mathematical-optimization

I noticed that R doesn't use all of my CPU, and I want to increase that tremendously (upwards to 100%). I don't want it to just parallelize a few functions; I want R to use more of my CPU resources. I am trying to run a pure IP set packing program using the lp() function. Currently, I run windows and I have 4 cores on my computer. I have tried to experiment with snow, doParallel, and foreach (though I do not know what I am doing with them really). In my code I have this... library(foreach) library(doParallel) library(snowfall) cl <- makeCluster(4) registerDoParallel(cl) sfInit(parallel = TRUE,

I have a neural network written in Java which uses a sigmoid transfer function defined as follows: private static double sigmoid(double x) { return 1 / (1 + Math.exp(-x)); } and this is called many times during training and computation using the network. Is there any way of speeding this up? It's not that it's slow, it's just that it is used a lot, so a small optimisation here would be a big overall gain. tangens For neural networks, you don't need the exact value of the sigmoid function. So you can precalculate 100 values and reuse the value that is closest to your input, or even better (as a

I am getting an error in my c++/gurobi file: Error code = 10004 Unable to retrieve attribute 'X' I read that this might have something to do with labels? But I don't see how there is a problem. It works for some input files, but not for others. So I have created a toy file, t5.txt in attachment. This file does not work, but removing the last column and setting 8 to 7 fixes it. I am puzzled... Below is the output of model.write. Everything seems to make sense, any Ideas what I am doing wrong? Whenever I do a model.write(test.sol), the program stops, so there seems to be something wrong with the

I want to do a root search for the following nonlinear equations, I do it in Python but it doesn't work. my code is below from pylab import * import scipy import scipy.optimize def z1(x,y): temp=1+1j+x+2*y; return temp def z2(x,y): temp=-1j-2*x+sqrt(3)*y; return temp def func(x): temp=[z1(x[0],x[1])-1.0/(1-1.0/(z2(x[0],x[1]))),1-2.0/(z2(x[0],x[1])-4.0/z1(x[0],x[1]))] return temp result=scipy.optimize.fsolve(func,[1+1j,1+1j]) print result when I run it, it shows errors: ---> 30 result=scipy.optimize.fsolve(func,[1+1j,1+1j]) C:\Python27\lib\site-packages\scipy\optimize\minpack.py in fsolve(func,

Can I solve a system of nonlinear equations in terms of parameters in python? Is there a example or tutorial? I can do this easily in maple, but the expressions for my particular system are pretty big and copying them over is quite hard. Example: sigma*(y-x) = 0 x*(rho-z)-y = 0 x*y-beta*z = 0 You should get the solutions: [[x = 0, y = 0, z = 0], [x = sqrt(beta*rho-beta), y = sqrt(beta*rho-beta), z = rho-1], [x = -sqrt(beta*rho-beta), y = -sqrt(beta*rho-beta), z = rho-1]] The reason I ask: I have a large system of nonlinear ODEs. I want to solve for the fixed points (this is doable, it's been