I'm trying to understand the history of Gradient descent and Stochastic gradient descent. Gradient descent was invented in Cauchy in 1847.Méthode générale pour la résolution des systèmes d'équations simultanées. pp. 536–538 For more information about it see here.

Since then gradient descent methods kept developing and I'm not familiar with their history. In particular I'm interested in the invention of stochastic gradient descent.

A reference that can be used in an academic paper in more than welcomed.

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    $\begingroup$ I learned about SGD before the machine learning, so it must have been before this whole thing $\endgroup$
    – Aksakal
    Nov 14, 2017 at 14:24
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    $\begingroup$ Well, Cauchy for sure invented GD before machine learning so I won't be surprise that SGC was also invented before. $\endgroup$
    – DaL
    Nov 14, 2017 at 14:32
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    $\begingroup$ Kiefer-Wolfowitz Stochastic Approximation en.wikipedia.org/wiki/Stochastic_approximation is most of the way there, other than not directly "simulating" for the gradient. $\endgroup$ Nov 16, 2017 at 14:24
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    $\begingroup$ "Stochastic Gradient Descent" from ML is the same as "Stochastic Subgradient Method" from convex optimization. And subgradients methods was discovered during 1960-1970 in USSR, Moscow. Maybe also in USA. I saw a video where Boris Polyak (he is author of heavy-ball method) said that he (and all people) start think about subgradients methods in 1970. (youtube.com/watch?v=2PcidcPxvyk&t=1963s).... $\endgroup$ Dec 9, 2017 at 5:43
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    $\begingroup$ Didn't Jon Skeet code it in C# earlier than Cauchy? $\endgroup$ Dec 27, 2020 at 13:52

2 Answers 2


Stochastic Gradient Descent is preceded by Stochastic Approximation as first described by Robbins and Monro in their paper, A Stochastic Approximation Method. Kiefer and Wolfowitz subsequently published their paper, *Stochastic Estimation of the Maximum of a Regression Function* which is more recognizable to people familiar with the ML variant of Stochastic Approximation (i.e Stochastic Gradient Descent), as pointed out by Mark Stone in the comments. The 60's saw plenty of research along that vein -- Dvoretzky, Powell, Blum all published results that we take for granted today. It is a relatively minor leap to get from the Robbins and Monro method to the Kiefer Wolfowitz method, and merely a reframing of the problem to then get to Stochastic Gradient Descent (for regression problems). The above papers are widely cited as being the antecedents of Stochastic Gradient Descent, as mentioned in this review paper by Nocedal, Bottou, and Curtis, which provides a brief historical perspective from a Machine Learning point of view.

I believe that Kushner and Yin in their book Stochastic Approximation and Recursive Algorithms and Applications suggest that the notion had been used in control theory as far back as the 40's, but I don't recall if they had a citation for that or if it was anecdotal, nor do I have access to their book to confirm this.

Herbert Robbins and Sutton Monro A Stochastic Approximation Method The Annals of Mathematical Statistics, Vol. 22, No. 3. (Sep., 1951), pp. 400-407, DOI: 10.1214/aoms/1177729586

J. Kiefer and J. Wolfowitz Stochastic Estimation of the Maximum of a Regression Function Ann. Math. Statist. Volume 23, Number 3 (1952), 462-466, DOI: 10.1214/aoms/1177729392

Leon Bottou and Frank E. Curtis and Jorge Nocedal Optimization Methods for Large-Scale Machine Learning, Technical Report, arXiv:1606.04838

  • $\begingroup$ Can you give exact references? And for the invention of SGD, it seems to be in the 40's but it is not clear by who and where? $\endgroup$
    – DaL
    Nov 19, 2017 at 7:08
  • $\begingroup$ Certainly it's widely believed to be Robbins and Monro in 1951 with Stochastic Approximation Algorithms. I have heard that something similar showed up in the control theory literature in the 40's (like I said, I think from Kushner and Yin but I don't have that book handy), but aside from that one place everyone seems to cite Robbins and Monro, including the Nocedal et al. reference I linked to. $\endgroup$ Nov 19, 2017 at 17:23
  • $\begingroup$ So our leading candidate now is H. Robbins and S. Monro. A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3):400–407, 1951., as written in Nocedal, Bottou, and Curtis in pdfs.semanticscholar.org/34dd/… $\endgroup$
    – DaL
    Nov 20, 2017 at 6:35
  • $\begingroup$ I so it is referred to as the origin of SGD but in the summary (actually abstract in today terms) it is written "M(x) is assumed to he a monotone function of x but is unkno~vn to the experimenter, and it is desired to find the solution x = 0 of thc equation M(x)= a, where a is a given constant." If M(x) is unknown, one cannot derive it. Maybe it is another ancient ancestor? $\endgroup$
    – DaL
    Nov 20, 2017 at 6:50
  • $\begingroup$ Agreed, in some sense. Kiefer Wolfowitz used the analysis of this to come up with their paper which is more recognizable in the form we see today. As mentioned above by Mark Stone. Their paper can be found here: projecteuclid.org/download/pdf_1/euclid.aoms/1177729392. $\endgroup$ Nov 20, 2017 at 14:52


Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological review. 1958 Nov;65(6):386.

I am not sure if SGD was invented before this in optimization literature—probably was—but here I believe he describes an application of SGD to train a perceptron.

If the system is under a state of positive reinforcement, then a positive AV is added to the values of all active A-units in the source-sets of "on" responses, while a negative A V is added to the active units in the source- sets of "off" responses.

He calls these "two types of reinforcement".

He also references a book with more on these "bivalent systems".

Rosenblatt F. The perceptron: a theory of statistical separability in cognitive systems (Project Para). Cornell Aeronautical Laboratory; 1958.

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    $\begingroup$ A good step ahead, thanks! I find the first reference online here citeseerx.ist.psu.edu/viewdoc/… I'll go over it. However, I expect to find the algorithm more explicit and formal. $\endgroup$
    – DaL
    Nov 14, 2017 at 14:19
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    $\begingroup$ +1 for the remark about optimization. Since it's used in Machine Learning to do optimization and since optimization became a big deal 40 or 50 years before ML -- and computers also entered the picture about the same time -- that seems like a good lead. $\endgroup$
    – Wayne
    Nov 14, 2017 at 15:12
  • $\begingroup$ I don't understand why you say that this quote describes SGD. $\endgroup$
    – amoeba
    Nov 14, 2017 at 15:13
  • $\begingroup$ @amoeba hopefully I am not making a mistake, was just skimming the paper, but I though he was describing the perceptron update which is just SGD with constant learning rate. $\endgroup$
    – sjw
    Nov 14, 2017 at 15:36
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    $\begingroup$ That's right. I am just saying that the stochastic aspect is not evident from the quote you chose. I mean, "stochastic" GD simply means that updates are done one training sample at a time (instead of computing gradient using all the available training samples). The algorithm given in en.wikipedia.org/wiki/Perceptron#Steps makes this "stochastic" aspect immediately clear in step #2. $\endgroup$
    – amoeba
    Nov 14, 2017 at 15:58

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