This HTML5 document contains 64 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n16https://dx.doi.org/10.1002/
dctermshttp://purl.org/dc/terms/
yago-reshttp://yago-knowledge.org/resource/
n15http://www.jhuapl.edu/SPSA/PDF-SPSA/
dbohttp://dbpedia.org/ontology/
foafhttp://xmlns.com/foaf/0.1/
n19https://global.dbpedia.org/id/
yagohttp://dbpedia.org/class/yago/
dbthttp://dbpedia.org/resource/Template:
n8https://link.springer.com/book/10.1007/
n14http://www.jhuapl.edu/
rdfshttp://www.w3.org/2000/01/rdf-schema#
freebasehttp://rdf.freebase.com/ns/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
owlhttp://www.w3.org/2002/07/owl#
wikipedia-enhttp://en.wikipedia.org/wiki/
provhttp://www.w3.org/ns/prov#
dbchttp://dbpedia.org/resource/Category:
dbphttp://dbpedia.org/property/
xsdhhttp://www.w3.org/2001/XMLSchema#
wikidatahttp://www.wikidata.org/entity/
dbrhttp://dbpedia.org/resource/

Statements

Subject Item
dbr:List_of_numerical_analysis_topics
dbo:wikiPageWikiLink
dbr:Simultaneous_perturbation_stochastic_approximation
Subject Item
dbr:Mathematical_optimization
dbo:wikiPageWikiLink
dbr:Simultaneous_perturbation_stochastic_approximation
Subject Item
dbr:Rademacher_distribution
dbo:wikiPageWikiLink
dbr:Simultaneous_perturbation_stochastic_approximation
Subject Item
dbr:SPSA
dbo:wikiPageWikiLink
dbr:Simultaneous_perturbation_stochastic_approximation
dbo:wikiPageDisambiguates
dbr:Simultaneous_perturbation_stochastic_approximation
Subject Item
dbr:Simultaneous_perturbation_stochastic_approximation
rdf:type
yago:Event100029378 yago:Rule105846932 yago:WikicatOptimizationAlgorithmsAndMethods yago:PsychologicalFeature100023100 yago:YagoPermanentlyLocatedEntity yago:WikicatStochasticAlgorithms yago:Activity100407535 yago:Algorithm105847438 yago:Abstraction100002137 yago:Procedure101023820 yago:Act100030358
rdfs:label
Simultaneous perturbation stochastic approximation
rdfs:comment
Simultaneous perturbation stochastic approximation (SPSA) is an algorithmic method for optimizing systems with multiple unknown parameters. It is a type of stochastic approximation algorithm. As an optimization method, it is appropriately suited to large-scale population models, adaptive modeling, simulation optimization, and atmospheric modeling. Many examples are presented at the SPSA website http://www.jhuapl.edu/SPSA. A comprehensive book on the subject is Bhatnagar et al. (2013). An early paper on the subject is Spall (1987) and the foundational paper providing the key theory and justification is Spall (1992).
dcterms:subject
dbc:Randomized_algorithms dbc:Stochastic_optimization dbc:Optimization_algorithms_and_methods dbc:Numerical_climate_and_weather_models
dbo:wikiPageID
24464148
dbo:wikiPageRevisionID
1099031949
dbo:wikiPageWikiLink
dbr:Idea dbc:Numerical_climate_and_weather_models dbr:Convergence_(mathematics) dbr:Differentiable_function dbc:Randomized_algorithms dbc:Optimization_algorithms_and_methods dbr:Steepest dbr:Stochastic_gradient_descent dbr:Probability dbr:Symmetric dbc:Stochastic_optimization dbr:Stochastic_approximation dbr:Optimization dbr:Atmospheric_model dbr:Hypothesis dbr:Parameters dbr:Rademacher_distribution dbr:Approximation dbr:Algorithm dbr:Simulated_annealing dbr:Dimension dbr:Unbiased
dbo:wikiPageExternalLink
n8:978-1-4471-4285-0 n14:SPSA. n15:Spall_An_Overview.PDF n16:eej.20239
owl:sameAs
yago-res:Simultaneous_perturbation_stochastic_approximation n19:fbRW freebase:m.0803hkz wikidata:Q17084424
dbp:wikiPageUsesTemplate
dbt:Isbn
dbo:abstract
Simultaneous perturbation stochastic approximation (SPSA) is an algorithmic method for optimizing systems with multiple unknown parameters. It is a type of stochastic approximation algorithm. As an optimization method, it is appropriately suited to large-scale population models, adaptive modeling, simulation optimization, and atmospheric modeling. Many examples are presented at the SPSA website http://www.jhuapl.edu/SPSA. A comprehensive book on the subject is Bhatnagar et al. (2013). An early paper on the subject is Spall (1987) and the foundational paper providing the key theory and justification is Spall (1992). SPSA is a descent method capable of finding global minima, sharing this property with other methods as simulated annealing. Its main feature is the gradient approximation that requires only two measurements of the objective function, regardless of the dimension of the optimization problem. Recall that we want to find the optimal control with lossfunction : Both Finite Differences Stochastic Approximation (FDSA)and SPSA use the same iterative process: where represents the iterate, is the estimate of the gradient of the objective function evaluated at , and is a positive number sequence converging to 0. If is a p-dimensional vector, the component of the symmetric finite difference gradient estimator is: FD: 1 ≤i ≤p, where is the unit vector with a 1 in the place, and is a small positive number that decreases with n. With this method, 2p evaluations of J for each are needed. Clearly, when p is large, this estimator loses efficiency. Let now be a random perturbation vector. The component of the stochastic perturbation gradient estimator is: SP: Remark that FD perturbs only one direction at a time, while the SP estimator disturbs all directions at the same time (the numerator is identical in all p components). The number of loss function measurements needed in the SPSA method for each is always 2, independent of the dimension p. Thus, SPSA uses p times fewer function evaluations than FDSA, which makes it a lot more efficient. Simple experiments with p=2 showed that SPSA converges in the same number of iterations as FDSA. The latter follows approximately the steepest descent direction, behaving like the gradient method. On the other hand, SPSA, with the random search direction, does not follow exactly the gradient path. In average though, it tracks it nearly because the gradient approximation is an almost unbiasedestimator of the gradient, as shown in the following lemma.
prov:wasDerivedFrom
wikipedia-en:Simultaneous_perturbation_stochastic_approximation?oldid=1099031949&ns=0
dbo:wikiPageLength
8931
foaf:isPrimaryTopicOf
wikipedia-en:Simultaneous_perturbation_stochastic_approximation
Subject Item
dbr:Stochastic_approximation
dbo:wikiPageWikiLink
dbr:Simultaneous_perturbation_stochastic_approximation
Subject Item
dbr:Stochastic_optimization
dbo:wikiPageWikiLink
dbr:Simultaneous_perturbation_stochastic_approximation
Subject Item
dbr:Finite_Differences_Stochastic_Approximation
dbo:wikiPageWikiLink
dbr:Simultaneous_perturbation_stochastic_approximation
dbo:wikiPageRedirects
dbr:Simultaneous_perturbation_stochastic_approximation
Subject Item
wikipedia-en:Simultaneous_perturbation_stochastic_approximation
foaf:primaryTopic
dbr:Simultaneous_perturbation_stochastic_approximation