I'm trying to use the SCIP solver (http://scip.zib.de/). My input (1.lp) is in lpsolve format. It looks like this:
max: +2 x_0_0;
+x_0_0 <= 1;
+x_0_0 <= 1;
-3x_0_0 <= 0;
0 <= x_0_0 <= 1;
int x_0_0;
I run SCIP like this:
"c:\Program Files\SCIP\scip.exe" -f 1.lp -l 1.lp.out
However, SCIP generates this output:
SCIP version 3.0.0 [precision: 8 byte] [memory: block] [mode: optimized] [LP solver: SoPlex 1.7.0] [GitHash: c95600b]
Copyright (c) 2002-2012 Konrad-Zuse-Zentrum fuer Informationstechnik Berlin (ZIB)
External codes:
SoPlex 1.7.0 Linear Programming Solver developed at Zuse Institute Berlin (soplex.zib.de) [GitHash: 657dfe5]
cppad-20120101.3 Algorithmic Differentiation of C++ algorithms developed by B. Bell (www.coin-or.org/CppAD)
Ipopt 3.10.2 Interior Point Optimizer developed by A. Waechter et.al. (www.coin-or.org/Ipopt)
user parameter file <scip.set> not found - using default parameters
read problem <[...]1.lp>
============
input:
^
error reading file <[...]1.lp>
I guess that means it is choking on a whitespace... What am I doing wrong?
EDIT:
See my answer for details. After giving the input in CPLEX format, everything works fine.
The answer is that SCIP apparently cannot read lpsolve input files. The LP format they are referring to in this overview of readable file formats is in fact the CPLEX LP format.
Related
I am using the SCIP solver in AMPL mode, with SoPlex as the LP solver. This is the pre-compiled version available at https://scipopt.org/index.php#download.
I am solving mixed integer programs (MIPs) and would like to tell SoPlex to use the dual simplex algorithm for the LP subproblems. To do this, should I just specify lp/initalgorithm = d and lp/resolvealgorithm = d in scip.set? Or is there some way to send settings to the SoPlex subproblem solver, along the lines shown here?
Setting the parameters as you suggested is exactly correct. That guarantees that SoPlex will start with the dual simplex every time (which it would almost certainly also do by default).
I need to find a pool of solutions with AMPL (I am relatively new to it) using the option "poolstub" but I get an error when I try to retrive them. I will try to explain everything step by step. This is my code:
option solver cplex;
model my_model.mod;
data my_data.dat;
option cplex_options 'poolstub=multmip poolcapacity=10 populate=1 poolintensity=4 poolreplace=1';
solve;
At this point AMPLE gives me this:
CPLEX 20.1.0.0: poolstub=multmip
poolcapacity=10
populate=1
poolintensity=4
poolreplace=1
CPLEX 20.1.0.0: optimal solution; objective 4.153846154
66 dual simplex iterations (0 in phase I)
It seems like AMPL has not stored the solutions in the pool.
And in fact, if I try to retrive them with this code
for {i in 1..Current.npool} {
solution ('multmip' & i & '.sol');
display _varname, _var;
}
I get this error:
Bad suffix .npool for Initial
context: for {i in >>> 1..Current.npool} <<< {
Possible suffix values for Initial.suffix:
astatus exitcode message relax
result sstatus stage
for{...} { ? ampl: for{...} { ? ampl:
I have no integer variables, only real ones and I read that CPLEX doesn't support the populate method for linear programs. Could this be the problem or is something else missing? Thank you in advance
You have identified your problem correctly. Entity Initial does not have the npool suffix, which means the solver (in your case CPLEX) did not return one.
Gurobi can return that information for linear programs, but it seems to be identical to the optimal solution, so it would not give you any extra information (more info on AMPL-Gurobi options).
Here is an example AMPL script:
model net1.mod;
data net1.dat;
option solver gurobi;
option gurobi_options 'ams_stub=allopt ams_mode=1';
solve;
for {n in 1..Total_Cost.npool} {
solution ("allopt" & n & ".sol");
display Ship;
}
Output (on my machine):
Gurobi 9.1.1: ams_stub=allopt
ams_mode=2
ams_epsabs=0.5
Gurobi 9.1.1: optimal solution; objective 1819
1 simplex iterations
Alternative MIP solution 1, objective = 1819
1 alternative MIP solutions written to "allopt1.sol"
... "allopt1.sol".
Alternative solutions do not include dual variable values.
Best solution is available in "allopt1.sol".
suffix npool OUT;
Alternative MIP solution 1, objective = 1819
Ship :=
NE BOS 90
NE BWI 60
NE EWR 100
PITT NE 250
PITT SE 200
SE ATL 70
SE BWI 60
SE EWR 20
SE MCO 50
;
The files net1.mod and net1.dat are from the AMPL book.
When solving a MIP the solver can store sub-optimal solutions that it found along the way as they might be interesting for some reason to the modeler.
In terms of your LP, are you interested in the vertices the simplex algorithm visits?
I am currently using SCIP in a Linux environment and would like to move towards using PySCIPOpt as my research is slowly moving towards Machine Learning.
I have read the PySCIPOpt tutorial in Github as well as a document by S Maher and found them not being able to answer my question before I make the jump.
Will regular functions in SCIP such as read (problem) be available in PySCIPOpt too? This is because I have mps fils, pbo files and would not like to rewrite functions or classes that parse the file to fit them into the format found in Maher's document:
from pyscipopt import Model
scip = Model ()
x = scip.addVar(’x’, vtype=’C’)
y = scip.addVar(’y’, vtype=’I’)
scip. setObjective (x + y)
scip.addCons(2∗x + y∗y >= 10)
scip.optimize ()
I think you mean the commands that you use in the interactive shell? (there is no read function in SCIP).
All functions that you can use in PySCIPopt are wrapped in the scip.pyx file in the src directory of PySCIPopt.
So you can read a Problem with readProblem which wraps the SCIP API function SCIPreadProb
The code would look something like:
from pyscipopt import Model
model = Model()
model.readProblem('filename')
model.optimize()
So I am on windows 10 and am using multiple GPUs now in order to run the training of some machine learning model and this model is about GAN algorithm you can check the full code over here :
Here, I get to the point where there is need to reduce the sum from different GPU devices as following:
if len(devices) > 1:
with tf.name_scope('SumAcrossGPUs'), tf.device(None):
for var_idx, grad_shape in enumerate(self._grad_shapes):
g = [dev_grads[dev][var_idx][0] for dev in devices]
if np.prod(grad_shape): # nccl does not support zero-sized tensors
g = tf.contrib.nccl.all_sum(g)
for dev, gg in zip(devices, g):
dev_grads[dev][var_idx] = (gg, dev_grads[dev][var_idx][1])
Now in this part I get an error regarding NCCL, which I noticed that is not supported on windows it needs linux, therefore I am stuck here...what is the "work around solution" here??..how can I manage to use NCCL on windows or an alternative to the code above..is there any simple way to do that?...thanks in advance.
Note: I have checked out some stackoverflow issues already. However, no answer exist which can solve my problem.
I'm using the neos-server to solve a highly constrained MINLP, using the bonmin algorithm. Solving using either a Branch and Bound or the hybrid method. The input code is AMPL
I want to know if it's possible to output variable results for a failed run?
I've tried just about every bonmin option listed here
https://projects.coin-or.org/Bonmin/browser/stable/1.7/Bonmin/doc/BONMIN_UsersManual.pdf?format=raw
I don't know enough about optimization solvers to really understand all of these options.
I've tried different AMPL options but these only work if I have a successful run.
Ultimately, I want to output all the variables in my model with the values from the latest failed run.
This is my commands file
options bonmin_options "bonmin.bb_log_level 4 \
bonmin.algorithm B-BB print_level 6";
solve;
option display_precision 10;
display solve_result_num, solve_result;
display cost.result;
display _varname, _var;
Below is a the header output from a failed run. This provides outputs for all my variables but they are all 0
Solver : minco:Bonmin:AMPL
Start : 2017-08-30 11:20:12
End : 2017-08-30 11:26:34
Host : NEOS HTCondor Pool
Disclaimer:
This information is provided without any express or
implied warranty. In particular, there is no warranty
of any kind concerning the fitness of this
information for any particular purpose.
*************************************************************
File exists
You are using the solver bonmin-ampl.
Executing AMPL.
processing data.
processing commands.
Executing on prod-exec-1.neos-server.org
Presolve eliminates 20629 constraints and 18794 variables.
Substitution eliminates 8664 variables.
Adjusted problem:
12175 variables:
7093 nonlinear variables
5082 linear variables
10647 constraints; 63680 nonzeros
2553 nonlinear constraints
8094 linear constraints
8084 equality constraints
2563 inequality constraints
1 linear objective; 17 nonzeros.
Setting $presolve_fixeps >= 1.41e-14 could change presolve results.
Bonmin 1.8.4 using Cbc 2.9.6 and Ipopt 3.12.4
bonmin: bonmin.bb_log_level 4
bonmin.algorithm B-BB
print_level 6
Start reading options from stream.
Finished reading options from file.
Cbc3007W No integer variables - nothing to do
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
******************************************************************************
NLP0012I
Num Status Obj It time Location
NLP0014I 1 INFEAS 2.2729823 1144 178.86781
NLP0014I 2 INFEAS 2.2729823 1144 176.90811
Cbc3007W No integer variables - nothing to do
Cbc0006I The LP relaxation is infeasible or too expensive
"Finished"
bonmin: Infeasible problem
solve_result_num = 220
solve_result = infeasible
cost.result = infeasible