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bfgs method造句

例句与造句

  1. The BFGS method is one of the most popular members of this class.
  2. R's optim general-purpose optimizer routine uses the BFGS method by using method = " BFGS ".
  3. Quasi-Newton methods also require more memory to operate ( see also the limited-memory L-BFGS method ).
  4. Due to its resulting linear memory requirement, the L-BFGS method is particularly well suited for optimization problems with a large number of variables.
  5. 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.
  6. It's difficult to find bfgs method in a sentence. 用bfgs method造句挺难的
  7. 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.
  8. 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.
  9. 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.
  10. 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.

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