Difference between revisions of "Lbfgs.m"

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{{DISPLAYTITLE:lbfgs.m}} __NOTOC__
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{{DISPLAYTITLE:lbfgs.m}}
 
 
 
Calculates an approximation to the Newton-Raphson search direction using past gradients to build a serviceable substitute to a Hessian. The Hessian matrix is never explicitly formed or inverted. This function is the implementation from section 4 of http://dx.doi.org/10.1090/S0025-5718-1980-0572855-7
 
Calculates an approximation to the Newton-Raphson search direction using past gradients to build a serviceable substitute to a Hessian. The Hessian matrix is never explicitly formed or inverted. This function is the implementation from section 4 of http://dx.doi.org/10.1090/S0025-5718-1980-0572855-7
  
 
==Syntax==
 
==Syntax==
  
direction=lbfgs(dx_hist,dg_hist,g)
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    direction=lbfgs(dx_hist,dg_hist,g)
  
 
==Arguments==
 
==Arguments==
 
+
           
dx_hist        - history of x increments, a stack
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    dx_hist        - history of x increments, a stack  
 
                       of column vectors, from the latest
 
                       of column vectors, from the latest
 
                       to the earliest
 
                       to the earliest
 
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     dg_hist        - history of gradient increments,
 
     dg_hist        - history of gradient increments,
                       a stack of column vectors, from
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                       a stack of column vectors, from  
 
                       the latest to the earliest
 
                       the latest to the earliest
 
+
 
 
     g              - current gradient
 
     g              - current gradient
  
Returns:
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==Output==
  
     direction      - LBFGS approximation to the
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     direction      - LBFGS approximation to the  
                       maximisation step vector
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                       search direction
 
 
david.goodwin@inano.au.dk
 
ilya.kuprov@weizmann.ac.il
 
  
 
==Notes==
 
==Notes==
 
 
The L-BFGS algorithm is the default of [[fminnewton.m]], and is a good mix of computational efficiency and fast convergence.
 
The L-BFGS algorithm is the default of [[fminnewton.m]], and is a good mix of computational efficiency and fast convergence.
  
 
==See also==
 
==See also==
 
 
[[fminnewton.m]], [[hessreg.m]], [[linesearch.m]]
 
[[fminnewton.m]], [[hessreg.m]], [[linesearch.m]]
  
  
 
''Version 2.2, authors: [[Ilya Kuprov]], [[David Goodwin]]''
 
''Version 2.2, authors: [[Ilya Kuprov]], [[David Goodwin]]''
 
==Output==
 
 
direction      - LBFGS approximation to the
 
                      search direction
 

Revision as of 15:49, 5 April 2026

Calculates an approximation to the Newton-Raphson search direction using past gradients to build a serviceable substitute to a Hessian. The Hessian matrix is never explicitly formed or inverted. This function is the implementation from section 4 of http://dx.doi.org/10.1090/S0025-5718-1980-0572855-7

Syntax

    direction=lbfgs(dx_hist,dg_hist,g)

Arguments

   dx_hist         - history of x increments, a stack 
                     of column vectors, from the latest
                     to the earliest

   dg_hist         - history of gradient increments,
                     a stack of column vectors, from 
                     the latest to the earliest
 
   g               - current gradient

Output

   direction       - LBFGS approximation to the 
                     search direction

Notes

The L-BFGS algorithm is the default of fminnewton.m, and is a good mix of computational efficiency and fast convergence.

See also

fminnewton.m, hessreg.m, linesearch.m


Version 2.2, authors: Ilya Kuprov, David Goodwin