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License: Eclipse Public License 2.0
Python-MIP: collection of Python tools for the modeling and solution of Mixed-Integer Linear programs
License: Eclipse Public License 2.0
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?
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
Line 783 in b1edfec
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()
.
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?
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?
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.
I have a model with ~300,000 rows x 40,000 columns. Is it possible to use multiprocessing similar to speed up the optimization?
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?
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?
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).
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?
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))
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:
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?
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.
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:
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
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
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)
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.
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?
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.
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?
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.
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?
Hi
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!
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
Line 47 in b1d9afe
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
I was wondering if there are any plans to create a release for mip
on conda-forge
?
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?
now we have:
def get_objective(self) -> LinExpr:
return self.get_dbl_attr("ObjVal")
Sir, do you have any plan to support cplex solver?
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
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
self.vars[0]
AttributeError: 'SolverCbc' object has no attribute 'vars'
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?
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.
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?
model.var_by_name
does not work on current master. I have added a test in #63 which demonstrates the issue.
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
0x100a56000 - 0x1011dbfdf libobjc.A.dylib (756.2) <7C312627-43CB-3234-9324-4DEA92D59F50> /usr/lib/libobjc.A.dylib
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0x10139a000 - 0x1015fbffb libicucore.A.dylib (62141.0.1) <A0D63918-76E9-3C1B-B255-46F4C1DA7FE8> /usr/lib/libicucore.A.dylib
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0x101701000 - 0x101716ff7 libc++abi.dylib (400.17) <38C09CED-9090-3719-90F3-04A2749F5428> /usr/lib/libc++abi.dylib
0x10172d000 - 0x101780ff7 libc++.1.dylib (400.9.4) <9A60A190-6C34-339F-BB3D-AACE942009A4> /usr/lib/libc++.1.dylib
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0x101827000 - 0x1018abfc3 libcorecrypto.dylib (602.260.2) <01464D24-570C-3B83-9D18-467769E0FCDD> /usr/lib/system/libcorecrypto.dylib
0x1018c8000 - 0x101901ff7 libdispatch.dylib (1008.270.1) <97273678-E94C-3C8C-89F6-2E2020F4B43B> /usr/lib/system/libdispatch.dylib
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0x101982000 - 0x101982ff7 liblaunch.dylib (1336.261.2) <2B07E27E-D404-3E98-9D28-BCA641E5C479> /usr/lib/system/liblaunch.dylib
0x101989000 - 0x10198efff libmacho.dylib (927.0.3) <A377D608-77AB-3F6E-90F0-B4F251A5C12F> /usr/lib/system/libmacho.dylib
0x101995000 - 0x101997ffb libquarantine.dylib (86.220.1) <6D0BC770-7348-3608-9254-F7FFBD347634> /usr/lib/system/libquarantine.dylib
0x10199e000 - 0x10199fff7 libremovefile.dylib (45.200.2) <9FBEB2FF-EEBE-31BC-BCFC-C71F8D0E99B6> /usr/lib/system/libremovefile.dylib
0x1019a5000 - 0x1019bcff3 libsystem_asl.dylib (356.200.4) <A62A7249-38B8-33FA-9875-F1852590796C> /usr/lib/system/libsystem_asl.dylib
0x1019ca000 - 0x1019caff7 libsystem_blocks.dylib (73) <A453E8EE-860D-3CED-B5DC-BE54E9DB4348> /usr/lib/system/libsystem_blocks.dylib
0x1019cf000 - 0x101a56fff libsystem_c.dylib (1272.250.1) <7EDACF78-2FA3-35B8-B051-D70475A35117> /usr/lib/system/libsystem_c.dylib
0x101a7f000 - 0x101a82ffb libsystem_configuration.dylib (963.270.3) <2B4A836D-68A4-33E6-8D48-CD4486B03387> /usr/lib/system/libsystem_configuration.dylib
0x101a89000 - 0x101a8cff7 libsystem_coreservices.dylib (66) <719F75A4-74C5-3BA6-A09E-0C5A3E5889D7> /usr/lib/system/libsystem_coreservices.dylib
0x101a92000 - 0x101a98fff libsystem_darwin.dylib (1272.250.1) <EC9B39A5-9592-3577-8997-7DC721D20D8C> /usr/lib/system/libsystem_darwin.dylib
0x101aa3000 - 0x101aa9ff7 libsystem_dnssd.dylib (878.270.2) <E9A5ACCF-E35F-3909-AF0A-2A37CD217276> /usr/lib/system/libsystem_dnssd.dylib
0x101aaf000 - 0x101afaffb libsystem_info.dylib (517.200.9) <D09D5AE0-2FDC-3A6D-93EC-729F931B1457> /usr/lib/system/libsystem_info.dylib
0x101b11000 - 0x101b5cff7 libsystem_m.dylib (3158.200.7) <F19B6DB7-014F-3820-831F-389CCDA06EF6> /usr/lib/system/libsystem_m.dylib
0x101b6b000 - 0x101b95fff libsystem_malloc.dylib (166.270.1) <011F3AD0-8E6A-3A89-AE64-6E5F6840F30A> /usr/lib/system/libsystem_malloc.dylib
0x101ba5000 - 0x101bafff7 libsystem_networkextension.dylib (767.250.2) <FF06F13A-AEFE-3A27-A073-910EF78AEA36> /usr/lib/system/libsystem_networkextension.dylib
0x101bbb000 - 0x101bc2fff libsystem_notify.dylib (172.200.21) <145B5CFC-CF73-33CE-BD3D-E8DDE268FFDE> /usr/lib/system/libsystem_notify.dylib
0x101bc9000 - 0x101bccff7 libsystem_sandbox.dylib (851.270.1) <9494594B-5199-3186-82AB-5FF8BED6EE16> /usr/lib/system/libsystem_sandbox.dylib
0x101bd3000 - 0x101bd5ff3 libsystem_secinit.dylib (30.260.2) <EF1EA47B-7B22-35E8-BD9B-F7003DCB96AE> /usr/lib/system/libsystem_secinit.dylib
0x101bdb000 - 0x101c03ff7 libsystem_kernel.dylib (4903.271.2) <EA204E3C-870B-30DD-B4AF-D1BB66420D14> /usr/lib/system/libsystem_kernel.dylib
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0x101c45000 - 0x101c4cff3 libsystem_symptoms.dylib (820.267.1) <03F1C2DD-0F5A-3D9D-88F6-B26C0F94EB52> /usr/lib/system/libsystem_symptoms.dylib
0x101c53000 - 0x101c68fff libsystem_trace.dylib (906.260.1) <FC761C3B-5434-3A52-912D-F1B15FAA8EB2> /usr/lib/system/libsystem_trace.dylib
0x101c77000 - 0x101c7cffb libunwind.dylib (35.4) <24A97A67-F017-3CFC-B0D0-6BD0224B1336> /usr/lib/system/libunwind.dylib
0x101c84000 - 0x101cb3fff libxpc.dylib (1336.261.2) <7DEE2300-6D8E-3C00-9C63-E3E80D56B0C4> /usr/lib/system/libxpc.dylib
0x101ef6000 - 0x101ef7fff +_heapq.cpython-36m-darwin.so (???) <B3A1BA9C-14C5-D54A-EB65-2D408DDABF23> /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/lib-dynload/_heapq.cpython-36m-darwin.so
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0x103404000 - 0x103409fff +math.cpython-36m-darwin.so (???) <297B5753-6871-CD1C-487B-D3D8495637A6> /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/lib-dynload/math.cpython-36m-darwin.so
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0x1037b3000 - 0x1037bdfff +_socket.cpython-36m-darwin.so (???) <8068331B-D50A-03D6-CFE6-C68DA4DCF96F> /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/lib-dynload/_socket.cpython-36m-darwin.so
0x1037c9000 - 0x1037ccfff +select.cpython-36m-darwin.so (???) <146B3653-184D-28A2-1431-42C5BC5B3B13> /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/lib-dynload/select.cpython-36m-darwin.so
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0x104e46000 - 0x104e46fff +grp.cpython-36m-darwin.so (???) <AC9E9BBA-EFE0-5927-246B-FAD1D11535D9> /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/lib-dynload/grp.cpython-36m-darwin.so
0x104e89000 - 0x104e89ff7 +_scproxy.cpython-36m-darwin.so (???) <379C5B17-F029-A789-FDB4-A148ADE3B068> /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/lib-dynload/_scproxy.cpython-36m-darwin.so
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0x105337000 - 0x10536cfff libpcap.A.dylib (79.250.1) <C0893641-7DFF-3A33-BDAE-190FF54837E8> /usr/lib/libpcap.A.dylib
0x10547c000 - 0x105492fff libcoretls.dylib (155.220.1) <4C64BE3E-41E3-3020-8BB7-07E90C0C861C> /usr/lib/libcoretls.dylib
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0x105967000 - 0x105d14ffb com.apple.Foundation (6.9 - 1575.19) <A85BF812-B784-36B9-89BB-E29772B0708C> /System/Library/Frameworks/Foundation.framework/Versions/C/Foundation
0x105ff1000 - 0x106000fc7 com.apple.AppleFSCompression (96.200.3 - 1.0) <3CF60CE8-976E-3CB8-959D-DD0948C1C2DE> /System/Library/PrivateFrameworks/AppleFSCompression.framework/Versions/A/AppleFSCompression
0x10600a000 - 0x10600afff libOpenScriptingUtil.dylib (179.1) <4D603146-EDA5-3A74-9FF8-4F75D8BB9BC6> /usr/lib/libOpenScriptingUtil.dylib
0x10600f000 - 0x10600fff3 libauto.dylib (187) <3E3780E1-96F3-3A22-91C5-92F9A5805518> /usr/lib/libauto.dylib
0x106015000 - 0x106019ffb libpam.2.dylib (22.200.1) <586CF87F-349C-393D-AEEB-FB75F94A5EB7> /usr/lib/libpam.2.dylib
0x10601f000 - 0x1061fcfff libsqlite3.dylib (274.26) <6404BA3B-BCA4-301F-B2FE-8776105A2AA3> /usr/lib/libsqlite3.dylib
0x106227000 - 0x106234fff libxar.1.dylib (417.1) <39CCF46B-C81A-34B1-92A1-58C4E5DA846E> /usr/lib/libxar.1.dylib
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Calls made by this process:
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Calls made by all processes on this machine:
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VIRTUAL REGION
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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.
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
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?
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
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'
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
Currently there are various random print function calls. For example:
Line 1277 in a836a71
for example,
productSet = ["a" , "b"]
var x[p in product]
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:
.idea/
).project
and .pydevproject
).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.
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 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.
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.
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).
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!
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
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