About: Limited-memory BFGS

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Limited-memory BFGS Goto Sponge NotDistinct Permalink

An Entity of Type : yago:Rule105846932, within Data Space : dbpedia.org associated with source document(s)
QRcode icon

http://dbpedia.org/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FLimited-memory_BFGS

Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory. It is a popular algorithm for parameter estimation in machine learning. The algorithm's target problem is to minimize over unconstrained values of the real-vector where is a differentiable scalar function.

Attributes	Values
rdf:type	software yago:WikicatOptimizationAlgorithmsAndMethods yago:Abstraction100002137 yago:Act100030358 yago:Activity100407535 yago:Algorithm105847438 yago:Event100029378 yago:Procedure101023820 yago:PsychologicalFeature100023100 yago:YagoPermanentlyLocatedEntity yago:Rule105846932
rdfs:label	L-BFGS (es) Limited-memory BFGS (en) L-BFGS (ko) L-BFGS法 (ja)
rdfs:comment	L-BFGS y L-BFGS-B son dos métodos de optimización de funciones con un gran número de parámetros o de una gran complejidad. Se trata de un método que hace un uso limitado de la memoria (usa mucha menos memoria que otros algoritmos para el mismo problema); L-BFGS viene de de memoria limitada. Permite obtener el mínimo de una función. Únicamente necesita la función y su gradiente, pero no la matriz Hessiana. L-BFGS, desarrollado por es capaz de resolver funciones sin restricciones, mientras que la variante L-BFGS-B ( y Richard Byrd) puede resolver funciones con restricciones simples (del tipo , siendo la variable i-ésima y y los límites inferior y superior de esa variable) en sus parámetros. Si las restricciones son complejas otros métodos, como , deben ser usados. (es) Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory. It is a popular algorithm for parameter estimation in machine learning. The algorithm's target problem is to minimize over unconstrained values of the real-vector where is a differentiable scalar function. (en) L-BFGS (혹은 Limited-memory BFGS, LM-BFGS) 준-뉴턴 방식 (quasi-Newton methods)의 최적화 알고리즘이다. 제한된 컴퓨터 메모리를 이용하여 기존 BFGS (Broyden–Fletcher–Goldfarb–Shanno algorithm) 알고리즘을 속도면에서 개선한 알고리즘이다. (결과는 근사값). 본 알고리즘은 Adam과 함께 머신 러닝에 있어서 널리 사용되는 파라메터 추정 알고리즘이다. 본 알고리즘의 목적함수는 의 최소값을 찾는 것이며, 여기서, 는 unconstrained value의 real-vector이고, 는 미분가능한 스칼라 함수이다. (문서는 Wikipedia의 영어판에 게재된 Limited-memory BFGS 문서의 한국어 번역이며, 필자의 판단으로 일부 내용은 의역하였음.) (ko)
dcterms:subject	Optimization algorithms and methods
Wikipage page ID	6142533 (xsd:integer)
Wikipage revision ID	1122421735 (xsd:integer)
Link from a Wikipage to another Wikipage	SciPy Multinomial logistic regression Algorithm Conditional random field Online machine learning Wolfe conditions Quasi-Newton method Optimization (mathematics) Constraint (mathematics) Convex function Machine learning Sign (mathematics) Smooth function Computer memory Optimization algorithms and methods ALGLIB Fortran Fortran 77 Stochastic gradient descent Regularization (mathematics) Active set Backtracking line search Hessian matrix Taxicab geometry Differentiable function Sparse matrix Broyden–Fletcher–Goldfarb–Shanno algorithm R (programming language) Loss function
Link from a Wikipage to an external page	http://aria42.com/blog/2014/12/understanding-lbfgs%7Cfirst1=Aria%7Clast1=Haghighi%7Ctitle=Numerical http://www.ece.northwestern.edu/~nocedal/PSfiles/limited-memory.ps.gz%7Ctitle= https://www.google.com/books/edition/Conjugate_Gradient_Algorithms_in_Nonconv/RhRkaDPmwVoC%3Fhl=en&gbpv=1&pg=PA159
sameAs	Limited-memory BFGS Limited-memory BFGS Limited-memory BFGS Limited-memory BFGS Limited-memory BFGS Limited-memory BFGS Limited-memory BFGS
dbp:wikiPageUsesTemplate	dbt:Cite_book dbt:Cite_journal dbt:Cite_web dbt:Math dbt:Mvar dbt:Reflist dbt:Short_description dbt:Optimization_algorithms
has abstract	L-BFGS y L-BFGS-B son dos métodos de optimización de funciones con un gran número de parámetros o de una gran complejidad. Se trata de un método que hace un uso limitado de la memoria (usa mucha menos memoria que otros algoritmos para el mismo problema); L-BFGS viene de de memoria limitada. Permite obtener el mínimo de una función. Únicamente necesita la función y su gradiente, pero no la matriz Hessiana. L-BFGS, desarrollado por es capaz de resolver funciones sin restricciones, mientras que la variante L-BFGS-B ( y Richard Byrd) puede resolver funciones con restricciones simples (del tipo , siendo la variable i-ésima y y los límites inferior y superior de esa variable) en sus parámetros. Si las restricciones son complejas otros métodos, como , deben ser usados. Para cada iteración el algoritmo busca una aproximación de la matriz Hessiana, concretamente de su inversa. Si la función tiene N variables, la matriz Hessiana tiene elementos. Si N es grande, el tiempo necesario para calcular toda la matriz de forma exacta puede ser prohibitivo. Es por esto que se busca una aproximación. (es) Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory. It is a popular algorithm for parameter estimation in machine learning. The algorithm's target problem is to minimize over unconstrained values of the real-vector where is a differentiable scalar function. Like the original BFGS, L-BFGS uses an estimate of the inverse Hessian matrix to steer its search through variable space, but where BFGS stores a dense approximation to the inverse Hessian (n being the number of variables in the problem), L-BFGS stores only a few vectors that represent the approximation implicitly. Due to its resulting linear memory requirement, the L-BFGS method is particularly well suited for optimization problems with many variables. Instead of the inverse Hessian Hk, L-BFGS maintains a history of the past m updates of the position x and gradient ∇f(x), where generally the history size m can be small (often ). These updates are used to implicitly do operations requiring the Hk-vector product. (en) L-BFGS (혹은 Limited-memory BFGS, LM-BFGS) 준-뉴턴 방식 (quasi-Newton methods)의 최적화 알고리즘이다. 제한된 컴퓨터 메모리를 이용하여 기존 BFGS (Broyden–Fletcher–Goldfarb–Shanno algorithm) 알고리즘을 속도면에서 개선한 알고리즘이다. (결과는 근사값). 본 알고리즘은 Adam과 함께 머신 러닝에 있어서 널리 사용되는 파라메터 추정 알고리즘이다. 본 알고리즘의 목적함수는 의 최소값을 찾는 것이며, 여기서, 는 unconstrained value의 real-vector이고, 는 미분가능한 스칼라 함수이다. 원래의 BFGS와 같이, L-BFGS 알고리즘은 variable space의 검색을 조정하는 추정된 inverse Hessian matrix를 이용한다. 하지만 BFGS는 inverse Hessian에서 추정된 전체 을 이용하고, L-BFGS는 단지 몇개의 벡터만을 저장한다. 비록 일부의 벡터이지만 이런 일부의 벡터 역시 암시적으로 대표성을 지니고 있다. (주로 홀수개의 벡터를 지정하고 10 이하. 문헌에서 봤는데 이유는 기억이 가물가물.. ) 여기서 n은 문제에서 변수의 갯수를 의미한다. 선형 메모리의 요구사항으로 인해, L-BFGS는 많은 변수를 가진 최적화 문제에 적합하다고 알려져 있다. inverse Hessian Hk 대신에 L-BFGS는 과거 m update의 기록과 gradient ∇f(x) 이용한다. 일반적으로 과거 기록인 m 의 크기는 주로 10 이하를 선정한다. (문서는 Wikipedia의 영어판에 게재된 Limited-memory BFGS 문서의 한국어 번역이며, 필자의 판단으로 일부 내용은 의역하였음.) (ko)
gold:hypernym	Algorithm
prov:wasDerivedFrom	wikipedia-en:Limited-memory_BFGS?oldid=1122421735&ns=0
page length (characters) of wiki page	15252 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Limited-memory_BFGS
is Link from a Wikipage to another Wikipage of	Bregman method Davidon–Fletcher–Powell formula List of numerical analysis topics Quasi-Newton method Gradient descent Apache Spark Stan (software) Jorge Nocedal Stochastic gradient descent L-BFGS L-BFGS-B: Optimization subject to simple bounds LBFGS LM-BFGS Orthant-wise limited-memory quasi-Newton OWL-QN
is Wikipage redirect of	L-BFGS L-BFGS-B: Optimization subject to simple bounds LBFGS LM-BFGS Orthant-wise limited-memory quasi-Newton OWL-QN
is foaf:primaryTopic of	wikipedia-en:Limited-memory_BFGS

Faceted Search & Find service v1.17_git139 as of Feb 29 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3330 as of Mar 19 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (62 GB total memory, 54 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software