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{{Short description|Family of iterative methods}}
'''Stochastic approximation''' methods are a family of [[iterative methods]] typically used for [[root-finding]] problems or for [[optimization problem|optimization]] problems. The recursive update rules of stochastic approximation methods can be used, among other things, for solving linear systems when the collected data is corrupted by noise, or for approximating [[Extremum|extreme values]] of functions which cannot be computed directly, but only estimated via noisy observations.
In a nutshell, stochastic approximation algorithms deal with a function of the form <math display="inline"> f(\theta) = \operatorname E_{\xi} [F(\theta,\xi)] </math>
which is the [[expected value]] of a function depending on a [[random variable]] <math display="inline">\xi </math>. The goal is to recover properties of such a function <math display="inline">f</math> without evaluating it directly. Instead, stochastic approximation algorithms use random samples of <math display="inline">F(\theta,\xi)</math> to efficiently approximate properties of <math display="inline">f</math> such as zeros or extrema.
Recently, stochastic approximations have found extensive applications in the fields of statistics and machine learning, especially in settings with [[big data]]. These applications range from [[stochastic optimization]] methods and algorithms, to online forms of the [[Expectation–maximization algorithm| EM algorithm]], reinforcement learning via [[Temporal difference learning|temporal differences]], and [[deep learning]], and others.<ref name=":1">{{cite journal |last1=Toulis |first1=Panos |first2=Edoardo |last2=Airoldi|title=Scalable estimation strategies based on stochastic approximations: classical results and new insights |journal=Statistics and Computing |volume=25 |issue=4 |year=2015 |pages=781–795|doi=10.1007/s11222-015-9560-y|pmid=26139959 |pmc=4484776 }}</ref>
Stochastic approximation algorithms have also been used in the social sciences to describe collective dynamics: fictitious play in learning theory and consensus algorithms can be studied using their theory.<ref>{{cite web|last1=Le Ny|first1=Jerome|title=Introduction to Stochastic Approximation Algorithms|url=http://www.professeurs.polymtl.ca/jerome.le-ny/teaching/DP_fall09/notes/lec11_SA.pdf|website=Polytechnique Montreal|publisher=Teaching Notes|
The earliest, and prototypical, algorithms of this kind are the '''Robbins–Monro''' and '''Kiefer–Wolfowitz''' algorithms introduced respectively in 1951 and 1952.
==Robbins–Monro algorithm==
The Robbins–Monro algorithm, introduced in 1951 by [[Herbert Robbins]] and [[John U. Monro#Personal life|Sutton Monro]],<ref name="rm">{{Cite journal | last1 = Robbins | first1 = H. |
Here, <math>a_1, a_2, \dots</math> is a sequence of positive step sizes. [[Herbert Robbins|Robbins]] and Monro proved<ref name="rm" /><sup>, Theorem 2</sup> that <math>\theta_n</math> [[convergence of random variables|converges]] in <math>L^2</math> (and hence also in probability) to <math>\theta^*</math>, and Blum<ref name=":0">{{Cite journal|last=Blum|first=Julius R.|date=1954-06-01|title=Approximation Methods which Converge with Probability one|journal=The Annals of Mathematical Statistics|language=EN|volume=25|issue=2|pages=382–386|doi=10.1214/aoms/1177728794|issn=0003-4851|doi-access=free}}</ref> later proved the convergence is actually with probability one, provided that:
* <math display="inline">N(\theta)</math> is uniformly bounded,
* <math display="inline">M(\theta)</math> is nondecreasing,
* <math display="inline">M'(\theta^*)</math> exists and is positive, and
* The sequence <math display="inline">a_n</math> satisfies the following requirements:
<math display="block">\qquad \sum^{\infty}_{n=0}a_n = \infty \quad \mbox{ and } \quad \sum^{\infty}_{n=0}a^2_n < \infty \quad </math>
A particular sequence of steps which satisfy these conditions, and was suggested by Robbins–Monro, have the form: <math display="inline">a_n=a/n</math>, for <math display="inline"> a > 0 </math>. Other series, such as <math>a_n = \frac{1}{n \ln n}, \frac{1}{n \ln n \ln\ln n}, \dots</math> are possible but in order to average out the noise in <math display="inline">N(\theta)</math>, the above condition must be met. === Example ===
Consider the problem of estimating the mean <math>\theta^*</math> of a probability distribution from a stream of independent samples <math>X_1, X_2, \dots</math>.
Let <math>N(\theta) := \theta - X</math>, then the unique solution to <math display="inline">\operatorname E[N(\theta)] = 0</math> is the desired mean <math>\theta^*</math>. The RM algorithm gives us<math display="block">\theta_{n+1}=\theta_n - a_n(\theta_n - X_n) </math>This is equivalent to [[stochastic gradient descent]] with loss function <math>L(\theta) = \frac 12 \|X - \theta\|^2 </math>. It is also equivalent to a weighted average:<math display="block">\theta_{n+1}=(1-a_n)\theta_n + a_n X_n </math>In general, if there exists some function <math>L</math> such that <math>\nabla L(\theta) = N(\theta) - \alpha </math>, then the Robbins–Monro algorithm is equivalent to stochastic gradient descent with loss function <math>L(\theta)</math>. However, the RM algorithm does not require <math>L</math> to exist in order to converge.
===Complexity results===
#If <math display="inline">f(\theta)</math> is twice continuously differentiable, and strongly convex, and the minimizer of <math display="inline">f(\theta)</math> belongs to the interior of <math display="inline">\Theta</math>, then the Robbins–Monro algorithm will achieve the asymptotically optimal convergence rate, with respect to the objective function, being <math display="inline">\operatorname E[f(\theta_n) - f^*] = O(1/n)</math>, where <math display="inline">f^*</math> is the minimal value of <math display="inline">f(\theta)</math> over <math display="inline">\theta \in \Theta</math>.<ref name="jsacks">{{Cite journal | last1 = Sacks | first1 = J. | title = Asymptotic Distribution of Stochastic Approximation Procedures | doi = 10.1214/aoms/1177706619 | journal = The Annals of Mathematical Statistics | volume = 29 | issue = 2 | pages = 373–405 | year = 1958 | jstor = 2237335
# Conversely, in the general convex case, where we lack both the assumption of smoothness and strong convexity, Nemirovski and Yudin<ref name="NYcomp">Problem Complexity and Method Efficiency in Optimization, A. Nemirovski and D. Yudin, ''Wiley -Intersci. Ser. Discrete Math'' '''15''' ''John Wiley'' ''New York'' (1983) .</ref> have shown that the asymptotically optimal convergence rate, with respect to the objective function values, is <math display="inline">O(1/\sqrt{n})</math>. They have also proven that this rate cannot be improved.
===Subsequent developments and Polyak–Ruppert averaging===
While the Robbins–Monro algorithm is theoretically able to achieve <math display="inline"> O(1/n)</math> under the assumption of twice continuous differentiability and strong convexity, it can perform quite poorly upon implementation. This is primarily due to the fact that the algorithm is very sensitive to the choice of the step size sequence, and the supposed asymptotically optimal step size policy can be quite harmful in the beginning.<ref name="NJLS" /><ref name="jcsbook">[https://books.google.com/books?id=f66OIvvkKnAC
Chung (1954)<ref>{{Cite journal|last=Chung|first=K. L.|date=1954-09-01|title=On a Stochastic Approximation Method|journal=The Annals of Mathematical Statistics|language=EN|volume=25|issue=3|pages=463–483|doi=10.1214/aoms/1177728716|issn=0003-4851|doi-access=free}}</ref>
'''''A1)'''''
Therefore, the sequence <math display="inline">a_n = n^{-\alpha}</math> with <math display="inline">0 < \alpha < 1</math> satisfies this restriction, but <math display="inline">\alpha = 1</math> does not, hence the longer steps. Under the assumptions outlined in the Robbins–Monro algorithm, the resulting modification will result in the same asymptotically optimal convergence rate <math display="inline">O(1/\sqrt{n})</math> yet with a more robust step size policy.<ref name="pj" /> Prior to this, the idea of using longer steps and averaging the iterates had already been proposed by Nemirovski and Yudin<ref name="NY">On Cezari's convergence of the steepest descent method for approximating saddle points of convex-concave functions, A. Nemirovski and D. Yudin, ''Dokl. Akad. Nauk SSR'' '''2939''', (1978 (Russian)), Soviet Math. Dokl. '''19''' (1978 (English)).</ref> for the cases of solving the stochastic optimization problem with continuous convex objectives and for convex-concave saddle point problems. These algorithms were observed to attain the nonasymptotic rate <math display="inline">O(1/\sqrt{n})</math>.
A more general result is given in Chapter 11 of Kushner and Yin<ref>{{Cite book|url=https://www.springer.com/us/book/9780387008943|title=Stochastic Approximation and Recursive Algorithms and {{!}} Harold Kushner {{!}} Springer|
<math display="block">\theta^n(t)=\theta_{n+i},\quad U^n(t)=(\theta_{n+i}-\theta^*)/\sqrt{a_{n+i}}\quad\mbox{for}\quad t\in[t_{n+i}-t_n,t_{n+i+1}-t_n),i\ge0</math>Let the iterate average be <math>\Theta_n=\frac{a_n}{t}\sum_{i=n}^{n+t/a_n-1}\theta_i</math> and the associate normalized error to be <math>\hat{U}^n(t)=\frac{\sqrt{a_n}}{t}\sum_{i=n}^{n+t/a_n-1}(\theta_i-\theta^*)</math>.
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With assumption '''A1)''' and the following '''A2)'''
'''''A2)''''' ''There is a Hurwitz matrix <math display="inline">A</math> and a symmetric and positive-definite matrix <math display="inline">\Sigma</math> such that <math display="inline">\{U^n(\cdot)\}</math> converges weakly to <math display="inline">U(\cdot)</math>, where <math display="inline">U(\cdot)</math> is the statisolution to
satisfied, and define ''<math display="inline">\bar{V}=(A^{-1})'\Sigma(A')^{-1}</math>''. Then for each ''<math display="inline">t</math>'',
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=== Application in stochastic optimization ===
Suppose we want to solve the following stochastic optimization problem<math display="block">g(\theta^*) = \min_{\theta\in\Theta}\operatorname{E}[Q(\theta,X)],</math>where <math display="inline">g(\theta) = \operatorname{E}[Q(\theta,X)]</math> is differentiable and convex, then this problem is equivalent to find the root <math>\theta^*</math> of <math>\nabla g(\theta) = 0</math>. Here <math>Q(\theta,X)</math> can be interpreted as some "observed" cost as a function of the chosen <math>\theta</math> and random effects <math>X</math>. In practice, it might be hard to get an analytical form of <math>\nabla g(\theta)</math>, Robbins–Monro method manages to generate a sequence <math>(\theta_n)_{n\geq 0}</math> to approximate <math>\theta^*</math> if one can generate <math>(X_n)_{n\geq 0}▼
▲<math display="block">g(\theta^*) = \min_{\theta\in\Theta}\operatorname{E}[Q(\theta,X)],</math>where <math display="inline">g(\theta) = \operatorname{E}[Q(\theta,X)]</math> is differentiable and convex, then this problem is equivalent to find the root <math>\theta^*</math> of <math>\nabla g(\theta) = 0</math>. Here <math>Q(\theta,X)</math> can be interpreted as some "observed" cost as a function of the chosen <math>\theta</math> and random effects <math>X</math>. In practice, it might be hard to get an analytical form of <math>\nabla g(\theta)</math>, Robbins–Monro method manages to generate a sequence <math>(\theta_n)_{n\geq 0}</math> to approximate <math>\theta^*</math> if one can generate <math>(X_n)_{n\geq 0}
</math> , in which the conditional expectation of <math>X_n
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Here <math>H(\theta, X)</math> is an unbiased estimator of <math>\nabla g(\theta)</math>. If <math>X</math> depends on <math>\theta</math>, there is in general no natural way of generating a random outcome <math>H(\theta, X)</math> that is an unbiased estimator of the gradient. In some special cases when either IPA or likelihood ratio methods are applicable, then one is able to obtain an unbiased gradient estimator <math>H(\theta, X)</math>. If <math>X</math> is viewed as some "fundamental" underlying random process that is generated ''independently'' of <math>\theta</math>, and under some regularization conditions for derivative-integral interchange operations so that <math>\operatorname{E}\Big[\frac{\partial}{\partial\theta}Q(\theta,X)\Big] = \nabla g(\theta)</math>, then <math>H(\theta, X) = \frac{\partial}{\partial \theta}Q(\theta, X)</math> gives the fundamental gradient unbiased estimate. However, for some applications we have to use finite-difference methods in which <math>H(\theta, X)</math> has a conditional expectation close to <math>\nabla g(\theta)</math> but not exactly equal to it.
We then define a recursion analogously to [[Newton's Method]] in the deterministic algorithm:
: <math display="block">\theta_{n+1} = \theta_n - \varepsilon_n H(\theta_n,X_{n+1}).</math>
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The following result gives sufficient conditions on <math>\theta_n
</math> for the algorithm to converge:<ref>{{Cite book|title=Numerical Methods for stochastic Processes|
C1) <math>\varepsilon_n \geq 0, \forall\; n\geq 0. </math>
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==Kiefer–Wolfowitz algorithm==
The Kiefer–Wolfowitz algorithm was introduced in 1952 by [[Jacob Wolfowitz]] and [[Jack_Kiefer_(statistician)|Jack Kiefer]],<ref name = "KW">{{Cite journal | last1 = Kiefer | first1 = J. | last2 = Wolfowitz | first2 = J. | doi = 10.1214/aoms/1177729392 | title = Stochastic Estimation of the Maximum of a Regression Function | journal = The Annals of Mathematical Statistics | volume = 23 | issue = 3 | pages = 462 | year = 1952
Let <math>M(x) </math> be a function which has a maximum at the point <math>\theta </math>. It is assumed that <math>M(x)</math> is unknown; however, certain observations <math>N(x)</math>, where <math>\operatorname E[N(x)] = M(x)</math>, can be made at any point <math>x</math>. The structure of the algorithm follows a gradient-like method, with the iterates being generated as ::<math> x_{n+1} = x_n + a_n
where <math>N(x_n+c_n)</math> and <math>N(x_n-c_n)</math> are independent
Kiefer and Wolfowitz proved that, if <math>M(x)</math> satisfied certain regularity conditions, then <math>x_n</math> will converge to <math>\theta</math> in probability as <math>n\to\infty </math>, and later Blum<ref name=":0" /> in 1954 showed <math>x_n</math> converges to <math>\theta</math> almost surely, provided that:
* <math>\operatorname{Var}(N(x))\le S<\infty</math> for all <math>x</math>.
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===Subsequent developments and important issues===
#The Kiefer Wolfowitz algorithm requires that for each gradient computation, at least <math>d+1</math> different parameter values must be simulated for every iteration of the algorithm, where <math>d </math> is the dimension of the search space. This means that when <math>d</math> is large, the Kiefer–Wolfowitz algorithm will require substantial computational effort per iteration, leading to slow convergence.
## To address this problem, Spall proposed the use of [[Simultaneous perturbation stochastic approximation|simultaneous perturbations]] to estimate the gradient. This method would require only two simulations per iteration, regardless of the dimension <math>d</math>.<ref name = "Jsp">{{Cite journal | last1 = Spall | first1 = J. C. | title = Adaptive stochastic approximation by the simultaneous perturbation method | doi = 10.1109/TAC.2000.880982 | journal = IEEE Transactions on Automatic Control | volume = 45 | issue = 10 | pages = 1839–1853 | year = 2000
#In the conditions required for convergence, the ability to specify a predetermined compact set that fulfills strong convexity (or concavity) and contains the unique solution can be difficult to find. With respect to real world applications, if the ___domain is quite large, these assumptions can be fairly restrictive and highly unrealistic.
==Further developments==
An extensive theoretical literature has grown up around these algorithms, concerning conditions for convergence, rates of convergence, multivariate and other generalizations, proper choice of step size, possible noise models, and so on.<ref name="kushneryin">{{Cite book | last1 = Kushner | first1 = H. J. |
[[C. Johan Masreliez]] and [[R. Douglas Martin]] were the first to apply
stochastic approximation to [[Robust statistics|robust]] [[estimation]].<ref>{{Cite journal | last1 = Martin | first1 = R. | last2 = Masreliez | first2 = C. | doi = 10.1109/TIT.1975.1055386 | title = Robust estimation via stochastic approximation | journal = IEEE Transactions on Information Theory | volume = 21 | issue = 3 | pages = 263 | year = 1975
The main tool for analyzing stochastic approximations algorithms (including the Robbins–Monro and the Kiefer–Wolfowitz algorithms) is a theorem by [[Aryeh Dvoretzky]] published in
| last = Dvoretzky | first = Aryeh | author-link = Aryeh Dvoretzky
| editor-last = Neyman | editor-first = Jerzy | editor-link = Jerzy Neyman
| contribution = On stochastic approximation
| contribution-url = https://projecteuclid.org/euclid.bsmsp/1200501645
| mr = 84911
| pages = 39–55
| publisher = University of California Press
| title = Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 1954–1955, vol. I
| year = 1956}}</ref>
==See also==
*[[Stochastic gradient descent]]
*[[Stochastic variance reduction]]
==References==
{{reflist}}
{{Statistics|collection}}
{{DEFAULTSORT:Stochastic Approximation}}
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