mathematical-optimization

I am trying to combine cvxopt (an optimization solver) and PyMC (a sampler) to solve convex stochastic optimization problems . For reference, installing both packages with pip is straightforward: pip install cvxopt pip install pymc Both packages work independently perfectly well. Here is an example of how to solve an LP problem with cvxopt : # Testing that cvxopt works from cvxopt import matrix, solvers # Example from http://cvxopt.org/userguide/coneprog.html#linear-programming c = matrix([-4., -5.]) G = matrix([[2., 1., -1., 0.], [1., 2., 0., -1.]]) h = matrix([3., 3., 0., 0.]) sol = solvers

I'm looking for an optimization library. My two requirements are that it does not use JNI and that it does not have license restrictions preventing it from being used on multiple computers commercially. The only one I've found that meets these requirements is Choco, but it is unusably buggy. Since I couldn't find any optimization software in Java I wrote my own implementation of the Simplex Method and submitted it to Apache Commons Math library: https://issues.apache.org/jira/browse/MATH-246 http://ojalgo.org/generated/org/ojalgo/optimisation/linear/LinearSolver.html Recently JOptimizer , free

I have a big linear optimizer I'm running and need some help setting up the constraints to get what I want. It's hard for me to express it in words exactly (hence the vague post-title), so I've written an example, details: Select a total of 5 items, maximizing value and keeping the cost under 5k. Each item has 2 "types". They are either labeled type1 = A, B, C, D, or E, and either type2 = X or Y. 4 items must be type X, 1 must be type Y The below example works great, but I want to add two more constraints and I'm not really sure how to do it. The two other constraints: I want every

I want to further my real world semi definite programming optimization problem with a constraint on sum of absolute values. For example: abs(x1) + abs(x2) + abs(x3) <= 10. I have searched internet and documentation but could not find a way to represent this. I am using python and cvxopt module. Your constraint is equivalent to the following eight constraints: x1 + x2 + x3 <= 10 x1 + x2 - x3 <= 10 x1 - x2 + x3 <= 10 x1 - x2 - x3 <= 10 -x1 + x2 + x3 <= 10 -x1 + x2 - x3 <= 10 -x1 - x2 + x3 <= 10 -x1 - x2 - x3 <= 10 I haven't used cvxopt , so I don't know if there is an easier way to handle your

I'm looking for a good easy to use Java based Quadratic Programming (QP) solver. Googling around I came across ojAlgo ( http://ojalgo.org ). However, I was wondering if there are any other/better alternatives. Have a look at Apache Commons Math . I haven't used ojalgo, and I really can't say I've used Commons Lang enough to be able to provide you with a lot of details, but it did do what I needed. Description from their website: Commons Math is a library of lightweight, self-contained mathematics and statistics components addressing the most common problems not available in the Java

I need to solve an under-determined linear system of equations and constraints, then find the particular solution that minimises a cost function. This needs to be done in purely portable managed code that will run in .NET and Mono. What freely available libraries are there that I can use to implement this? All of the optimisation algorithms provided by free libraries I have found only support interval constraints on single variables, e.g. 0 < x < 1 , not constraints like x + 2y < 4 . I have also found that often the linear equations solvers only support linear systems with one solution. The

What's the best way to initialize a simplex for use in a Nelder-Mead simplex search from a user's 'guess' vertex? I'm not sure if there is a best way to choose the initial simplex in the Nelder-Mead method, but the following is what is done in common practice. The construction of the initial simplex S is obtained from generating n+1 vertices x0,..,xn around what you call a user's "guess" vertex xin in a N dimensional space. The most frequent choice is x0=xin and the remaining n vertices are then generated so that xj=x0+hj*ej where ej is the unit vector of the j -th coordinate axis in R^n and