Is there any python implementation available of TSP using the IBM cplex tool, with Dantzig-Fulkerson-Johnson formulation.
If there is any can I get the link for it?
The exact same question was asked here. Here is my (slightly modified) answer from there:
The distribution does not have an example implementation in Python, but there is one in OPL: opl/examples/opl/models/TravelingSalesmanProblem/tsp.mod in your installation. This could serve as a good starting point when implementing things in Python.
In the example the problem is solved in a loop: after each solve, subtour elimination constraints are separated. In Python you would probably want to do this using a lazy constraint callback.
In general, subtour elimination constraints are usually not added directly to the model since there exponentially many of them.
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So I want to try to solve my optimization problem using particle swarm optimiztion algorithm. As I comoratable with python I was looking into PySwarms toolkit. The issue is I am not really experienced in this field and don't really know how to account for integrality constraints of my problem. I was looking for advice on what are some approches to dealing with integral variables in PSO. And maybe some examples with PySwarms or any good alternative packages?
You can try pymoo module, which is an excellent multi-objective optimization tool. It can also solve mixed variable problems. Despite pymoo is first of all designed to solve such problems using genetic algorithms, there is an implementation of PSO (single-objective with continuous variables). Maybe you'll find it useful to try to solve your mixed variable problem using genetic algorithm or one of its modifications (e.g. NSGAII).
I am trying to use GLPK for solving an LP problem. My problem is the routing problem in a computer network. Given network topology and each link capacity and the traffic demand matrix for each source-destination pair in the network, I want to minimize maximum link utilization in the network. This is an LP problem and I know how to use GLPK to get the optimum solution.
My problem is that I want to get the sub-optimal solutions also. Is there any way that I can get say top 10 suboptimal solutions by GLPK?
Best
For a pure LP (with only continuous variables), the concept of finding "next best" solutions is very difficult (just move an epsilon away, and you have another solution). We can define this differently: find "next best" corner points (a.k.a. bases). This is not so easy to do, but there is a somewhat complex way by encoding bases using binary variables (link).
If the problem is actually a MIP (with binary variables) it is easier to find "next best" solutions. Some advanced solvers have built-in facilities for this (called: solution pool). Note: glpk does not have this option. Alternatively, we can also do this by adding a cut that forbids the best-found solution and then resolve (link). In this case we exploited some structure. A general cut for 0-1 variables is derived here. This can also be done for general integer variables, but then things get a bit messy.
I currently solve optimization problems with complex variables using CVX + Mosek, on MATLAB. I'm now considering switching to Gurobi + Python for some applications.
Is there a way to declare complex values (both inside constraints and as optimization variables) directly into Gurobi's Python interface?
If not, which are good modeling languages, with Python interface, that automates the reduction of the problem to real variables before calling the solver?
I know, for instance, that YALMIP does this reduction (though no Python interface), and newer versions of CVXPY also (but I haven't used it extensively, and don't know if it already has good performance, is stable, and reasonably complete). Any thoughts on these issues and recommendations of other interfaces are thus welcome.
The only possible variables in Gurobi are:
Integer;
Binary;
Continuous;
Semi-Continuous and;
Semi-Integer.
Also, I don't know the problem you're trying to solve, but complex number are quite strange for linear optimization.
The complex plane isn't a ordered field, so that is not possible to say that a given complex number z1 > z2
You'll probably have to model your problem in such way that you can decompose the constraints with real and imaginary parts, so that you can work only with real numbers.
I am working on a Python package for computing several NP-Hard graph invariants. The current version of the package uses brute force for nearly all of the algorithms, but I am very interested in using integer programming to help speed up the computations for larger graphs.
For example, a simple integer program for solving the independence number of an n-vertex graph is to maximize given the constraints , where .
How do I solve this using PuLP? Is PuLP my best option, or would it be beneficial to use solvers in another language, like Julia, and interface may package with those?
I don't propose to write your full implementation for you, but to address the final question about PuLP versus other languages.
PuLP provides a Python wrapper over a range of existing LP Solvers.
Once you have specified your problem with a Python syntax, it converts it to another language internally (e.g. you can save .lp files, and inspect them) and passes that to any one of a number of third-party solvers, that generally aren't written in Python.
So there is no need to learn another language to get a better solver.
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Is there an open-source alternative to MATLAB's fmincon function for constrained linear optimization? I'm rewriting a MATLAB program to use Python / NumPy / SciPy and this is the only function I haven't found an equivalent to. A NumPy-based solution would be ideal, but any language will do.
Is your problem convex? Linear? Non-linear? I agree that SciPy.optimize will probably do the job, but fmincon is a sort of bazooka for solving optimization problems, and you'll be better off if you can confine it to one of the categories below (in increasing level of difficulty to solve efficiently)
Linear Program (LP)
Quadratic Program (QP)
Convex Quadratically-Constrained Quadratic Program (QCQP)
Second Order Cone Program (SOCP)
Semidefinite Program (SDP)
Non-Linear Convex Problem
Non-Convex Problem
There are also combinatoric problems such as Mixed-Integer Linear Programs (MILP), but you didn't mention any sort of integrality constraints, suffice to say that they fall into a different class of problems.
The CVXOpt package will be of great use to you if your problem is convex.
If your problem is not convex, you need to choose between finding a local solution or the global solution. Many convex solvers 'sort of' work in a non-convex domain. Finding a good approximation to the global solution would require some form Simulated Annealing or Genetic Algorithm. Finding the global solution will require an enumeration of all local solutions or a combinatorial strategy such as Branch and Bound.
Python optimization software:
OpenOpt http://openopt.org (this one is numpy-based as you wish, with automatic differentiation by FuncDesigner)
Pyomo https://software.sandia.gov/trac/coopr/wiki/Package/pyomo
CVXOPT http://abel.ee.ucla.edu/cvxopt/
NLPy http://nlpy.sourceforge.net/
The open source Python package,SciPy, has quite a large set of optimization routines including some for multivariable problems with constraints (which is what fmincon does I believe). Once you have SciPy installed type the following at the Python command prompt
help(scipy.optimize)
The resulting document is extensive and includes the following which I believe might be of use to you.
Constrained Optimizers (multivariate)
fmin_l_bfgs_b -- Zhu, Byrd, and Nocedal's L-BFGS-B constrained optimizer
(if you use this please quote their papers -- see help)
fmin_tnc -- Truncated Newton Code originally written by Stephen Nash and
adapted to C by Jean-Sebastien Roy.
fmin_cobyla -- Constrained Optimization BY Linear Approximation
GNU Octave is another MATLAB clone that might have what you need.
For numerical optimization in Python you may take a look at OpenOpt solvers:
http://openopt.org/NLP
http://openopt.org/Problems
I don't know if it's in there, but there's a python distribution called Enthought that might have what you're looking for. It was designed specifically for data analysis has over 60 additional libraries.
Have a look at http://www.aemdesign.com/downloadfsqp.htm.
There you will find C code which provides the same functionality as fmincon. (However, using a different algorithm. You can read the manual if you are interested in the details.)
It's open source but not under GPL.
There is a program called SciLab that is a MATLAB clone.
I haven't used it at all, but it is open source and might have the function you are looking for.
Octave in the latest version implements an equivalent to the Matlab fmincon function into the optimization package.
https://octave.sourceforge.io/optim/function/fmincon.html
Scilab has an implementation of fmincon (using IPOpt) which is now regularly updated:
https://atoms.scilab.org/toolboxes/fmincon
For large-scale optimization it outperforms Matlab's fmincon.