lbfgsb3c: Interfacing wrapper for the Nocedal - Morales LBFGSB3...
In lbfgsb3c: Limited Memory BFGS Minimizer with Bounds on Parameters with optim() 'C' Interface
Description
Usage
Arguments
Details
Value
Note
Author(s)
References
See Also
Examples
Description
Interfacing wrapper for the Nocedal - Morales LBFGSB3 (Fortran) limited memory BFGS solver.
Usage
1
2
3
4
5
6
7
8
9
10
11
lbfgsb3c(par, fn, gr = NULL, lower = -Inf, upper = Inf,
control = list(), ..., rho = NULL)
lbfgsb3(par, fn, gr = NULL, lower = -Inf, upper = Inf,
control = list(), ..., rho = NULL)
lbfgsb3f(par, fn, gr = NULL, lower = -Inf, upper = Inf,
control = list(), ..., rho = NULL)
lbfgsb3x(par, fn, gr = NULL, lower = -Inf, upper = Inf,
control = list(), ..., rho = NULL)
Arguments
par
A parameter vector which gives the initial guesses to
the parameters that will minimize fn. This can be
named, for example, we could use par=c(b1=1, b2=2.345,
b3=0.123)
fn
A function that evaluates the objective function to be
minimized. This can be a R function or a Rcpp function
pointer.
gr
If present, a function that evaluates the gradient
vector for the objective function at the given parameters
computing the elements of the sum of squares function at the
set of parameters start. This can be a R function or a
Rcpp function pointer.
lower
Lower bounds on the parameters. If a single number,
this will be applied to all parameters. Default -Inf.
upper
Upper bounds on the parameters. If a single number,
this will be applied to all parameters. Default Inf.
control
An optional list of control settings. See below in
details.
...
Any data needed for computation of the objective
function and gradient.
rho
An Environment to use for function evaluation. If
present the arguments in ... are ignored. Otherwise the
... are converted to an environment for evaluation.
Details
See the notes below for a general appreciation of this package.
The control list can contain:
trace If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method "L-BFGS-B" there are six levels of tracing. (To understand exactly what these do see the source code: higher levels give more detail.)
factr controls the convergence of the "L-BFGS-B" method. Convergence occurs when the reduction in the objective is within this factor of the machine tolerance. Default is 1e7, that is a tolerance of about 1e-8.
pgtol helps control the convergence of the "L-BFGS-B" method. It is a tolerance on the projected gradient in the current search direction. This defaults to zero, when the check is suppressed.
abstol helps control the convergence of the "L-BFGS-B" method. It is an absolute tolerance difference in x values. This defaults to zero, when the check is suppressed.
reltol helps control the convergence of the "L-BFGS-B" method. It is an relative tolerance difference in x values. This defaults to zero, when the check is suppressed.
lmm is an integer giving the number of BFGS updates retained in the "L-BFGS-B" method, It defaults to 5.
maxit maximum number of iterations.
iprint If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method "L-BFGS-B" there are six levels of tracing. (To understand exactly what these do see the source code: higher levels give more detail.)
info a boolean to indicate if more optimization information is captured and output in a $info list
Value
A list of the following items
par The best set of parameters found.
value The value of fn corresponding to par.
counts A two-element integer vector giving the number of calls to fn and gr respectively. This excludes any calls to fn to compute a finite-difference approximation to the gradient.
convergence An integer code. 0 indicates successful completion
Note
This package is a wrapper to the Fortran code released by Nocedal and Morales.
This poses several difficulties for an R package. While the .Fortran()
tool exists for the interfacing, we must be very careful to align the arguments
with those of the Fortran subroutine, especially in type and storage.
A more annoying task for interfacing the Fortran code is that Fortran WRITE or
PRINT statements must all be replaced with calls to special R-friendly output
routines. Unfortunately, the Fortran is full of output statements. Worse, we may
wish to be able to suppress such output, and there are thus many modifications
to be made. This means that an update of the original code cannot be simply
plugged into the R package src directory.
Finally, and likely because L-BFGS-B has a long history, the Fortran code is far
from well-structured. For example, the number of function and gradient evaluations
used is returned as the 34'th element of an integer vector. There does not appear
to be an easy way to stop the program after some maximum number of such evaluations
have been performed.
On the other hand, the version of L-BFGS-B in optim() is a C translation
of a now-lost Fortran code. It does not implement the improvements Nocedal and
Morales published in 2011. Hence, despite its deficiencies, this wrapper has been
prepared.
In addition to the above reasons for the original lbfgsb3 package,
this additional package allows C calling of L-BFGS-B 3.0 by a
program as well as adjustments to the tolerances that were not
present in the original CRAN package. Also adjustments were made
to have outputs conform with R's optim routine.
Author(s)
Matthew Fidler (move to C and add more options for adjustments),
John C Nash <nashjc@uottawa.ca> (of the wrapper and edits to Fortran code to allow R output)
Ciyou Zhu, Richard Byrd, Jorge Nocedal, Jose Luis Morales (original Fortran packages)
References
Morales, J. L.; Nocedal, J. (2011). "Remark on 'algorithm 778: L-BFGS-B:
Fortran subroutines for large-scale bound constrained optimization' ".
ACM Transactions on Mathematical Software 38: 1.
Byrd, R. H.; Lu, P.; Nocedal, J.; Zhu, C. (1995). "A Limited Memory Algorithm
for Bound Constrained Optimization". SIAM J. Sci. Comput. 16 (5): 1190-1208.
Zhu, C.; Byrd, Richard H.; Lu, Peihuang; Nocedal, Jorge (1997). "L-BFGS-B:
Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained
optimization". ACM Transactions on Mathematical Software 23 (4): 550-560.
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
# Rosenbrock's banana function
n=3; p=100
fr = function(x)
{
f=1.0
for(i in 2:n) {
f=f+p*(x[i]-x[i-1]**2)**2+(1.0-x[i])**2
}
f
}
grr = function(x)
{
g = double(n)
g[1]=-4.0*p*(x[2]-x[1]**2)*x[1]
if(n>2) {
for(i in 2:(n-1)) {
g[i]=2.0*p*(x[i]-x[i-1]**2)-4.0*p*(x[i+1]-x[i]**2)*x[i]-2.0*(1.0-x[i])
}
}
g[n]=2.0*p*(x[n]-x[n-1]**2)-2.0*(1.0-x[n])
g
}
x = c(a=1.02, b=1.02, c=1.02)
(opc <- lbfgsb3c(x,fr, grr))
(op <- lbfgsb3(x,fr, grr, control=list(trace=1)))
(opx <- lbfgsb3x(x,fr, grr))
(opf <- lbfgsb3f(x,fr, grr))
Example output
$par
a b c
1.000041 1.000083 1.000169
$grad
a b c
-0.0002752393 -0.0006637576 0.0008213517
$value
[1] 1
$counts
[1] 21 21
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
At iteration 0 f=1.084032
At iteration 2 f=144.115766
At iteration 3 f=1.003318
At iteration 4 f=1.000877
At iteration 5 f=1.000876
At iteration 6 f=1.000866
At iteration 7 f=1.000863
At iteration 8 f=1.000853
At iteration 9 f=1.000707
At iteration 10 f=1.000295
At iteration 11 f=1.080517
At iteration 12 f=1.000158
At iteration 13 f=1.000142
At iteration 14 f=1.000088
At iteration 15 f=1.000277
At iteration 16 f=1.000033
At iteration 17 f=1.000001
At iteration 18 f=1.000000
At iteration 19 f=1.000000
At iteration 20 f=1.000000
At iteration 21 f=1.000000
$par
a b c
1.000041 1.000083 1.000169
$grad
a b c
-0.0002752393 -0.0006637576 0.0008213517
$value
[1] 1
$counts
[1] 21 21
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$info
$info$task
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$info$itask
[1] 8
$info$lsave
[1] FALSE FALSE FALSE FALSE
$info$icsave
[1] 6
$info$dsave
[1] 1.170212e+03 1.000000e+00 2.220446e-09 1.634496e-06 2.220446e-16
[6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
[11] -1.103432e-09 1.000000e+10 8.213517e-04 1.000000e+00 -1.411118e-09
[16] 2.671576e-12 -1.411118e-09 -1.411118e-12 -1.411118e-09 -1.411118e-09
[21] 1.000000e+00 1.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00
[26] 0.000000e+00 5.000000e+00 1.000000e+10 2.000000e+10
$info$isave
[1] 3 1 4 1 4 7 8 9 10 14 18 21 24 27 30 33 0 0 0 0 0 1 0 0 0
[26] 0 1 1 1 14 13 0 0 21 0 1 0 3 0 4 0 0 0 1
$par
a b c
1.000041 1.000083 1.000169
$grad
a b c
-0.0002752393 -0.0006637576 0.0008213517
$value
[1] 1
$counts
[1] 21 21
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$par
a b c
1.000041 1.000083 1.000169
$grad
a b c
-0.0002752393 -0.0006637576 0.0008213517
$value
[1] 1
$counts
[1] 21 21
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$info
$info$task
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$info$itask
[1] 8
$info$lsave
[1] FALSE FALSE FALSE FALSE
$info$icsave
[1] 6
$info$dsave
[1] 1.170212e+03 1.000000e+00 2.220446e-09 1.634496e-06 2.220446e-16
[6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
[11] -1.103432e-09 1.000000e+10 8.213517e-04 1.000000e+00 -1.411118e-09
[16] 2.671576e-12 -1.411118e-09 -1.411118e-12 -1.411118e-09 -1.411118e-09
[21] 1.000000e+00 1.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00
[26] 0.000000e+00 5.000000e+00 1.000000e+10 2.000000e+10
$info$isave
[1] 3 1 4 1 4 7 8 9 10 14 18 21 24 27 30 33 0 0 0 0 0 1 0 0 0
[26] 0 1 1 1 14 13 0 0 21 0 1 0 3 0 4 0 0 0 1
lbfgsb3c documentation built on May 2, 2019, 4:59 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Note Author(s) References See Also Examples
Description
Interfacing wrapper for the Nocedal - Morales LBFGSB3 (Fortran) limited memory BFGS solver.
Usage
1 2 3 4 5 6 7 8 9 10 11 | lbfgsb3c(par, fn, gr = NULL, lower = -Inf, upper = Inf,
control = list(), ..., rho = NULL)
lbfgsb3(par, fn, gr = NULL, lower = -Inf, upper = Inf,
control = list(), ..., rho = NULL)
lbfgsb3f(par, fn, gr = NULL, lower = -Inf, upper = Inf,
control = list(), ..., rho = NULL)
lbfgsb3x(par, fn, gr = NULL, lower = -Inf, upper = Inf,
control = list(), ..., rho = NULL)
|
Arguments
par |
A parameter vector which gives the initial guesses to
the parameters that will minimize |
fn |
A function that evaluates the objective function to be minimized. This can be a R function or a Rcpp function pointer. |
gr |
If present, a function that evaluates the gradient
vector for the objective function at the given parameters
computing the elements of the sum of squares function at the
set of parameters |
lower |
Lower bounds on the parameters. If a single number, this will be applied to all parameters. Default -Inf. |
upper |
Upper bounds on the parameters. If a single number, this will be applied to all parameters. Default Inf. |
control |
An optional list of control settings. See below in details. |
... |
Any data needed for computation of the objective function and gradient. |
rho |
An Environment to use for function evaluation. If present the arguments in ... are ignored. Otherwise the ... are converted to an environment for evaluation. |
Details
See the notes below for a general appreciation of this package.
The control list can contain:
trace If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method "L-BFGS-B" there are six levels of tracing. (To understand exactly what these do see the source code: higher levels give more detail.)
factr controls the convergence of the "L-BFGS-B" method. Convergence occurs when the reduction in the objective is within this factor of the machine tolerance. Default is 1e7, that is a tolerance of about 1e-8.
pgtol helps control the convergence of the "L-BFGS-B" method. It is a tolerance on the projected gradient in the current search direction. This defaults to zero, when the check is suppressed.
abstol helps control the convergence of the "L-BFGS-B" method. It is an absolute tolerance difference in x values. This defaults to zero, when the check is suppressed.
reltol helps control the convergence of the "L-BFGS-B" method. It is an relative tolerance difference in x values. This defaults to zero, when the check is suppressed.
lmm is an integer giving the number of BFGS updates retained in the "L-BFGS-B" method, It defaults to 5.
maxit maximum number of iterations.
iprint If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method "L-BFGS-B" there are six levels of tracing. (To understand exactly what these do see the source code: higher levels give more detail.)
info a boolean to indicate if more optimization information is captured and output in a $info list
Value
A list of the following items
par The best set of parameters found.
value The value of fn corresponding to par.
counts A two-element integer vector giving the number of calls to fn and gr respectively. This excludes any calls to fn to compute a finite-difference approximation to the gradient.
convergence An integer code. 0 indicates successful completion
Note
This package is a wrapper to the Fortran code released by Nocedal and Morales.
This poses several difficulties for an R package. While the .Fortran()
tool exists for the interfacing, we must be very careful to align the arguments
with those of the Fortran subroutine, especially in type and storage.
A more annoying task for interfacing the Fortran code is that Fortran WRITE or
PRINT statements must all be replaced with calls to special R-friendly output
routines. Unfortunately, the Fortran is full of output statements. Worse, we may
wish to be able to suppress such output, and there are thus many modifications
to be made. This means that an update of the original code cannot be simply
plugged into the R package src directory.
Finally, and likely because L-BFGS-B has a long history, the Fortran code is far from well-structured. For example, the number of function and gradient evaluations used is returned as the 34'th element of an integer vector. There does not appear to be an easy way to stop the program after some maximum number of such evaluations have been performed.
On the other hand, the version of L-BFGS-B in optim() is a C translation
of a now-lost Fortran code. It does not implement the improvements Nocedal and
Morales published in 2011. Hence, despite its deficiencies, this wrapper has been
prepared.
In addition to the above reasons for the original lbfgsb3 package, this additional package allows C calling of L-BFGS-B 3.0 by a program as well as adjustments to the tolerances that were not present in the original CRAN package. Also adjustments were made to have outputs conform with R's optim routine.
Author(s)
Matthew Fidler (move to C and add more options for adjustments), John C Nash <nashjc@uottawa.ca> (of the wrapper and edits to Fortran code to allow R output) Ciyou Zhu, Richard Byrd, Jorge Nocedal, Jose Luis Morales (original Fortran packages)
References
Morales, J. L.; Nocedal, J. (2011). "Remark on 'algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization' ". ACM Transactions on Mathematical Software 38: 1.
Byrd, R. H.; Lu, P.; Nocedal, J.; Zhu, C. (1995). "A Limited Memory Algorithm for Bound Constrained Optimization". SIAM J. Sci. Comput. 16 (5): 1190-1208.
Zhu, C.; Byrd, Richard H.; Lu, Peihuang; Nocedal, Jorge (1997). "L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization". ACM Transactions on Mathematical Software 23 (4): 550-560.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # Rosenbrock's banana function
n=3; p=100
fr = function(x)
{
f=1.0
for(i in 2:n) {
f=f+p*(x[i]-x[i-1]**2)**2+(1.0-x[i])**2
}
f
}
grr = function(x)
{
g = double(n)
g[1]=-4.0*p*(x[2]-x[1]**2)*x[1]
if(n>2) {
for(i in 2:(n-1)) {
g[i]=2.0*p*(x[i]-x[i-1]**2)-4.0*p*(x[i+1]-x[i]**2)*x[i]-2.0*(1.0-x[i])
}
}
g[n]=2.0*p*(x[n]-x[n-1]**2)-2.0*(1.0-x[n])
g
}
x = c(a=1.02, b=1.02, c=1.02)
(opc <- lbfgsb3c(x,fr, grr))
(op <- lbfgsb3(x,fr, grr, control=list(trace=1)))
(opx <- lbfgsb3x(x,fr, grr))
(opf <- lbfgsb3f(x,fr, grr))
|
Example output
$par
a b c
1.000041 1.000083 1.000169
$grad
a b c
-0.0002752393 -0.0006637576 0.0008213517
$value
[1] 1
$counts
[1] 21 21
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
At iteration 0 f=1.084032
At iteration 2 f=144.115766
At iteration 3 f=1.003318
At iteration 4 f=1.000877
At iteration 5 f=1.000876
At iteration 6 f=1.000866
At iteration 7 f=1.000863
At iteration 8 f=1.000853
At iteration 9 f=1.000707
At iteration 10 f=1.000295
At iteration 11 f=1.080517
At iteration 12 f=1.000158
At iteration 13 f=1.000142
At iteration 14 f=1.000088
At iteration 15 f=1.000277
At iteration 16 f=1.000033
At iteration 17 f=1.000001
At iteration 18 f=1.000000
At iteration 19 f=1.000000
At iteration 20 f=1.000000
At iteration 21 f=1.000000
$par
a b c
1.000041 1.000083 1.000169
$grad
a b c
-0.0002752393 -0.0006637576 0.0008213517
$value
[1] 1
$counts
[1] 21 21
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$info
$info$task
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$info$itask
[1] 8
$info$lsave
[1] FALSE FALSE FALSE FALSE
$info$icsave
[1] 6
$info$dsave
[1] 1.170212e+03 1.000000e+00 2.220446e-09 1.634496e-06 2.220446e-16
[6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
[11] -1.103432e-09 1.000000e+10 8.213517e-04 1.000000e+00 -1.411118e-09
[16] 2.671576e-12 -1.411118e-09 -1.411118e-12 -1.411118e-09 -1.411118e-09
[21] 1.000000e+00 1.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00
[26] 0.000000e+00 5.000000e+00 1.000000e+10 2.000000e+10
$info$isave
[1] 3 1 4 1 4 7 8 9 10 14 18 21 24 27 30 33 0 0 0 0 0 1 0 0 0
[26] 0 1 1 1 14 13 0 0 21 0 1 0 3 0 4 0 0 0 1
$par
a b c
1.000041 1.000083 1.000169
$grad
a b c
-0.0002752393 -0.0006637576 0.0008213517
$value
[1] 1
$counts
[1] 21 21
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$par
a b c
1.000041 1.000083 1.000169
$grad
a b c
-0.0002752393 -0.0006637576 0.0008213517
$value
[1] 1
$counts
[1] 21 21
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$info
$info$task
[1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
$info$itask
[1] 8
$info$lsave
[1] FALSE FALSE FALSE FALSE
$info$icsave
[1] 6
$info$dsave
[1] 1.170212e+03 1.000000e+00 2.220446e-09 1.634496e-06 2.220446e-16
[6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
[11] -1.103432e-09 1.000000e+10 8.213517e-04 1.000000e+00 -1.411118e-09
[16] 2.671576e-12 -1.411118e-09 -1.411118e-12 -1.411118e-09 -1.411118e-09
[21] 1.000000e+00 1.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00
[26] 0.000000e+00 5.000000e+00 1.000000e+10 2.000000e+10
$info$isave
[1] 3 1 4 1 4 7 8 9 10 14 18 21 24 27 30 33 0 0 0 0 0 1 0 0 0
[26] 0 1 1 1 14 13 0 0 21 0 1 0 3 0 4 0 0 0 1
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.