A practical and straightforward math tool that comes in handy for users who need to solve mixed integer nonlinear programming (MINLP) problems.
- MIDACO-SOLVER
- Version :5.0
- License :Demo
- OS :Windows All
- Publisher :Schlueter Martin
MIDACO-SOLVER for Windows 32 bit Download Now(For Python)
MIDACO-SOLVER for Windows 64 bit Download Now(For Python)
MIDACO-SOLVER for Windows 32 bit Download Now(For Java)
MIDACO-SOLVER for Windows 64 bit Download Now(For R)
MIDACO-SOLVER Description
MIDACO-SOLVER is a comprehensive and effective utility that provides you with a simple means of solving mathematical optimization problems effortlessly.
Although the utility was initially developed for Mixed Integer Nonlinear Programming (MINLP) problems, MIDACO-SOLVER is able to handle tasks where the objective function (which is known as f(x) ) depends on continuous variables (known as x).
Irrespective of which programming language you are using, such as Matlab, Octave, C/C++, Fortran, Python, Java, R or Excel/VB, MIDACO-SOLVER is geared towards users who need to solve math optimization problems.
After compiling the proper files, the tool displays a file that allows you to define problem dimensions, change the starting point and the stopping criteria, choose the parameters you are interested in and modify printing options.
As an heuristic algorithm, MIDACO-SOLVER can not provide an absolute guarantee for reaching the optimal solution. However, it is able to implement specific functions that allow the algorithm to escape from local optima.
Using this utility you are able to quickly solve multi-objective optimization problems as well. For instance, let’s just consider a problem that contains three different variables where F1(X) needs to be lower than 100, F2(X) should not exceed 50 and F3(X) must not surpass 70. With the help of MIDACO-SOLVER you can add a new variable and define the inequality constraints such as G(1) = 100 – F1(X) and so on.
To wrap it up, MIDACO-SOLVER has the capability of solving problems with a significant number of equality constraints and it can be applied to search solutions for problems with critical function properties like high non-convexity.