lbfgs

library lbfgs_fortran

module Lbfgs_fortran

Represent the memory space needed to solve a minimization problem. It is usually allocated automatically but it is possible to do it manually to, say, allocate it once only before a loop.
Abnormal(f, msg)
is raised if the routine terminated abnormally without being able to satisfy the termination conditions. In such an event, the variable x
(see F.min
) will contain the current best approximation found and f
is the value of the target function at x
. msg
is a message containing additional information (returned by the original FORTRAN code).
If the error message is not precise enough, it is recommended to turn printing on to understand what is the problem.
type print =
Control of the frequency at which information is outputted.
Holds informations on the current state of the computation that can help to decide whether to stop.
module F : sig ... end
Fortran Layout.
module C : sig ... end
C layout.
val work : ?corrections:int > int > work
work n
allocate the work space for a problem of size at most n
.
Accessing the state
val is_constrained : state > bool
Tells whether the problem is constrained.
val nintervals : state > int
The total number of intervals explored in the search of Cauchy points.
val nskipped_updates : state > int
The total number of skipped BFGS updates before the current iteration.
val iter : state > int
The number of current iteration.
val nupdates : state > int
The total number of BFGS updates prior the current iteration.
val nintervals_current : state > int
The number of intervals explored in the search of Cauchy point in the current iteration.
val neval : state > int
The total number of function and gradient evaluations.
val neval_current : state > int
The number of function value or gradient evaluations in the current iteration.
val previous_f : state > float
Returns f(x) in the previous iteration.
val norm_dir : state > float
2norm of the line search direction vector.
val eps : state > float
The machine precision epsmch generated by the code.
val time_cauchy : state > float
The accumulated time spent on searching for Cauchy points.
val time_subspace_min : state > float
The accumulated time spent on subspace minimization.
val time_line_search : state > float
The accumulated time spent on line search.
val slope : state > float
The slope of the line search function at the current point of line search.
val slope_init : state > float
The slope of the line search function at the starting point of the line search.
val normi_grad : state > float
The infinity norm of the projected gradient