Conjugate Gradient Methods less Dependent on Conjugacy
Copyright policy )Conjugate Gradient Methods less Dependent on Conjugacy
This paper reviews some recent conjugate gradient (CG) methods and outlines some promising directions for further developments. Two charts summarize the methods and indicate the main line of development, one for the methods utilizing a variable metric and limited memory and the other for methods based upon successive affine reduction.
variable metric, Nonlinear programming, Linear programming, large-scale minimization, high-dimensional minimization, conjugate gradient, successive affine reduction, Numerical methods based on necessary conditions
variable metric, Nonlinear programming, Linear programming, large-scale minimization, high-dimensional minimization, conjugate gradient, successive affine reduction, Numerical methods based on necessary conditions
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).19 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!