I have a simple linear programming problem. After solving it, I get the correct result. I want to speed it up using hot-start feature of MOSEK, but I don't know how to set some parameters like "res.sol.bas.sku", "res.sol.bas.skn", .... I only know an initial solution, i.e, "res.sol.bas.xx", where the value of the variables are stored for a near to optimal solution. Is it possible for me to accelerate the engine using Hot start feature in this way!
Regards
You seem to using MATLAB. Did you read
http://docs.mosek.com/6.0/toolbox/node009.html#238393032
Does it solve the issue?
Related
A snippet of my task is to generate, let's say, 256 quasi-random numbers using CUDA. I've read cuRAND docs and from there I've learnt that I need to use a set of direction vectors, which I can get using curandGetDirectionVectors32 function. But the problem is that I still can not understand what is 'set of direction vectors'. Especially how to use it, how to limit its length etc.
Also there's no example in Device API Examples with Sobol's generator. And there's no working example in google. I've found some explanation but scrambled_sobol_v_host is not declared in that scope and unclear for me.
So, my question is could anyone, please, provide me with tiny working example of usage of this generator?
And I have troubles with understanding difference between Sobol's generator and scrambled Sobol's generator.
Thank you in advance.
Direction vectors are the seeding method for that number generator. For implementation you should be able to follow using QuasirandomGenerator (for dummies)
I know it might be dumb to ask such a question because Dymola is short for Dynamic Model laboratory, and I should use modelica/dymola to solve dynamic problems.
However, I still wonder if it is possible to get some kind of quasi-static result from it by using the same model?
For example:
For a simple mass spring damper system, we have
F = mx_dotdot+cx_dot+kx
Of course, modelica is good at handling this kind of problem. What my question is, is there a way to tell the solver that the dynamic components(states) should all be zero without modifying the original model? Then at each time step, I could get a static solution.
F = kx
Thanks ahead for any answers!
I have found this somewhere over internet and I'm being curious how it's done that this lib is so smooth (even on my slow PC) and what's more important what algorithm had been used to create that 'sticky' effect. Maybe you know where can I learn about that kind of algorithms?
Thanks for help in advance :)
This visualisation uses a force layout. The algorithm is described in the documentation and you can of course have a look at the source code yourself. You can also have a look at the source of this particular visualisation to see what parameters etc were used, but a better way to get started would be to have a look at the documentation and examples on the D3 website.
I need to know the algorithm(s) it uses, because I have to write my own program. Levenberg-Marquardt doesn't really do the same. Is there like a list of algorithms, from which Mathematica chooses what algorithm to use for a specific problem?
Thank you.
Mathematica 8.x can use the following algorithms for NonLinearModelFit[] for its Method option:
Possible settings for Method include "ConjugateGradient", "Gradient", "LevenbergMarquardt", "Newton", "NMinimize", and "QuasiNewton", with the default being Automatic.
See the documentation for additional options etc.
Note that NonLinearFit[] is obsolete; you should now use FindFit[] instead.
I was searching google for something and saw the post that some one needed a piece of software in which he can take a sheet of material- and that he can maximize the cuts.
he needs to be able to enter the width and the length of the roll.
Then he would enter the sizes of cuts that he would need. After all the sizes are entered, the software would tell me how to cut the material so that he can achieve maximum efficiency.
Possible???
is there some AI involved?
This is referred to as a packing problem and is an important area of research in combinatorics, a field of mathematics. See http://en.wikipedia.org/wiki/Packing_problem
Yes it is possible and done in manufacturing. The simple way to think about it is you try any combination the computer can think of and take the one that is best (brute force). Basically it pretends to make a cut and then sees what is left, makes another pretend cut and sees what is left until the material is gone and then it tries them in a different location/order.
It is an optimization problem and there are many solutions out there.
http://en.wikipedia.org/wiki/Optimization_problem A wiki link that will lead you to many hours of reading.
This is entirely possible. It's just an optimization layout problem. I can't help you code it, but it's definitely possible and I suspect strongly that there are even efficient algorithms to do it.
I would imagine this problem has already solved a long time ago, when people did the calculations rather than software. I dont know the name of the problem, but i have seen that solving the optimal way to pack circlse in a box is a well known mathematical problem.
This is indeed 2D bin packing. As for software, take a look at Drools Planner (open source, java).