bfgs method造句

• The BFGS method is one of the most popular members of this class.
• R's optim general-purpose optimizer routine uses the BFGS method by using method = " BFGS ".
• Quasi-Newton methods also require more memory to operate ( see also the limited-memory L-BFGS method ).
• Due to its resulting linear memory requirement, the L-BFGS method is particularly well suited for optimization problems with a large number of variables.
• They are typically determined by some sort of optimization procedure, e . g . maximum likelihood estimation, that finds values that best fit the observed data ( i . e . that give the most accurate predictions for the data already observed ), usually subject to L-BFGS method.
• Newton-based methods  Newton-Raphson Algorithm, Quasi-Newton methods ( e . g ., BFGS method )  tend to converge in fewer iterations, although each iteration typically requires more computation than a conjugate gradient iteration, as Newton-like methods require computing the Hessian ( matrix of second derivatives ) in addition to the gradient.
• The most common quasi-Newton algorithms are currently the SR1 formula ( for symmetric rank one ), the BHHH method, the widespread BFGS method ( suggested independently by Broyden, Fletcher, Goldfarb, and Shanno, in 1970 ), and its low-memory extension, L-BFGS . The Broyden's class is a linear combination of the DFP and BFGS methods.
• The most common quasi-Newton algorithms are currently the SR1 formula ( for symmetric rank one ), the BHHH method, the widespread BFGS method ( suggested independently by Broyden, Fletcher, Goldfarb, and Shanno, in 1970 ), and its low-memory extension, L-BFGS . The Broyden's class is a linear combination of the DFP and BFGS methods.
• In a quasi-Newton method, such as that due to Davidon, Fletcher and Powell or Broyden Fletcher Goldfarb Shanno ( BFGS method ) an estimate of the full Hessian, \ frac { \ partial ^ 2 S } { \ partial \ beta _ j \ partial \ beta _ k }, is built up numerically using first derivatives \ frac { \ partial r _ i } { \ partial \ beta _ j } only so that after " n " refinement cycles the method closely approximates to Newton's method in performance.