Search type Search syntax
Tags [tag]
Exact "words here"
Author user:1234
user:me (yours)
Score score:3 (3+)
score:0 (none)
Answers answers:3 (3+)
answers:0 (none)
isaccepted:yes
hasaccepted:no
inquestion:1234
Views views:250
Sections title:apples
body:"apples oranges"
URL url:"*.example.com"
Favorites infavorites:mine
infavorites:1234
Status closed:yes
duplicate:no
migrated:no
wiki:no
Types is:question
is:answer
Exclude -[tag]
-apples
For more details on advanced search visit our help page
Results tagged with Search options user 80479

Gradient descent is a first-order iterative optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. For stochastic gradient descent there is also the [sgd] tag.

17
votes
Genetic algorithms (GA) are a family of heuristics which are empirically good at providing a decent answer in many cases, although they are rarely the best option for a given domain. You mention deri …
answered Dec 3 '16 by lacerbi
40
votes
The literature on evaluation of expensive black-box function is quite vast and it is usually based on surrogate-model methods, as other people pointed out. Black-box here means that little is known ab …
answered Feb 1 '16 by lacerbi
5
votes
The state-of-the-art for non-convex optimization in a complex, ill-conditioned and multi-modal landscape is Covariance Matrix Adaptation - Evolution Strategies, aka CMA-ES, which in various versions ( …
answered Feb 17 '16 by lacerbi