<div dir="ltr"><div>Hi Ambros.</div><div><br></div><div>Thank you for your response. Your explanation was aligned with my assumption... Thank you for clarifying.</div><div><br></div><div>SCIP searches for an optimal solution. For some MINLP problem, I would like SCIP to return a solution (the first solution it may find is sufficient). I was reading that IPOPT is integrated within SCIP, however I was unable to trigger ipopt for these problem. Any help would be appreciated.</div><div><br></div><div>For reference, I am using SCIP from interactive/batch(script) mode. I installed SCIP using the binaries posted on the website.</div><div><br></div><div>Also, I would like to know if it is possible to pause SCIP and retrieve its state and, then, continue from the saved state from the interactive/batch(script) mode. If so, how?</div><div><br></div><div>Thank you in advance.</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, Feb 20, 2018 at 1:49 AM, Ambros Gleixner <span dir="ltr"><<a href="mailto:gleixner@zib.de" target="_blank">gleixner@zib.de</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Vincent,<br>
<br>
SCIP (like virtually all solvers based on floating-point arithmetics) uses tolerances to check feasibility and integrality. Anything with a fractionality below 1e-9 (parameter numerics/epsilon) is consider integral, so also the value 0.999999999999998 that you saw in your solution. You can use SCIPisIntegral() to check or SCIPround() to round solution values in the C API.<br>
<br>
Best,<br>
Ambros<span class=""><br>
<br>
<br>
Am 20.02.2018 um 03:40 schrieb Vincent Mirian:<br>
</span><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><span class="">
Hi Benny.<br>
<br>
Thank you for your response. Currently, I am using the LP format with SCIP.<br>
<br>
I create a problem (attached). In the problem I defined a variable AssignC4BLK0 and AssignC10BLK1 as binaries. However, the output assigns a non-binary to these variables. Is there something that I am doing incorrectly? Or is this natural behavior from SCIP?<br>
<br>
Regards.<br>
<br></span><div><div class="h5">
On Wed, Feb 14, 2018 at 1:55 AM, Benjamin Müller <<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.de</a> <mailto:<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.d<wbr>e</a>>> wrote:<br>
<br>
Hi Vincent,<br>
<br>
you did two mistakes:<br>
<br>
1. <y#0> should have a coefficient of 1 in the objective.<br>
<br>
2. You declared your quadratic constraint to be linear and there<br>
were also some semantic problems.<br>
<br>
Your model should actually look like:<br>
<br>
STATISTICS<br>
Problem name : test.cip<br>
Variables : 5 (0 binary, 5 integer, 0 implicit integer, 0<br>
continuous)<br>
Constraints : 0 initial, 5 maximal<br>
OBJECTIVE<br>
Sense : minimize<br>
VARIABLES<br>
[integer] <c#0b#0>: obj=0, original bounds=[0,1]<br>
[integer] <c#1b#0>: obj=0, original bounds=[0,1]<br>
[integer] <c#0b#1>: obj=0, original bounds=[0,1]<br>
[integer] <c#1b#1>: obj=0, original bounds=[0,1]<br>
[integer] <y#0>: obj=1, global bounds=[0,4], local bounds=[0,4]<br>
CONSTRAINTS<br>
[linear] <pack_1>: <c#0b#0>[I] +<c#0b#1>[I] == 1;<br>
[linear] <pack_2>: <c#1b#0>[I] +<c#1b#1>[I] == 1;<br>
[linear] <cap_0>: <c#0b#0>[I] +<c#1b#0>[I] <= 1;<br>
[linear] <cap_1>: <c#0b#1>[I] +<c#1b#1>[I] <= 1;<br>
[quadratic] <obj_0>: -<c#0b#1>[C] <c#1b#1>[C] -<c#0b#0>[I]<br>
<c#1b#0>[I] -<y#0>[C] == -4;<br>
END<br>
<br>
Please read your instance with SCIP display the problem in order to<br>
see how SCIP parsed it.<br>
<br>
./bin/scip -c "read test.cip display problem"<br>
<br>
Anyway, I think that you might want to use a modelling language<br>
instead of dealing with our .cip format. You could, e.g., use ZIMPL,<br>
AMPL, GAMS, ...<br>
<br>
Best,<br>
Benny<br>
<br>
PS: Please don't include the Ipopt mailing any more. Your problems<br>
are not related to Ipopt.<br>
<br>
<br>
<br>
On 02/14/2018 07:01 AM, Vincent Mirian wrote:<br>
<br>
Hi all.<br>
<br>
Thanks for the reply Benny.<br>
<br>
I created the following input file to the framework.<br>
STATISTICS<br>
Problem name : test.cip<br>
Variables : 5 (0 binary, 5 integer, 0 implicit<br>
integer, 0 continuous)<br>
Constraints : 0 initial, 5 maximal<br>
OBJECTIVE<br>
Sense : minimize<br>
VARIABLES<br>
[integer] <c#0b#0>: obj=0, original bounds=[0,1]<br>
[integer] <c#1b#0>: obj=0, original bounds=[0,1]<br>
[integer] <c#0b#1>: obj=0, original bounds=[0,1]<br>
[integer] <c#1b#1>: obj=0, original bounds=[0,1]<br>
[integer] <y#0>: obj=0, global bounds=[0,4], local bounds=[0,4]<br>
CONSTRAINTS<br>
[linear] <pack_1>: <c#0b#0>[I] +<c#0b#1>[I] == 1;<br>
[linear] <pack_2>: <c#1b#0>[I] +<c#1b#1>[I] == 1;<br>
[linear] <cap_0>: <c#0b#0>[I] +<c#1b#0>[I] <= 1;<br>
[linear] <cap_1>: <c#0b#1>[I] +<c#1b#1>[I] <= 1;<br>
[linear] <obj_0>: -<c#0b#1>[I]*<c#1b#1>[I]*1<br>
-<c#0b#0>[I]*<c#1b#0>[I]*1 +4 -<y#0> == 0;<br>
END<br>
<br>
The results should assign y#0 to 4. However, I get the following<br>
output (snippet of output to reduce length):<br>
<br>
presolving (1 rounds: 1 fast, 1 medium, 1 exhaustive):<br>
5 deleted vars, 5 deleted constraints, 0 added constraints, 1<br>
tightened bounds, 0 added holes, 0 changed sides, 0 changed<br>
coefficients<br>
0 implications, 0 cliques<br>
presolved problem has 0 variables (0 bin, 0 int, 0 impl, 0 cont)<br>
and 0 constraints<br>
transformed objective value is always integral (scale: 1)<br>
Presolving Time: 0.00<br>
<br>
objective value: 0<br>
c#0b#0 1 (obj:0)<br>
c#1b#1 1 (obj:0)<br>
<br>
The assignment of c#0b#0 and c#1b#1 are valid solutions. I am<br>
confused on the reason that the objective value is 0. I am<br>
assume that the objective value is y#0, since it is the only<br>
variable not stated. The value of y#0 should be 4.<br>
<br>
Some guidance would be appreciated. Note that I searched the<br>
SCIP mailing list archive but I did not find any references for<br>
the input format and description of the output.<br>
<br>
Any help would be appreciated. Thank you.<br>
<br>
<br>
<br>
On Tue, Feb 13, 2018 at 11:38 PM, Benjamin Müller<br>
<<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.de</a> <mailto:<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.d<wbr>e</a>><br></div></div>
<mailto:<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.d<wbr>e</a><div><div class="h5"><br>
<mailto:<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.d<wbr>e</a>>>> wrote:<br>
<br>
Hi Vincent,<br>
<br>
I would recommend using JSCIPOpt, which is our SCIP-Java<br>
interface<br>
that can be found here:<br>
<br>
<a href="https://github.com/SCIP-Interfaces/JSCIPOpt" rel="noreferrer" target="_blank">https://github.com/SCIP-Interf<wbr>aces/JSCIPOpt</a><br>
<<a href="https://github.com/SCIP-Interfaces/JSCIPOpt" rel="noreferrer" target="_blank">https://github.com/SCIP-Inter<wbr>faces/JSCIPOpt</a>><br>
<<a href="https://github.com/SCIP-Interfaces/JSCIPOpt" rel="noreferrer" target="_blank">https://github.com/SCIP-Inter<wbr>faces/JSCIPOpt</a><br>
<<a href="https://github.com/SCIP-Interfaces/JSCIPOpt" rel="noreferrer" target="_blank">https://github.com/SCIP-Inter<wbr>faces/JSCIPOpt</a>>><br>
<br>
All relevant steps for building the interface are listed in the<br>
INSTALL.md, but basically, you will need to download the<br>
latest SCIP<br>
Optimization Suite and compile it with Ipopt. You might<br>
want to have<br>
a look at the JSCIPOpt/examples/Quadratic, which shows how<br>
to model<br>
a problem with simple quadratic constraints.<br>
<br>
Please note that SCIP can't handle a nonlinear objective<br>
function.<br>
For this reason, you need to add an auxiliary variable z<br>
and then<br>
model your problem as<br>
<br>
min z<br>
z >= sum_{ij} X_{ij} Y_{ij}<br>
...<br>
<br>
Best,<br>
Benny<br>
<br>
On 02/13/2018 07:05 PM, Vincent Mirian wrote:<br>
<br>
HI all.<br>
<br>
I am new to the SCIP community.<br>
<br>
I am looking for a non-linear solver to solve:<br>
- an objective function similar to 0 <= sum over i and<br>
j of X_ij<br>
* Y_ij * Constant < max<br>
- with constraints similar tosum(X_ij) = 1 and<br>
sum(Y_ij) = 1<br>
(forcing the values of X_ij and Y_ij to zero or one). A<br>
solution<br>
would be sufficient (it can be local or global optimal).<br>
<br>
I would need to interface the solver with Java. I found<br>
Google<br>
Optimization Tools (OR-Tools) to interface SCIP with<br>
java. Note<br>
that this work is for academic research.<br>
<br>
- Would SCIP or Ipopt be suitable for my task? Would<br>
anyone have<br>
experience integrating SCIP or Ipopt into Java?<br>
- I'm using a machine with ubuntu 64-bit. Are there any<br>
issues<br>
with installing these tools in this machine environment?<br>
- What is the manner to describe the constraints and<br>
objective<br>
function using these tools?<br>
<br>
Thank you.<br>
-- Vincent Mirian<br>
<br>
<br>
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<br>
<br>
-- _____________________________<wbr>_<br>
Benjamin Müller<br>
Zuse Institute Berlin<br>
Takustr. 7, 14195 Berlin<br>
<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.de</a> <mailto:<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.d<wbr>e</a>><br></span>
<mailto:<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.d<wbr>e</a> <mailto:<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.d<wbr>e</a>>><span class=""><br>
<a href="tel:%2B49%2030%20841%2085-195" value="+493084185195" target="_blank">+49 30 841 85-195</a> <tel:%2B49%2030%20841%2085-195<wbr>><br>
<tel:%2B49%2030%20841%2085-195<wbr>><br>
<br>
<br>
<br>
<br>
-- Vincent Mirian<br>
<br>
<br>
-- _____________________________<wbr>_<br>
Benjamin Müller<br>
Zuse Institute Berlin<br>
Takustr. 7, 14195 Berlin<br>
<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.de</a> <mailto:<a href="mailto:benjamin.mueller@zib.de" target="_blank">benjamin.mueller@zib.d<wbr>e</a>><br>
<a href="tel:%2B49%2030%20841%2085-195" value="+493084185195" target="_blank">+49 30 841 85-195</a> <tel:%2B49%2030%20841%2085-195<wbr>><br>
<br>
<br>
<br>
<br>
-- <br>
Vincent Mirian<br>
<br>
<br></span><span class="">
______________________________<wbr>_________________<br>
Scip mailing list<br>
<a href="mailto:Scip@zib.de" target="_blank">Scip@zib.de</a><br>
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<br>
</blockquote>
<br>
-- <br>
Ambros Gleixner, Research Group Mathematical Optimization Methods at Zuse Institute Berlin, <a href="http://www.zib.de/gleixner" rel="noreferrer" target="_blank">http://www.zib.de/gleixner</a><span class=""><br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div>Vincent Mirian</div></div></div></div>
</div>