mathematical-optimization

I'm using scipy.optimize.minimize for unrestricted optimization of an objective function which receives a couple of parameters and runs a complex numerical simulation based on these parameters. This simulation does not always converge in which case I make the objective function return inf, in some cases, in others NaN. I thought that this hack would prevent the minimization from converging anywhere near a set of parameters that makes the simulation diverge. Instead, I encountered a case where the simulation won't even converge for the starting set of parameters but instead of failing, the

I'm new to stackoverflow and searched a lot, but couldn't find an answer to my question. I'm trying to minimise the problem bellow with the optimisation package Rsolnp. Although the solver gives me a solution, every time I run the code I get the following warning message: Warning messages: 1: In cbind(temp, funv) : number of rows of result is not a multiple of vector length (arg 1) Furthermore, the solution is completely different from the solutions I get with ipop and solve.QP. Their solutions are almost the same (0.2480, 0.0000, 0.0121, 0.7400). I tried many different formulations of the

I'm performing an operation, lets call it CalculateSomeData. CalculateSomeData operates in successive "generations", numbered 1..x. The number of generations in the entire run is fixed by the input parameters to CalculateSomeData and is known a priori. A single generation takes anywhere from 30 minutes to 2 hours to complete. Some of that variability is due to the input parameters and that cannot be controlled. However, a portion of that variability is due to things like hardware capacities, CPU load from other processes, network bandwidth load, etc. One parameter that can be controlled per

I have a few inequalities regarding {x,y} , that satisfies the following equations: x>=0 y>=0 f(x,y)=x^2+y^2>=100 g(x,y)=x^2+y^2<=200 Note that x and y must be integer. Graphically it can be represented as follows, the blue region is the region that satisfies the above inequalities: The question now is, is there any function in Matlab that finds every admissible pair of {x,y} ? If there is an algorithm to do this kind of thing I would be glad to hear about it as well. Of course, one approach we can always use is brute force approach where we test every possible combination of {x,y} to see

Given a set of points in a 3D Cartesian space, I am looking for an algorithm that will sort these points, such that the minimal Euclidean distance between two consecutive points would be maximized. It would also be beneficial if the algorithm tends to maximize the average Euclidean distance between consecutive points. Edit: I've crossposted on https://cstheory.stackexchange.com/ and got a good answer. See https://cstheory.stackexchange.com/questions/8609/sorting-points-such-that-the-minimal-euclidean-distance-between-consecutive-poin . Here is a lower bound for the cost of the solution, which

I am looking for an open source implementation of a method doing constrained optimization for nonlinear multivariable function in Java . There are several open source java implementations that can do this, such as: OptaPlanner (apache license, 100% java, lots of examples and documentation) jacop choco ... IPOPT is the most robust solver I know of. It has a Java interface although I have no idea how good that is, I only use the C++ API. I recently ported Michael Powells' COBYLA2 derivative-free optimizer for nonlinear objective functions and constraints to Java. You'll find the source code here