I have a MILP problem. I use CBC solver to solve it. If I call CBC through GAMS, the computation time will be short. But when I solve it directly using CBC.exe, it takes too much time. In both cases, I use the default value of the parameters. Is it GAMS that optimizes the parameters of CBC? Thank you to tell me the reason.
GAMS does not optimize the solver parameters explicitly. Though, in some cases it might set its own defaults. Also, is it possible, that your two formulations of the model are a little different?
Related
Can I find ode solvers (ode23, ode45, and ode113) in Scilab?
I use these solvers in MATLAB, but I have no idea if there is the same option in Scilab or not.
Thanks in advance.
Did you try the search function? The answer in Convert ode45() to scilab should give an idea, even if RKF is not DoPri5.
Read the documentation on the other available steppers.
The default stepper without type parameter uses lsoda, which can be seen as comparable to ode113
With "stiff" you get lsode, which is approximately equivalent to ode15s.
"adams" could substitute for ode23, there are no explicit low-order methods available, so adaptive-step and order Adams-Bashford is the best you get for a fast integration. And, as mentioned,
"rkf" is an embedded explicit 4(5) method that can substitute for the embedded explicit (4)5 Dormand-Prince method of ode45.
There exist more modern solvers and step-size heuristics, using dense output, an advanced event "root->action" mechanism etc. Scilab is not alone in having a stalled development in this regard. The default is good enough for small projects and prototyping, for massive number-crunching use a compiled language.
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?
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 am looking for a complete CSP solver, meaning it will always find a solution if it exists and will tell you if no solution exists. A solver that is optimized for graph coloring is preferred, but not necessary. There are many iterative algorithms/solvers out there but I require a completeness(?) for my work.
I have implemented my own solver using the Weak-Commitment Search algorithm but I'm sure there are many optimizations and thread-based features that could make for a much faster solver and allow me to increase the number of variables I can use in my simulation. I realize it's an exponentially difficult problem, but every little bit will help!
Someone has implemented the branch-and-price method of Mehrotra and Trick. I haven't used this code, but I believe that the approach is the state of the art in exact coloring.
I am doing some numerical analysis work in VB6, and the question arises of which of
sqr(x)
or
x^0.5
I should use.
Is there any difference in the method used to evaluate these two expressions, and if so, which of them should I prefer?
VB6 does not document the method is uses to evaluate sqr() or x^0.5. Empirically, sqr() is much faster, which could mean that they are using a dedicated root finding algorithm here. The use of a specialized algorithm could mean that sqr() also has better numerical stability, but I have no information regarding this.