[masswerk]

So you may like better,

> “Multiple simultaneous optimizers” search for a (local) maximum value of some function E(λ1, …, λn) of several parameters. Each unit Ui independently “jitters” its parameter λ1, perhaps randomly, by adding a variation δi(t) to a current mean value μi. The changes in the quantities λi and E are correlated, and the result is used to slowly change μi. The filters are to remove DC components. This technique, a form of coherent detection, usually has an advantage over methods dealing separately and sequentially with each parameter.

(In “Steps”)

:-)

[riku_iki]

can you provide link, and what conclusions you derived from this text if your interest is meaningful discussion?

[masswerk]

The link has been already provided above (opus cit), it's directly connected to the very question of gradients, providing a specific implementation (it even comes with a circuit diagram). As you were claiming a lack of detail (but apparently not honoring the provided citation)…

(The earlier you go back in the papers, the more specifics you will find.)

[riku_iki]

You didn't give me any links.

And what are your conclusion from citation? You are claiming again that Minsky invented gradient descent?

[masswerk]

For the link and claims, see the the very comment you initially answered to.

[riku_iki]

That claim was answered: Minsky didn't invent gradient descent.

[masswerk]

That claim was never made, but by you. The claim was, Minsky had practical experience and wrote about experiences with gradient descend (aka "hill climbing") and problems of locality in a paper published Jan. 1961.

On the other hand: who invented "hill climbing"? You've contributed nothing to the question, you've posed (which was never mine, nor even an implicit part of any claims made).