I was wondering if anybody knows if it is possible to set multiple feasible initial solutions in Gurobi MILP solver.
I already know how to set a single initial solution, as indicated here.
From the APIs, you can supply multiple MIP starts using the NumStart attribute and StartNumber parameter. E.g.:
model.NumStart = 2
# iterate over all MIP starts
for s in range(model.NumStart):
# set StartNumber
model.params.StartNumber = s
# now set MIP start values using the Start attribute, e.g.:
for v in model.getVars():
v.Start = <value>
When using the command-line tool gurobi_cl you can use InputFile multiple times, e.g.
gurobi_cl InputFile=mipstart1.mst InputFile=mipstart2.mst model.mps
With both approaches, Gurobi will try all supplied MIP starts and use the best one.
Related
I am attempting to solve a non-convex quadratic optimization problem using Gurobi, but I have encountered an issue. Specifically, I have a specific objective function; however, I am only interested in finding a feasible solution. To do this, I tried two ways:
1- set my specific objective function as the model objective and set the parameter "SolutionLimit" to 1. This works fine, and Gurobi gives me a feasible solution.
2- give Gurobi no objective function (or set the objective to some arbitrary number like 0). In this case, Gurobi returns no feasible solution. The log it prints says:
Optimal solution found (tolerance 1.00e-04)
Warning: max constraint violation (1.5757e+01) exceeds tolerance
(model may be infeasible or unbounded - try turning presolve off)
Best objective -0.000000000000e+00, best bound -0.000000000000e+00, gap 0.0000%
I checked the solution it returned, and it is infeasible. I want the second method to work too. I have attempted to modify the solver parameters (such as "m.ModelSense = GRB.MAXIMIZE," "m.params.MIPFocus = 3," "m.params.NoRelHeurTime = 200," "m.params.DualReductions = 0," "m.params.Presolve = 2," and "m.params.Crossover = 0") in an effort to resolve this issue but have been unsuccessful. Are there any other parameters that I can adjust in order to successfully solve this problem?
This model has numerical issues; to understand more, please see Guidelines for Numerical Issues in the Gurobi Reference Manual.
I need some help in creating a VI that generates virtual or calculated channels based on several channels I measure.
e.g.
I measure voltage on several AI, lets say, ch A,B,C,D,E were B,C and E represent current on a shunt and would like to calculate a the power of the system
Q[A] = B+C
R[W] = A*Q
S[W] = D*E
T[W] = R+S
I would like to load the equations externally from a configuration file that may vary from one project to another equations would come in a format of a string Q=A+B , R= A*Q .....
*(during a run equation and channel count don't change - only when loading config).
The main issues that I am facing is that the inputs to each equation may have dependencies on virtual channels that do not have data yet
Was trying to use:
formula nodes/ Math scripts: https://zone.ni.com/reference/en-XX/help/371361R-01/lvconcepts/formula_nodes/
https://knowledge.ni.com/KnowledgeArticleDetails?id=kA03q000000x30HCAQ&l=en-IL
All data that should be chunked into a data stream (continues sampling) that can be presented on a Chart/Graph and saved to CSV/TDMS
do I need some additional packages?
I have tried the following based on the the example given - getting strange result
Answer
The elements you are looking for are not the Formula/Math Nodes but rather the:
Formula Parsing VIs
Using these VIs you are able to pass a calculation in the form of a string and an array of variable names and then evaluate the formula. This allows for run-time variable scripting, where most other nodes require compile time formula evaluation (With the exception of the python node).
Example
Example of using a very simple program to evaluate two different calculations using the same values and variables.
I am trying to understand how to set the gap for Gurobi.Optimizer, because it solves too long when the default gap threshold is used (10e-4).
I couldn't find it anywhere (they might be referred to as attributes or parameters).
PS: Also, I am trying to find the right tutorial or instructions on how to use the solver in JuMP. I checked here https://juliahub.com/docs/Gurobi/do9v6/0.7.7, but they don't reveal the meanings of different attributes and inputs. Please, send me one in case somebody knows.
Best,
A.
You can set the MIP gap via the MIPGap parameter:
using JuMP, Gurobi
model = Model(Gurobi.Optimizer)
set_optimizer_attribute(model, "MIPGap", 0.1)
You can read more about JuMP here: https://github.com/jump-dev/Gurobi.jl
I am using Gurobi for solving an optimization problem. In my problem, the goal is to analyze the most possible solutions. For this purpose, I am using the parameter:
PoolSearchMode=2
in Gurobi to find multiple solutions. But, when I retrieve the solutions, there are some same results!. For example, if it returns 100 solutions, half of them are the same and actually I have 50 different solutions.
For more detail, I am trying to find some sets of nodes in a graph that have a special feature. So I have set the parameter "PoolSearchMode" to 2 that causes the MIP to do a systematic search for the n best solutions. I have defined a parameter "best" to find solutions that have "objVal" equal to the best one. In the blow there is a part of my code:
m.Params.PoolSearchMode = 2
m.Params.PoolSolutions = 100
b = m.addVars(Edges, vtype=GRB.BINARY, name = "b")
.
.
.
if m.status == GRB.Status.OPTIMAL:
best = 0
for key in range(m.SolCount):
m.setParam(GRB.Param.SolutionNumber, key)
if m.objVal == m.PoolObjVal:
best+=1
optimal_sets = [[] for i in range(best)]
for key in range(best):
m.setParam(GRB.Param.SolutionNumber, key)
for e in (Edges):
if b[e].Xn>0 and b[e].varname[2:]=="{}".format(External_node):
optimal_sets[key].append(int(b[e].varname[0:2]))
return optimal_sets
I have checked and I found that, if there are not 100 solutions in a graph, it returns fewer solutions. But in these sets also there are same results like:
[1,2,3],
[1,2,3],
[1,3,5]
How can I fix this issue to get different solutions?
It seems that Gurobi returns multiple solutions to the MIP that can be mapped to the same solution of your underlying problem. I'm sure that if you checked the entire solution vector all these solutions would indeed be different. You are only looking at a subset of the variables in your set optimal_sets.
I have written a finite volume model. The parameter n represents the number of volumes. After translating, the parameter can't be modified. Dymola gives this message:
Warning: Setting n has no effect in model.
After translation you can only set literal start-values and non-evaluated parameters.
I think the problem is that the parameter n is used in the equation section. There I use the following code:
equation
...
for i in 2:n-1 loop
T[i] = some equation
end for
I also use n for the calculation of the initial values of T.
The purpose is to make a script that repeatedly executes the model but with a different n.
How can I do this?
The issue here is that your parameter n affects the number of variables in the problem. Dymola (and all other Modelica compilers I know of) evaluate such parameters at compile time. In other words, they hard code the value at compile time into the model.
One potential workaround in your case is to perform the translation or simulation inside your loop. Note that in the translate and simulate commands in Dymola you can include modifications. Just add them after the model name. For example MyModel would become MyModel(n=10).