mathematical-optimization

can anyone show me examples or reviews for constrained nonlinear optimization in Microsoft Solver foundation 3.0? How's it compared to Matlab's fmincon? Or is there any better .net library for constrained nonlinear optimization? thanks, Yin Zhu IMPORTANT UPDATE on Feb 25, 2012: MSF 3.1 now supports nonlinear optimization with bounded variables via its NelderMeadSolver solver: http://msdn.microsoft.com/en-us/library/hh404037(v=vs.93).aspx For general linear constraints, Microsoft solver foundation only support linear programming and quadratic programming via its interior point solver. For this

Forum I've got a set of data that apparently forms an ellipse in 3D space (not an ellipsoid, but a curve in 3D). Being inspired by following thread http://au.mathworks.com/matlabcentral/newsreader/view_thread/65773 and with the help from someone ,I manage to get the optimization code running and outputs a set of best parameters x (vector). However, when I try to use this x to replicate the ellipse,the outcomes is a strange straight line in the space. I have been stacked on this for days., still don't know what went wrong....pretty much devastated... I hope someone can shed some light on this.

I'd like to know the best way to use Scipy to fit Pandas DataFrame columns. If I have a data table (Pandas DataFrame) with columns ( A , B , C , D and Z_real ) where Z depends on A, B, C and D, I want to fit a function of each DataFrame row (Series) which makes a prediction for Z ( Z_pred ). The signature of each function to fit is func(series, param_1, param_2...) where series is the Pandas Series corresponding to each row of the DataFrame. I use the Pandas Series so that different functions can use different combinations of columns. I've tried passing the DataFrame to scipy.optimize.curve

I have a programming formulation from a paper and want to give it a tool for solving specific problems. The authors stated it as an linear programming (LP) instance, however I am not sure. Formulation is somewhat like as follows: max x1+x2+x3... s.t. x1.x3+x4.x5 <= 10 x2.x5+x3.x7+x1.x9 <=10 ... I tried to program it through cplexqcp function (due to quadratic constraints, however constraints do not include any x_i^2 variable). However I receive CPLEX Error 5002: Q in %s is not positive semi-definite error . Is this an instance of non-linear programming with non-convex constraints? Can I solve

Scipy NNLS perform this: Solve argmin_x || Ax - b ||_2 for x>=0. What's the alternative way to do it if I seek strictly non-zero solution (i.e. x > 0 ) ? Here is my LP code using Scipy's NNLS: import numpy as np from numpy import array from scipy.optimize import nnls def by_nnls(A=None, B=None): """ Linear programming by NNLS """ #print "NOF row = ", A.shape[0] A = np.nan_to_num(A) B = np.nan_to_num(B) x, rnorm = nnls(A,B) x = x / x.sum() # print repr(x) return x B1 = array([ 22.133, 197.087, 84.344, 1.466, 3.974, 0.435, 8.291, 45.059, 5.755, 0.519, 0. , 30.272, 24.92 , 10.095]) A1 = array([[

My code is to implement an active learning algorithm, using L-BFGS optimization. I want to optimize four parameters: alpha , beta , w and gamma . However, when I run the code below, I got an error: optimLogitLBFGS = sp.optimize.fmin_l_bfgs_b(func, x0 = x0, args = (X,Y,Z), fprime = func_grad) File "C:\Python27\lib\site-packages\scipy\optimize\lbfgsb.py", line 188, in fmin_l_bfgs_b **opts) File "C:\Python27\lib\site-packages\scipy\optimize\lbfgsb.py", line 311, in _minimize_lbfgsb isave, dsave) _lbfgsb.error: failed in converting 7th argument ``g' of _lbfgsb.setulb to C/Fortran array 0-th