I have a problem in Worhp with a constant objective. For this particular problem, it is only relevant to minimise the constraint violations.
Worhp indeed finishes the optimisation with the message "Optimal Solution Found (objective appears to be constant)”.
My question is: is there a way to tell Worhp up front that the objective function is constant, so it can be more efficient (faster) in finding the solution? Are there parameters in the worhp.xml that I can adjust to solve the problem faster?
I just found an answer to my own question. The FeasibleOnly parameter instructs Worhp to stop the optimisation once a feasible point is found.
A classical case of RTFM.... sorry!
Related
I am using Gurobi to run a MIQP (Mixed Integer Quadratic Programming) with linear constraints in Matlab. The solver is very slow and I would like your help to understand whether I can do something about it.
These are the lines which I use to launch the problem
clear model;
clear params;
model.A=[Aineq; Aeq];
model.rhs=[bineq; beq];
model.sense=[repmat('<', size(Aineq,1),1); repmat('=', size(Aeq,1),1)];
model.Q=Q;
model.obj=c;
model.vtype=type;
model.lb=total_lb;
model.ub=total_ub;
params.MIPGap=10^(-1);
result=gurobi(model,params);
This is a screenshot of the output in the Matlab window.
Question 1: It is the first time I am trying to run a MIQP and I would like to have your advice to understand what I can do to improve performance. Let me tell what I have tried so far:
I cheated by imposing params.MIPGap=10^(-1). In this way the phase of node exploration is made shorter. What are the cons of doing this?
I have big-M coefficients and I have tied them to the smallest possible values.
I have tried setting params.ScaleFlag=2; params.ObjScale=2 but it makes things slower
I have changed params.method but it does not seem to help (unless you have some specific recommendation)
I have increase params.Threads but it does not seem to help
Question 2 (minor): Why do I get a negative objective in the root simplex log? How can the objective function be negative?
Without having the full model here, there is not much on advise to give. Tight Big-M formulations are important, but you said, you checked them already. Sometimes splitting them up might help, but this is a complex field.
What might give great benefits for some problems is using the Gurobi parameter tuning tool. So try to export your model and feed the tuning tool with it. It automatically tries different of the hundreds of tuning parameters and might give some nice results.
Regarding the question about negative objectives in the simplex logs, I can think of a couple of possible explanations. First, note that the negative objective values occur in the presence of dual infeasibilities in the dual simplex run. In such a case, I'm not sure exactly what the primal objective values correspond to. Second, if you have a MIQP with products of binaries in the objective, Gurobi may convexify the objective in a way that makes it possible for a negative objective to appear in the reformulated model even when the original model must have a nonnegative objective in any feasible solution.
I am using MATLAB gamultiobj optimization
as I have 6 to 12 objective functions; the gamultiobj function inefficiently handling the problem, always terminated because the number of generations exceeded, not because the changes of the objective functions become smaller
I looked at the gamultiobj options documentations, but it didn't help
http://www.mathworks.com/help/gads/examples/multiobjective-genetic-algorithm-options.html
1- how can I increase the capability of gamultiobj function to handle this number of objective functions?
2- are there a better way at all (using MATLAB)?
Well,
this is my update:
1- I increased the number of generations, the population size, and assigned proper initial population using the common ga options, it worked better (I didn't know that they are working with gamultiobj too, but I knew, it isn't stated anywhere in the documentation explicitly).
2- after running and inspecting the results I realized that gamultiobj can handle many objective functions efficiently providing that they are independent. As long as the objective functions are strongly dependent (which is the case of my problem, unfortunately) the gamultiobj solver's efficiency dramatically decreases.
thanks !
You should increase the number of generations, possibly play with the options such as crossover, mutation, the constraint bounds in which you're going to get the solution.
The bounds are to specified correctly. and the initial population is also pretty much needed to get it to the correct set of parameters that you want to optimize
I'm writing code, that executes MLE. At each step, I get gradient at one point and then move along it to another point. But I have problem with determination of magnitude of the move. How to determine the best magnitude for good convergence? Can you give me an advice how to avoid other pitfalls, such as presence of several maximums?
Regarding the presence of several maxima: this issue will occur when dealing with a function that is not convex. It can be partially solved by multi-start optimization, which essentially means that you run the simulation multiple times in order to find as many maxima as possible and then selecting the 'highest' maximum from among them. Note that this does not guarantee global optimality, as the global optimum might be hard to reach (i.e. the local optima have a larger domain of attraction).
Regarding the optimal step size for convergence: you might want to look at back-tracking linesearch. A short explanation of it can be found in the answer to this question
We might be able to give you more specific help if you could give us some code to look at, as jkalden already pointed out.
I have a problem with ode45. I've defined a function and trying to solve it by ode, but when i run it, it takes so long. I tried to display the "t" input in my function and it showed time step was 10^-8 ! [I do not get any error from ode45]
So i put a breakpoint at the end of my function, and after I Step once, it goes to sym.m file and calls Function delet(h)
function dxr=Dynfun(t,x)
...
dxr=[A;B]
after Step it goes to
function delete(h)
if builtin('numel',h)==1 && inmem('-isloaded','mupadmex') && builtin('numel',h.s)==1 && ~isa(h.s,'maplesym')
mupadmex(h.s,1);
end
end
and that's what makes it too long, because it goes in a loop in there.
what's the problem?! Thanks
Sounds like it's a "stiff" problem to me. I would recommend using a solver that is designed for stiff problems. I would also recommend trying a fixed step solver at a small step size ~ 0.001 and see what the output looks like. If you are breaking in sym.m, sounds like you've some some symbolic logic going on in there. Is there a way you could take your symbolic expression and convert it to a matlab script?
As indicated by macduff, your problem could be stiff. Try ode15s (which is designed for stiff problems) and see if the stepsize still decreases to unacceptably low values.
If that is indeed the case, then your problem might contain a singularity for the initial values you give it. If your problem has dimensions lower than 3, you can define a small event function to get insight into the values at each step, and plot them to see if there is indeed something problematic going on.
Then -- do you really need symbolic math? The philosophy behind that is that it's easier to read for humans, which makes it terrible to deal with for computers :) If you can transform it into something non-symbolic, please do -- this will noticeably increase performance.
Also, more a word of advice, delete is also Matlab builtin function. It is generally a bad idea to name your functions after Matlab buitins -- it's confusing, and can cause a lot of overhead while Matlab is deciding which one to use.
I used Matlab-fminsearch for a negativ max likelihood model for a binomial distributed function. I don't get any error notice, but the parameter which I want to estimate, take always the start value. Apparently, there is a mistake. I know that I ask a totally general question. But is it possible that anybody had the same mistake and know how to deal with it?
Thanks a lot,
#woodchips, thank you a lot. Step by step, I've tried to do what you advised me. First of all, I actually maximized (-log(likelihood)) and this is not the problem. I think I found out the problem but I still have some questions, if I don't bother you. I have a model(param) to maximize in paramstart=p1. This model is built for (-log(likelihood(F))) and my F is a vectorized function like F(t,Z,X,T,param,m2,m3,k,l). I have a data like (tdata,kdata,ldata),X,T are grids and Z is a function on this grid and (m1,m2,m3) are given parameters.When I want to see the value of F(tdata,Z,X,T,m1,m2,m3,kdata,ldata), I get a good output. But I think fminsearch accept that F(tdata,Z,X,T,p,m2,m3,kdata,ldata) like a constant and thatswhy I always have as estimated parameter the start value. I will be happy, if you have any advise to tweak that.
You have some options you can try to tweak. I'd start with algorithm.
When the function value practically doesn't change around your startpoint it's also problematic. Maybe switching to log-likelyhood helps.
I always use fminunc or fmincon. They allow also providing the Hessian (typically better than "estimated") or 'typical values' so the algorithm doesn't spend time in unfeasible regions.
It is virtually always true that you should NEVER maximize a likelihood function, but ALWAYS maximize the log of that function. Floating point issues will almost always corrupt the problem otherwise. That your optimization starts and stops at the same point is a good indicator this is the problem.
You may well need to dig a little deeper than the above, but even so, this next test is the test I recommend that all users of optimization tools do for every one of their problems, BEFORE they throw a function into an optimizer. Evaluate your objective for several points in the vicinity. Does it yield significantly different values? If not, then look to see why not. Are you creating a non-smooth objective to optimize, or a zero objective? I.e., zero to within the supplied tolerances?
If it does yield different values but still not converge, then make sure you know how to call the optimizer correctly. Yeah, right, like nobody has ever made this mistake before. This is actually a very common cause of failure of optimizers.
If it does yield good values that vary, and you ARE calling the optimizer correctly, then think if there are regions into which the optimizer is trying to diverge that yield garbage results. Is the objective generating complex or imaginary results?