I began using PySCIPOpt/SCIP for a Coursera course on discrete optimization. I'd need to implement a simple separation from a fractional variable and wonder how to do it. Online SCIP literature does not provide relevant example.
Any Python example for me to get inspired for my assignment?
Thank you for the answer. Indeed I spent some hours reading SCIP documentation and I have trouble interfacing SCIp methods in Python.
I have been able to implement in Python a simple constraint handler to add first-type cuts and I'd like to add a separator to add second-type cuts.
The latter cuts are typically x = 0 or 1 cuts based on fractional x values and I stumble more with syntax - addCut() - and using generic methods than the process itself.
A Python example, a bit more involved than tsp.py, would greatly help me.
Your question is quite broad. I try to give some hints on where to look for an answer:
general information on separators in SCIP
difference between constraint handlers and separators
Python example on a separator implemented as constraint handler to solve the Traveling Salesman Problem
I can try to answer your question more precisely, if you explain your application/problem in more detail.
Related
I am using python's MIP module for optimization. I have set up a model with few parameters. I would want to limit the number of solutions and add stop timer if I don't find any solution in given time. I have added these parameters as given below:
m = Model(name='opt', sense=MAXIMIZE, solver_name=CBC)
m.optimize(max_solutions=1, max_seconds= 300)
somehow none of them seem working to me. it does not even stop looking for a solution after given time and it returns 2 solution sometimes even if I want to limit it to 1. Is there something I am missing in syntax?
One more thing, Gurobi has an option to add multiple variable using add_Vars parameter. Is there anything similar available in MIP too?
Thanks.
Yeah I have been doing some tests myself (with the Python-MIP solver) and seen some similar issues. Apparently it's still quite new and many improvements have been implemented recently or are yet to be developed. I will post from what I've learned:
regarding max_seconds: There's been at least one (closed) issue on the official repo related to using max_seconds parameter and CBC.
regarding max_solutions: If you are using version 1.6.2 or before, here's an explanation for that: until 1.6.1, the m.optimize(max_solutions=1) wasn't setting the maximum solution parameter to CBC. In that case you should try with the following lines (or just update to current version):
m.max_solutions = 1
m.optimize()
If the former don't help for the max_seconds and max_solutions parameters, I guess you'd better post your question as an issue at the library's repo to get answers and support from the project contributors.
Adding multiple variables, similar to gurobipy's Model.addVars() method: Yes, you can do it as follows
p = {(i, j): m.add_var(var_type=CONTINUOUS, lb=0, name="p[%d,%d]" % (i, j))
for i in Set_i for j in Set_j}
In this example, we are adding a variable p_ij and specifying it's continuous
(vs. binary or integer), has lower bound 0, and the sets where the indexes run. Set_i and Set_j are Python lists. See the documentation here for a more detailed explanation on how to use it. Similarly, you can create indexed constraints using the add_constr method, similar to Gurobi's Model.AddConstrs() method.
Is there a function in Julia that is similar to the solver function in Excel where I can provide and equation, and it will solve for the unknown variable? If not, does anybody know the math behind Excel's solver function?
I am not expecting anybody to solve the equation, but if it helps:
Price = (Earnings_1/(1+r)^1)+(Earnings_2/(1+r)^2)++(Earnings_3/(1+r)^3)+(Earnings_4/(1+r)^4)+(Earnings_5/(1+r)^5)+(((Earnings_5)(RiskFreeRate))/((1+r)^5)(1-RiskFreeRate))
The known variables are: Price, All Earnings, and RiskFreeRate. I am just trying to figure out how to solve for r.
Write this instead as an expression f(r) = 0 by subtracting Price over to the other side. Now it's a rootfinding problem. If you only have one variable you're solving for (looks to be the case), then Roots.jl is a good choice.
fzero(f, a::Real, b::Real)
will search for a solution between a and b for example, and the docs have more choices for algorithms when you don't know a range to start with and only give an initial condition for example.
In addition, KINSOL in Sundials.jl is good when you know you're starting close to a multidimensional root. For multidimensional and needing some robustness to the initial condition, I'd recommend using NLsolve.jl.
There's nothing out of the box no. Root finding is a science in itself.
Luckily for you, your function has an analytic first derivative with respect to r. That means that you can use Newton Raphson, which will be extremely stable for your function.
I'm sure you're aware your function behaves badly around r = -1.
While doing the CIS194 (Spring of 2013) homework 10, I got stuck with Applicative instance of a Parser type. I seek help from Google, an I came across with this Reddit post. The user ephrion gave an answer, which was also an example of typed hole exploration method, which I didn't quite understand. In the comments section of his answer he also said this:
It's extremely useful and one of the things that makes Haskell development so nice.
So question is, what this method is exactly, and are there some explicit order of steps in this method?
I still consider myself as a beginner when it comes to Haskell, and by googling about the subject I didn't find a very clear explanation how this kind of development style could be used.
Almost anywhere on the right hand side of an assignment in Haskell, you can write an underscore (optionally followed by other characters) instead of a value (constant or function). Instead of compiling, GHC will then tell you which type of value you might want to replace the underscore with, and list which identifiers in scope are of that type.
Matthías Páll Gissurarson is expanding the list of hints from GHC to include compound expressions.
CLP(FD) allows user to set the domain for every wannabe-integer variable, so it's able to solve equations.
So far so good.
However you can't do the same in CLP(R) or similar languages (where you can do only simple inferences). And it's not hard to understand why: the fractional part of a number may have an almost infinite region, putted down by an implementation limit. This mean the search space will be too large to make any practical use for a solver which deals with floats like with integers. So it's the user task to write generator in CLP(R) and set constraint guards where needed to get variables instantiated with numbers (if simple inference is not possible).
So my question here: is there any CLP(FD)-like language over reals? I think it could be implemented by use of number rounding, searching and following incremental approximation.
There are at least some major CLP(FD) solvers that support real (decision) variables:
Gecode
JaCoP
ECLiPSe CLP (ic library)
Choco (using Ibex)
(The first three also support var float in MiniZinc.)
The answser to your question is yes. There is Constraint-based Solvers dedicated for floating numbers. I do not have a list of solvers but I know that that ibex http://www.ibex-lib.org is a library allowing the use of floats. You should also have a look at SMT-Solvers implementing the Real-Theory (http://smtlib.cs.uiowa.edu/solvers.shtml).
Reading android code I find out is not uncommon to fill the xml with the second variant, like here.
To be honest, this alone would be enough for my head to torment me with the question, but then I see code in which the two notations are even mixed, like in this StackOverflow question.
Google wasn't of help, and I couldn't find this question on SO.
Anyone knows what's the difference, if any?
EDIT
Based on the firsts question I got here, there seems to be no improvement in performance or other things like that when using the second variant, so writing them as float when they can be integers just tells people they can insert decimal values in there. Anything else?
If that's the only reason, I think using the second option it's just like polluting the xml for little to no reason, are there other opinions?
Check out this link: http://developer.android.com/guide/topics/resources/more-resources.html#Dimension. It shows the possible values for dimensions. They are all floats. So, you can write them either way.
The 5.0 version is because certain properties accept a float/double value. All in all, 5.0 is the same as 5 when DP are involved.
one is an int the other is a float. They are types of primitives
It is the same. They both have the same values. 5 and 5.0 are of different types. 5 is of type int and 5.0 is of type float/double.