Skip to search formSkip to main contentSkip to account menu

Limited-memory BFGS

Known as: L-BFGS-B: Optimization subject to simple bounds, L-BFGS, LM-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… 
Wikipedia (opens in a new tab)

Related topics

Related topics

25 relations
AlgorithmApache SparkBacktracking line searchC++11

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
Accelerating SVRG via second-order information
We consider the problem of minimizing an objective function that is a sum of convex functions. For large sums, batch methods… 
2013
2013
Learning Semantic Representations for the Phrase Translation Model
This paper presents a novel semantic-based phrase translation model. A pair of source and target phrases are projected into… 
2012
2012
Solving Limited-Memory BFGS Systems with Generalized Diagonal Updates
In this paper, we investigate a formula to solve systems of the form (Bk + D)x = y, where Bk comes from a limited-memory BFGS… 
2008
2008
An active set limited memory BFGS algorithm for large-scale bound constrained optimization
An active set limited memory BFGS algorithm for large-scale bound constrained optimization is proposed. The active sets are… 
2006
2006
Preconditioning the limited-memory bfgs algorithm
For large scale unconstrained optimization, truncated Newton methods and limited memory quasi-Newton methods are most effective… 
Highly Cited
2004
Highly Cited
2004
Automatic History Matching of Geologic Facies
The truncated plurigaussian method for modeling geologic facies is appealing not only for the wide variety of textures and shapes… 
1999
1999
Tracking MR tag surfaces using a spatiotemporal filter and interpolator
Magnetic resonance (MR) tagging is a method for visualizing cardiac motion in MR images by inducing artificial, high‐contrast… 
1999
1999
C++ classes for linking optimization with complex simulations
The object-oriented programming paradigm can be used to overcome the incompatibilities between off-the-shelf optimization… 
1996
1996
A comparison of nonlinear optimization methods for supervised learning in multilayer feedforward neural networks
1993
1993
Algorithms and Bounds for Shortest Paths and Diameter in Faulty Hypercubes
In an n-dimensional hypercube Qn, with the fault set mod F mod >2/sub n-2/, assuming Sand D are not isolated, it is shown that… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)