2021
DOI: 10.11591/ijece.v11i1.pp716-727
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Stochastic local search: a state-of-the-art review

Abstract: The main objective of this paper is to provide a state-of-the-art review, analyze and discuss stochastic local search techniques used for solving hard combinatorial problems. It begins with a short introduction, motivation and some basic notation on combinatorial problems, search paradigms and other relevant features of searching techniques as needed for background. In the following a brief overview of the stochastic local search methods along with an analysis of the state-of-the-art stochastic local search al… Show more

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Cited by 1 publication
(2 citation statements)
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References 54 publications
(63 reference statements)
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“…Mertikopoulos et al [91] show that by iteratively reducing the learning rate to 0, the SGD exhibits almost sure convergence, avoids spurious critical points such as saddle points (with probability 1), and stabilizes quickly at local minima. There are a number of variations of the SGD algorithm, which are described below [65].…”
Section: )mentioning
confidence: 99%
See 1 more Smart Citation
“…Mertikopoulos et al [91] show that by iteratively reducing the learning rate to 0, the SGD exhibits almost sure convergence, avoids spurious critical points such as saddle points (with probability 1), and stabilizes quickly at local minima. There are a number of variations of the SGD algorithm, which are described below [65].…”
Section: )mentioning
confidence: 99%
“…w. This can consume a large additional memory size if the number of parameters approaches the billions. In recent years a number of further optimizers were developed [65]:…”
Section: Variants Of Stochastic Gradient Descentmentioning
confidence: 99%