Stochastic approximation

Known as: Robbins-Monro procedure, Kiefer-Wolfowitz algorithm, Kiefer-Wolfowitz process 
Stochastic approximation methods are a family of iterative stochastic optimization algorithms that attempt to find zeroes or extrema of functions… (More)
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2011
Highly Cited
2011
In this paper, we consider the minimization of a convex objective function defined on a Hilbert space, which is only available… (More)
  • figure 1
  • figure 2
  • figure 3
  • table 1
Is this relevant?
Highly Cited
2008
Highly Cited
2008
In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic… (More)
  • table 4.1
  • table 4.3
  • table 4.4
  • table 4.5
  • table 4.7
Is this relevant?
Highly Cited
2007
Highly Cited
2007
Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function… (More)
Is this relevant?
Highly Cited
2007
Highly Cited
2007
The Wang–Landau (WL) algorithm is an adaptive Markov chain Monte Carlo algorithm used to calculate the spectral density for a… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2005
Highly Cited
2005
In this paper we address the problem of the stability and convergence of the stochastic approximation procedure θ<inf>n+1</inf… (More)
Is this relevant?
Highly Cited
2000
Highly Cited
2000
Stochastic approximation (SA) has long been applied for problems of minimizing loss functions or root finding with noisy input… (More)
  • table I
  • table II
Is this relevant?
Highly Cited
1998
Highly Cited
1998
  • L Eon Bottou
  • 1998
The convergence of online learning algorithms is analyzed using the tools of the stochastic approximation theory, and proved… (More)
Is this relevant?
Highly Cited
1998
Highly Cited
1998
The asymptotic behavior of a distributed, asynchronous stochastic approximation scheme is analyzed in terms of a limiting… (More)
Is this relevant?
Highly Cited
1994
Highly Cited
1994
We provide some general results on the convergence of a class of stochastic approximation algorithms and their parallel and… (More)
Is this relevant?
Highly Cited
1987
Highly Cited
1987
This paper shows how stochastic approximation (SA) can be used to construct maximum likelihood estimates of system parameters… (More)
  • figure 1
Is this relevant?