accelerate-framework

I would like to find the inverse of a matrix. I know this involves first LU factorisation then the inversion step but I cannot find the required function by searching apple's docs of 10.7! This seems like a useful post Symmetric Matrix Inversion in C using CBLAS/LAPACK , pointing out that the sgetrf_ and sgetri_ functions should be used. However searching these terms I find nothing in Xcode docs. Does anybody have boiler plate code for this matrix operation? Apple does not document the LAPACK code at all, I guess because they just implement the standard interface from netlib.org . It's a shame

I do not have much math background but part of the project I am working on requires the FFT of a single vector. The matlab function fft(x) works accurately for what I need, but after trying to set up the Accelerate Framework fft functions I get completely inaccurate results. If anyone has more expertise/experience with Accelerate Framework fft I could really use some help trying to figure out what I am doing wrong. I based my fft set-up off an example I found on google, but there were no tutorials or anything that produced different results. EDIT1: Changed around some stuff based on the

I need to perform the autocorrelation of an array (vector) but I am having trouble finding the correct way to do so. I believe that I need the method "vDSP_conv" from the Accelerate Framework, but I can't follow how to successfully set it up. The thing throwing me off the most is the need for 2 inputs. Perhaps I have the wrong function, but I couldn't find one that operated on a single vector. The documentation can be found here Copied from the site vDSP_conv Performs either correlation or convolution on two vectors; single precision. void vDSP_conv ( const float __vDSP_signal[], vDSP_Stride _

I'm running an algorithm that is implemented in Python and uses NumPy. The most computationally expensive part of the algorithm involves solving a set of linear systems (i.e. a call to numpy.linalg.solve() . I came up with this small benchmark: import numpy as np import time # Create two large random matrices a = np.random.randn(5000, 5000) b = np.random.randn(5000, 5000) t1 = time.time() # That's the expensive call: np.linalg.solve(a, b) print time.time() - t1 I've been running this on: My laptop, a late 2013 MacBook Pro 15" with 4 cores at 2GHz ( sysctl -n machdep.cpu.brand_string gives me