Does Minibatch reduce drawback of SGD? Many expert said "Batch has more local optimal possibility than SGD". But, I don't know the reason..



How SGD could avoid local optimal better than Batch?
(Some people tell me about over shooting as the reason. But, I don't also know there is more over shooting in SGD)

If you explain this reason by using Geometric, Mathmatics or other intuition concept, It's gonna be great!
For gradient descent, batch updates calculate the weights based on all examples. This process continues until convergence which is typically measured by a derivative of zero. The resulting weights might end up in a local minimum or saddle point because both have a derivative of zero. 

Stochastic gradient descent takes a random sampling of examples, introducing randomness into the updates. This randomness increases the possibilities of escaping a local minimum or saddle point. The updates might randomly overestimate the area with a derivative of zero and find a new section of the error surface that leads to a better optimum.