GXM | This is the github repository for GeoExt Mobile | Frontend Framework library

There are two issues with your code:

1. In your second if statement you forgot abs, it has to be if (isTRUE(abs(gm[i]) > root)
2. You forgot to increase the value of your counter, i.e. i remains 1.

Additionally I made a small adjustment to your code. Instead of using if-if-else-if first check wether the root is found and if not update l and r using one if-else. Makes it easier to read and understand.

Because I updated my registration from GMCRegistrar.register() to GoogleCloudMessaging.register() I had to update my broadcast receiver to a WakefulBroadcastReceiver.

This link gave me the code I needed to grab the updated GCM messages.

Full steps I took:

1. Created new Custom WakefulBoradcastReciever to get new GCM messages.

CGCMBroadcastReciever.java

MATLAB has a multitude of functions for working with sets, including setdiff, intersect, union etc. In this case, you can use the ismember function:

One way to do this would be to treat each matrix as vectors in N dimensional space and you can find the L₂ norm (or the Euclidean distance) of each vector. After, check if the distance is 0. If it is, then you have a match. Specifically, you can create a matrix such that element (i,j) in this matrix calculates the distance between row i in one matrix to row j in the other matrix.

You can treat your problem by modifying the distance matrix that results from this problem such that 1 means the two vectors completely similar and 0 otherwise.

This post should be of interest: Efficiently compute pairwise squared Euclidean distance in Matlab.

I would specifically look at the answer by Shai Bagon that uses matrix multiplication and broadcasting. You would then modify it so that you find distances that would be equal to 0:

You can use an extreme form of vectorization, because what you have is basically a matrix product. In order to see this, consider

It's quite hard to do this vectorized, especially if the subsets you're trying to compare to are embedded in each row. What can be done for efficiency is to change the Ksets into a 3D matrix where each slice contains those subsets formatted into a 2D matrix where each subset is on a per-row basis. You can then use ismember combined with using just one loop on each row individually and populate your results.