Please, what is a good Windows or android application that works as the solver in HP calculators? I'm not happy with PTC Mathcad and Mathlab, because they don't solve any variable without having to reformulate the equations
I would like an app to solve any variable in an equation with multiples variables.
Thank you very much;
Related
I want to just be sure that I am eligible to use Bonmin and Couenne for solving just the NLP problem (Still I do not have integer variable) and I am eager to obtain global optimum not local. I also read that Ipopt first search for the global answer and if it does not find that it will provide a local answer. How I can understand my answer is a global answer when I using Ipopt. Also, I want to what is the best NLP and MINLP open source pythonic solvers for these issues that can be merged with Pyomo?
The main reason for my question is the following output using Bonmin:
NOTE: You are using Ipopt by default with the MUMPS linear solver.
Other linear solvers might be more efficient (see Ipopt documentation).
Regards
Some notes:
(1) "Ipopt first search for the global answer and if it does not find that it will provide a local answer" This is probably not how I would phrase it. IPOPT finds local solutions. For some problems these will be the global solution. For convex problems, this is always the case (except for numerical issues).
(2) Bonmin is a local MINLP solver, Couenne is a global NLP/MINLP solver. Typically Bonmin can solve larger problems than Couenne, but you get local solutions.
(3) "NOTE: You are using Ipopt by default with the MUMPS linear solver. Other linear solvers might be more efficient (see Ipopt documentation)." This is just a notification that you are using IPOPT with linear algebra routines from MUMPS. There are other linear sub-solvers that IPOPT can use and that may perform better on large problems. Often the HARWELL routines (typically called MAnn) give better performance. MUMPS is free while the Harwell routines require a license.
In a follow-up answer (well it is not answer at all) it is stated:
Regarding Ipopt how I can understand that it is finding the global
solution or local optimum? the code will notify that? Regarding to
Bonmin according to AMPL page AMPL It provides the global solution for
the convex problem " Finds globally optimal solutions to convex
nonlinear problems in continuous and discrete variables, and may be
applied heuristically to nonconvex problems." And you were saying that
it is obtained the local solution, I am a bit confused on this part.
But the general question about all those codes is that how I can find
out that the answer is global optimum?
(a) Ipopt does not know if a solution is a local or a global optimal solution. For convex problems a local optimum is a global optimal solution. You will need to convince yourself the problem you pass on to Ipopt is convex (Ipopt will not do this for you).
(b) Bonmin: the same: if the problem is convex it will find global solutions. Otherwise you will get a local solution. You will get no notification whether a solution is a global solution: Bonmin does not know if a solution is a global optimum.
(c) When looking for guaranteed global solutions you can use a local solver only when the problem is convex. For other problems you need a global solver. Another approach is to use a multi-start algorithm with a local solver. That gives you confidence that you are not ending up with a bad local optimum.
If possible, I suggest to discuss this with your teacher. These concepts are important to understand (and most solver manuals assume you know about them).
In MiniZinc (windows IDE) How can I resolve:
flatzinc: error: variables of type `var float' are not supported by the FD solver backend.
I realize I need a different solver but could not find a procedure installing one and the Preferences dlg does not seem to work. Also, not clear which solver would work.
The G12/fd solver don't support float decision variables. Do you find G12 MIP solver in the "Solver" list? If so, try it and see if it can solve the model. There is a Help text ("Help, Help...") that might help you add the solver if not.
However, if it's an non-linear model then G12/mip might complain, and you have to try another solver. Gecode and JaCoP has some support for non-linear float models.
In MiniZinc 2.0 it might work with G12/mip and a non-linear model if the "-G linear" flag is used: mzn2fzn will then linearize non-linear constraints.
I am trying to solve some problems that can be mapped in convex optimisation problem.
In particular is for analysis of quantum state tomography data.
In Matlab there are some tools to help you do this, like SeDuMi or CVX
http://sedumi.ie.lehigh.edu
http://cvxr.com/cvx/
But I could not find anything similar in Mathematica, on the web or in the forums.
Does anybody know if there is an easy way of implementing this kind of algorithm in Mathematica?
I would like to avoid to be forced to switch to Matlab to solve this problem. Nothing against it, but I have most of the programming for this state tomography developed in Mathematica.
Thank you very much.
I had also some troubles with Mathematica in
optimization, exactly on convex problems.
I suggest you export to CVX, which will require
some work because it wants the problem in matrix notation.
Otherwise, to remain with the algebraic formulation,
you could try with Maple, which has, as far
as I can tell, better optimizers than Mathematica.
(check the doc to have an idea)
I am a university student and trying to write a program to solve 1-D Schrodinger's equation with some kinda of potential equations.
I am not a CS major so really have no clue to start.
I did research online but didn't find thing that is suitable for entry level :( The only thing I understand now is that I probably need to use some algorithm solving differential equations
Could anyone give me some suggestions or references on how I could start? For example, how to transform the physical problem into computer science program and what kinda algorithm I should look for?
I am interested in computational so trying on this HARD problem ;)
Thank you all!
go to www.google.com
search for :how can i solve Schrodinger in matlab
go to here
Return stackoverflow
I have a simple optimization problem and am looking for java software for that.
The Apache math optimization software looks just like what I want but I cant find documentation to suit my needs (where those needs are to useful to a beginner / non maths professional!)
Does anyone know of a worked, simple, example?
In case it helps, the problem is that I want to find the max r where
r1 = s1 * m1
r2 = s2 * m2
and there are some constraints and formula for defining the relationship between the variables. The Excel Solver works fine for this problem. I got LPSolve working great, but this problem requires a multiplication of s and m, so I understand LPSolve cant help as this makes the problem non linear.
I recently ported the derivative-free non-linear constrained optimization code COBYLA2 to Java. Since it does not explicitly rely on derivatives, the algorithm may require quite a few iterations for larger problems. Nonetheless, you are able to formulate your problem with both a non-linear objective function and (potentially) non-linear constraints.
You can read more about it and download the source code from here.
I am not aware of a simple Java-based NLP solver. (I did find an example of Quadratic programming (QP) in Apache Math Works, but it doesn't qualify since you asked for a non-math professional example.)
I have two suggestions for you to solve your non-linear program:
1.. Excel's Solver does have the ability to tackle non-linear problems. (Don't use LPSOLVE.) In fact, NLP is the default mode in Solver.
Here are two links to using Excel to solve NLPs: Example 1 - Step by step Solver walk-through that covers NLP and
Example 2 - A General Neural network example in Excel
Also for Excel, I like Paul Jensen's (utexas) ORMM Add-in's.
He has a module called Teach NLP. Chapter 10 of his book deals with NLP and is available from his site.
2.. If you are going to be doing even some amount of data analysis, then I recommend investing a few hours to download and learn the basics of R.
R has numerous packages and libraries for optimization. optim() and nlme are relavant for solving non-linear programs.
Just for completeness, I mention SAS, MATLAB and CPLEX as other options. If you have access to any of these, they all do a very good job with solving non-linear programs.
Hope these pointers help.