What stops gradient descent from finding the largest error?

Question

If a gradient points towards a max or a min what stops gradient descent from maximizing error instead of minimizing it?

Is it the nature of the update step that makes this process one way?

Possible duplicate of [How can change in cost function be positive?](https://stats.stackexchange.com/questions/364360/how-can-change-in-cost-function-be-positive) see also https://stats.stackexchange.com/questions/367397/for-convex-problems-does-gradient-in-stochastic-gradient-descent-sgd-always-p/367459#367459 — Sycorax, Apr 22 '19 at 17:19

score 0 · Answer 1 · answered Apr 22 '19 at 16:58

0

In gradient decent, we are following the negative gradient direction, where the objective function will decrease instead of increase.

answered Apr 22 '19 at 16:58

Haitao Du

1 Answers1