find that self.vars doesn't seem to exist; if, in a debugging environment, I reach line 409 of cbc.py and enter

self.vars[0]

I get

Interestingly, the optimize() method seems to work just fine. So the objective-function data should be available, but I just can't return the linear expression with m.objective.

Is there a quick fix for this?

CBC error: max_seconds and max_nodes

I am trying to solve an NP-Hard problem and I set stopping criteria. When I set max_seconds for a relatively large number (larger than 200 seconds) kernel dies. However, when I set max_seconds for a smaller number kernel does not die.
I also tried max_nodes and had the same problem.
I have tired on Jupyter and Spyder (both installed via Anaconda).
I also tried for other large problems and the kernel always died when I set max_seconds which forces it to stop close to the optimal solution.
Note: when the solver_name = GUROBI is used, there is no issue. It works pretty well.

Remove IDE-specific files and folders

Shouldn't IDE-specific files and folders be removed from this repo and added to the gitignore? In this repo, there are files from at least three IDEs:

IntelliJ (folder .idea/)
Eclipse (files .project and .pydevproject)
KDevelop (folder .kdev4/)

If those specific IDEs aren't needed to build, test or publish this repo, I think they should be removed and added to gitignore.

Error removing constraints

Been using this package with pleasure.

However, I may have detected an error. When removing a constraint I got the following error:

File "/usr/local/lib/python3.7/site-packages/mip/cbc.py", line 767, in remove_constrs
cbclib.Cbc_deleteRows(self._model, idx)
TypeError: 'void(*)(void *, int, int *)' expects 3 arguments, got 2

If the interface from the link below is used, then indeed three arguments are expected:
https://www.coin-or.org/Doxygen/CoinAll/_cbc___c___interface_8h.html#a769db426ae492baea43be8c2d459924

I suspect the same holds for removing a variable.

`constr_get_index` does not work

Calling contr_get_index does not work on the current master branch.
The error I am getting is:

terminate called after throwing an instance of 'std::logic_error'
  what():  basic_string::_M_construct null not valid
Aborted

`model.var_by_name` does not work

model.var_by_name does not work on current master. I have added a test in #63 which demonstrates the issue.

max_gap is not working!?

I am trying to test the max_gap setting. If I understand correctly, it stops when the absolute relative distance between upper bound and current solution is lower than the max_gap value. I am assuming that then the solver should stop after seeing the first feasible solution, but it doesn't in the example. Maybe thats not the best example to test max_gap?

from itertools import product
from mip import Model, xsum, BINARY

p = [0, 3, 2, 5, 4, 2, 3, 4, 2, 4, 6, 0]

u = [[0, 0], [5, 1], [0, 4], [1, 4], [1, 3], [3, 2], [3, 1], [2, 4], [4, 0],
     [5, 2], [2, 5], [0, 0]]

c = [6, 8]

S = [[0, 1], [0, 2], [0, 3], [1, 4], [1, 5], [2, 9], [2, 10], [3, 8], [4, 6],
     [4, 7], [5, 9], [5, 10], [6, 8], [6, 9], [7, 8], [8, 11], [9, 11],
     [10, 11]]

(R, J, T) = (range(len(c)), range(len(p)), range(sum(p)))

model = Model()

x = [[model.add_var(name='x({},{})'.format(j, t), var_type=BINARY)
      for t in T] for j in J]

model.objective = xsum(x[len(J)-1][t] * t for t in T)

for j in J:
    model += xsum(x[j][t] for t in T) == 1

for (r, t) in product(R, T):
    model += xsum(u[j][r] * x[j][t2] for j in J
                  for t2 in range(max(0, t - p[j] + 1), t + 1)) <= c[r]

for (j, s) in S:
    model += xsum(t * x[s][t] - t * x[j][t] for t in T) >= p[j]


model.max_gap = 0.5 # <<<<<< 
model.optimize()

print('Schedule: ')
for (j, t) in product(J, T):
    if x[j][t].x >= 0.99:
        print('({},{})'.format(j, t))
print('Makespan = {}'.format(model.objective_value))

Segfault from CBC

When I try to solve a fairly large instance (hundreds of variables, thousands of constraints, all integer), I get a segfault from CBC:

Process:               Python [1666]
Path:                  /Library/Frameworks/Python.framework/Versions/3.6/Resources/Python.app/Contents/MacOS/Python
Identifier:            Python
Version:               3.6.2 (3.6.2)
Code Type:             X86-64 (Native)
Parent Process:        bash [809]
Responsible:           Python [1666]
User ID:               501

Date/Time:             2019-09-23 07:58:00.057 -0700
OS Version:            Mac OS X 10.14.6 (18G95)
Report Version:        12
Anonymous UUID:        3703FAD1-9129-3D73-97F8-AF5BB6F1B347

Sleep/Wake UUID:       2F0D8C4F-B37B-4E36-8981-88BF121B1B1F

Time Awake Since Boot: 3600 seconds
Time Since Wake:       1600 seconds

System Integrity Protection: enabled

Crashed Thread:        0  Dispatch queue: com.apple.main-thread

Exception Type:        EXC_BAD_ACCESS (SIGSEGV)
Exception Codes:       KERN_INVALID_ADDRESS at 0x00000001178e7000
Exception Note:        EXC_CORPSE_NOTIFY

Termination Signal:    Segmentation fault: 11
Termination Reason:    Namespace SIGNAL, Code 0xb
Terminating Process:   exc handler [1666]

VM Regions Near 0x1178e7000:
    MALLOC_LARGE           00000001178db000-00000001178e7000 [   48K] rw-/rwx SM=PRV  
--> 
    MALLOC_LARGE           00000001178ff000-0000000117985000 [  536K] rw-/rwx SM=PRV  

Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0   libsystem_platform.dylib      	0x0000000101c1fce9 _platform_memmove$VARIANT$Haswell + 41
1   cbc-c-darwin-x86-64.dylib     	0x0000000110d920a2 CbcModel::setBestSolution(double const*, int, double, bool) + 1730
2   cbc-c-darwin-x86-64.dylib     	0x0000000110e475ef CbcMain1(int, char const**, CbcModel&, int (*)(CbcModel*, int), CbcSolverUsefulData&) + 165615
3   cbc-c-darwin-x86-64.dylib     	0x0000000110d09ad8 Cbc_solve + 4456
4   libffi.dylib                  	0x000000010f7f9f6c ffi_call_unix64 + 76
5   libffi.dylib                  	0x000000010f7fa775 ffi_call + 785
6   _cffi_backend.cpython-36m-darwin.so	0x0000000110cd3327 cdata_call + 583
7   org.python.python             	0x00000001006ed674 _PyObject_FastCallDict + 180
8   org.python.python             	0x00000001007d2425 call_function + 165
9   org.python.python             	0x00000001007d3fe4 _PyEval_EvalFrameDefault + 5604
10  org.python.python             	0x00000001007d19a0 _PyEval_EvalCodeWithName + 2720
11  org.python.python             	0x00000001007d21ab fast_function + 219
12  org.python.python             	0x00000001007d25cb call_function + 587
13  org.python.python             	0x00000001007d3fe4 _PyEval_EvalFrameDefault + 5604
14  org.python.python             	0x00000001007d19a0 _PyEval_EvalCodeWithName + 2720
15  org.python.python             	0x00000001007d21ab fast_function + 219
16  org.python.python             	0x00000001007d25cb call_function + 587
17  org.python.python             	0x00000001007d3fe4 _PyEval_EvalFrameDefault + 5604
18  org.python.python             	0x00000001007d19a0 _PyEval_EvalCodeWithName + 2720
19  org.python.python             	0x00000001007d2836 _PyFunction_FastCallDict + 422
20  org.python.python             	0x00000001006ed724 _PyObject_FastCallDict + 356
21  org.python.python             	0x00000001006ed840 _PyObject_Call_Prepend + 208
22  org.python.python             	0x00000001006ed353 PyObject_Call + 99
23  org.python.python             	0x0000000100768b11 slot_tp_init + 81
24  org.python.python             	0x000000010075f2a4 type_call + 212
25  org.python.python             	0x00000001006ed674 _PyObject_FastCallDict + 180
26  org.python.python             	0x00000001007d2425 call_function + 165
27  org.python.python             	0x00000001007d3fe4 _PyEval_EvalFrameDefault + 5604
28  org.python.python             	0x00000001007d19a0 _PyEval_EvalCodeWithName + 2720
29  org.python.python             	0x00000001007d1b44 PyEval_EvalCode + 100
30  org.python.python             	0x000000010080e5fe PyRun_FileExFlags + 206
31  org.python.python             	0x000000010080e89f PyRun_SimpleFileExFlags + 447
32  org.python.python             	0x000000010082801a Py_Main + 3914
33  org.python.python             	0x0000000100000dfe 0x100000000 + 3582
34  org.python.python             	0x0000000100000c34 0x100000000 + 3124

Thread 1:
0   libsystem_pthread.dylib       	0x0000000101c323f0 start_wqthread + 0

Thread 2:
0   libsystem_pthread.dylib       	0x0000000101c323f0 start_wqthread + 0

Thread 0 crashed with X86 Thread State (64-bit):
  rax: 0x0000000117b99000  rbx: 0x0000000000000000  rcx: 0x0000000000011c40  rdx: 0x000000000001dc30
  rdi: 0x0000000117ba4ff0  rsi: 0x00000001178e7000  rbp: 0x00007fff5bf03890  rsp: 0x00007fff5bf03890
   r8: 0x0000000000000000   r9: 0x000000000000153f  r10: 0x000000000001e000  r11: 0x00000000002bdff0
  r12: 0x000000000001dc30  r13: 0x0000000117b99000  r14: 0x00000001020e9600  r15: 0x00000001178db010
  rip: 0x0000000101c1fce9  rfl: 0x0000000000010206  cr2: 0x00000001178e7000
  
Logical CPU:     6
Error Code:      0x00000004
Trap Number:     14


Binary Images:
       0x100000000 -        0x100000ff7 +org.python.python (3.6.2 - 3.6.2) <B22037D7-9A87-7B55-A815-0900174588B4> /Library/Frameworks/Python.framework/Versions/3.6/Resources/Python.app/Contents/MacOS/Python
       0x100003000 -        0x100447fe7  com.apple.CoreFoundation (6.9 - 1575.19) <B2850F42-CE01-3156-B121-FD4777290C8F> /System/Library/Frameworks/CoreFoundation.framework/Versions/A/CoreFoundation
       0x1006e2000 -        0x100907fef +org.python.python (3.6.2, [c] 2001-2017 Python Software Foundation. - 3.6.2) <25CF41F1-A8E4-5C9D-F0BD-7385A2815863> /Library/Frameworks/Python.framework/Versions/3.6/Python
       0x100a4f000 -        0x100a50ffb  libSystem.B.dylib (1252.250.1) <B1006948-7AD0-3CA9-81E0-833F4DD6BFB4> /usr/lib/libSystem.B.dylib
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                                VIRTUAL   REGION 
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Specifying max solutions not working

When passing in the optional parameter max_solutions to Model.optimize() I see the following output in the terminal

Welcome to the CBC MILP Solver 
Version: Trunk (unstable) 
Build Date: Oct 22 2019 
Revision Number: 2694 

command line - Cbc_C_Interface -max_solutions=1 -maxSavedSolutions=10 -integerT=1e-06 -solve -quit (default strategy 1)
No match for max_solutions - ? for list of commands

max_solutions should maybe be translated to maxSo(lutions)? (this is from cbc help prompt).

gurobi.py `get_objective` should return a LinExpr

now we have:

def get_objective(self) -> LinExpr:
        return self.get_dbl_attr("ObjVal")

constr_by_name does not work

I have tried the example but I cannot get the contrain by name. This is my code