Writing my first non-trivial MiniZinc app, I keep running into the error "cannot determine bounds". How does one generally resolve this error?
In general “cannot determine bounds” means that the solver cannot determine the bounds (the domain) of a decision variable.
Using "var int" as the domain of a decision variable should be avoided if possible, since it will probably slow down the solving process. There are times where the solver can figure out the domain, e.g. in cases likes
% ...
var int: z = sum(x);
when "x" have declared domains. But, as a rule, try to define the domains.
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 have a SCIP project to solve a binary problem with nonlinear objective function that works well but for some instances I got a message saying "the best solution is not feasible", and there is some violation in the constraints. (The violation is mostly very small)
To solve this issue, I added the SCIP_CALL_EXC( SCIPsetRealParam(scip, "numerics/feastol", 1e-5) to change the default value of feastol. But I get segmentation fault!
Following your helpful suggestion, the violation value is now much lower. My objective function is in the form of Min: AX+ LSqrt(BX). In the previous version, I had used an auxiliary variable, let say, Q such that Q^2 - L^2(BX) >=0 and the objective function was expressed as Min: AX+ Q . In the new version, I changed the inequality sign into equality and in combination with SCIPsetRealParam(scip, "numerics/feastol",1e-8), the violation in the constraints are much lower. What can I do more to decrease the violation value? Moreover, when I printed the value of feastol, I seenumerics/lpfeastol=1e-8 but lpfeastol is different from feastol!. So, why lpfeastol is modified instead?. I see the modified value of lpfeastol several times printed on screen when SCIP is solving the problem. I appreciate your help in advance
Maybe try changing the define of UPGSCALE in src/scip/cons_soc.c to some larger value.
The output for the lpfeastol is unfortunate, but normal. Reducing feastol automatically leads to adjusting lpfeastol, too. I cannot reproduce "I printed the value of feastol, I seenumerics/lpfeastol=1e-8 but lpfeastol is different from feastol".
I am trying to find out the parameters for the function below:
$$
\log L(\alpha,\beta,v) = v/\beta(e^{-\beta T} -1) + \alpha/\beta \sum_{i=1}^{n}(e^{-\beta(T-t_i)} -1) + \sum_{i=1}^{N}log(v e^{-\beta t_i} + \alpha \sum_{j=1}^{jmax(t_i)} e^{-\beta(t_i - t_j)}).
$$
However, the conventional methods like fmin, fminsearch are not converging properly. Any suggestions on any other methods or open libraries which I can use?
I was trying CVXPY, but they don't support the division by a variable in the expression.
The problem may not be convex (I have not verified this but it could be why CVXPY refused it). We don't have the data so we cannot try things out, but I can give some general advice:
Provide exact gradients (and 2nd derivatives if needed) or use a modeling system with automatic differentiation. Especially first derivatives should be preferably quite precise. With finite differences you may lose half the precision.
Provide a good starting point. May be using an alternative estimation method.
Some solvers can use bounds on the variables to restrict the feasible region where functions will be evaluated. This can be used to restrict the search to interesting areas only and also to protect operations like division and log functions.
I am trying to use the G_LLL_XD function on the NTL library. Whenever I use the function in this format:
G_LLL_XD(B, delta); ,
the program works.
Though, when I want to change the default deep or prune variables and write the function in one of these ways:
G_LLL_XD(B, delta, deep, check, verbose);
G_LLL_XD(B, delta, prune, check, verbose);
during runtime, I get this error:
R610
- abort() has been called
and in the command prompt it says:
"sorry...deep insertions not implemented"
I find this very weird since whenever I use prune as a variable, I get this crash error, which I shouldn't because the function shouldn't be looking for deep insertion but prune, and when I do use deep as a variable and have implemented deep, I still get an error.
Can anybody help me understand what the problem is or how I can fix this? Thank you very much.
I dont found a argument prune for LLL function in NTL. But there is one for BKZ. Since the are both accept positive intergers, its only a naming confusion.
From the documentation:
NOTE: use of "deep" is obsolete, and has been "deprecated". It is
recommended to use BKZ_FP to achieve higher-quality reductions.
Moreover, the Givens versions do not support "deep", and setting
deep != 0 will raise an error in this case.
So you can not use G_LLL_XD with deep != 0 but LLL_XD should work (but it is deprecated).
But as mentioned, you should consider using BKZ_XD instead of LLL_XD.
A BKZ basis of a lattice is also LLL reduced, so there should be no problem. BKZ is slower than LLL but you can choose a small Blocksize, maybe 10 or 20 but also 2 or 4 will work, to speed the reduction up.
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).