# Different parameter values when using stochastic gradient descent

I am having some issues with stochastic gradient descent. Using batch gradient descent where I consider all the training sets I have certain parameter values which I know are correct.

My function is convex,globally.

Now when I use stochastic gradient descent considering ten samples at a time, the algorithm converges nicely, but I get different parameter values. The parameter values that I get using stochastic gradient descent is like 1/2 of the one that I get using batch gradient descent.

Any insights what could go wrong?