# Is this a form of stochastic gradient descent?

I want to minimize the following with respect to parameters $$B$$.

$$\sum_{k = 1}^{K} f(A_{k}, B)$$

where $$A_k$$ are $$K$$ different data-sets and $$B$$ is a matrix of parameters.

Can I do this by a gradient descent where:

1. randomly pick one matrix $$A_{i}$$ among $$K$$ matrices.
2. perform a gradient step that minimizes $$f(A_{i}, B)$$
3. repeat steps 1 and 2 until convergence.

The function $$f$$ is the logarithm of a linear function.

After searching online, this looks like a stochastic gradient descent. Is this really an SGD?

• Yes this is SGD. – Nick Alger Jan 15 at 21:01