Stochastic optimization

Known as: Stochastic optimisation, Stochastic search 
Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables… (More)
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Highly Cited
2015
Highly Cited
2015
We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive… (More)
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Highly Cited
2011
Highly Cited
2011
We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient… (More)
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Highly Cited
2011
Highly Cited
2011
In this paper, we study two problems which often occur in various applications arising in wireless sensor networks. These are the… (More)
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Highly Cited
2011
Highly Cited
2011
We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on… (More)
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Highly Cited
2010
Highly Cited
2010
We present a new family of subgradient methods that dynamica lly incorporate knowledge of the geometry of the data observed in… (More)
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Highly Cited
2009
Highly Cited
2009
In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic… (More)
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Highly Cited
2004
Highly Cited
2004
Stochastic search and optimization techniques are used in a vast number of areas, including aerospace, medicine, transportation… (More)
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Highly Cited
2004
Highly Cited
2004
The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range… (More)
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Highly Cited
1996
Highly Cited
1996
An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant… (More)
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Highly Cited
1991
Highly Cited
1991
This paper presents a methodology for the solution of multistage stochastic optimization problems, based on the approximation of… (More)
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