package nlopt-ocaml
OCaml bindings to the NLOpt optimization library
Install
Dune Dependency
Authors
Maintainers
Sources
release-0.5.1.tar.gz
sha256=3aba7bdd7154aacbeeb806da7f6dad2eacbae0eb32d518f52af96b4505b60928
md5=cae1d5923a12ca414aacc39bb7a8c360
doc/nlopt/Nlopt/index.html
Module Nlopt
OCaml interface to the NLopt optimization library
val direct : [ `Global ] algorithm
val direct_l : [ `Global | `Ineq ] algorithm
val direct_l_rand : [ `Global ] algorithm
val direct_noscal : [ `Global ] algorithm
val direct_l_noscal : [ `Global ] algorithm
val direct_l_rand_noscal : [ `Global ] algorithm
val orig_direct : [ `Global | `Ineq ] algorithm
val orig_direct_l : [ `Global ] algorithm
val stogo : [ `Global | `Grad ] algorithm
val stogo_rand : [ `Global | `Grad ] algorithm
val lbfgs_nocedal : [ `Local | `Grad ] algorithm
val lbfgs : [ `Local | `Grad ] algorithm
val praxis : [ `Local ] algorithm
val var1 : [ `Local | `Grad ] algorithm
val var2 : [ `Local | `Grad ] algorithm
val tnewton : [ `Local | `Grad ] algorithm
val tnewton_restart : [ `Local | `Grad ] algorithm
val tnewton_precond : [ `Local | `Grad ] algorithm
val tnewton_precond_restart : [ `Local | `Grad ] algorithm
val crs2_lm : [ `Global ] algorithm
val mma : [ `Local | `Grad | `Ineq ] algorithm
val cobyla : [ `Local | `Ineq | `Eq ] algorithm
val newuoa : [ `Local ] algorithm
val newuoa_bound : [ `Local ] algorithm
val neldermead : [ `Local ] algorithm
val sbplx : [ `Local ] algorithm
val bobyqa : [ `Local ] algorithm
val isres : [ `Global | `Ineq | `Eq ] algorithm
val auglag : [ `Subsidiary | `Ineq | `Eq ] algorithm
val auglag_eq : [ `Subsidiary | `Ineq | `Eq ] algorithm
val mlsl : [ `Subsidiary | `Global ] algorithm
val mlsl_lds : [ `Subsidiary | `Global ] algorithm
val slsqp : [ `Local | `Grad | `Ineq | `Eq ] algorithm
val set_min_objective :
'a t ->
(float array -> float array option -> float) ->
unit
val set_max_objective :
'a t ->
(float array -> float array option -> float) ->
unit
val optimize :
'a t ->
float array ->
[> `Success
| `Stopval_reached
| `Stopval_reached
| `Ftol_reached
| `Xtol_reached
| `Maxeval_reached
| `Maxtime_reached ]
* float array
* float
optimize opt x
performs the optimization using x
as an initial guess (it must be of size get_dimension opt). Returns a triple (result, xopt, fopt)
where xopt
is the optimzed value and fopt
is the function value at that optimum.
val get_dimension : 'a t -> int
Bound constraints
val set_lower_bounds : 'a t -> float array -> unit
val get_lower_bounds : 'a t -> float array
val set_upper_bounds : 'a t -> float array -> unit
val get_upper_bounds : 'a t -> float array
Nonlinear constraints
val add_inequality_constraint :
[> `Ineq ] t ->
(float array -> float array option -> float) ->
float ->
unit
val add_equality_constraint :
[> `Eq ] t ->
(float array -> float array option -> float) ->
float ->
unit
Stopping criteria
val set_stopval : 'a t -> float -> unit
val get_stopval : 'a t -> float
val set_ftol_rel : 'a t -> float -> unit
val get_ftol_rel : 'a t -> float
val set_ftol_abs : 'a t -> float -> unit
val get_ftol_abs : 'a t -> float
val set_xtol_rel : 'a t -> float -> unit
val get_xtol_rel : 'a t -> float
val set_xtol_abs : 'a t -> float array -> unit
val get_xtol_abs : 'a t -> float array
val set_maxeval : 'a t -> int -> unit
val get_maxeval : 'a t -> int
val set_maxtime : 'a t -> float -> unit
val get_maxtime : 'a t -> float
Local/subsidiary optimization algorithm
Initial step size
val set_initial_step : 'a t -> float array -> unit
val get_initial_step : 'a t -> float array -> float array
Stochastic population
val set_population : 'a t -> int -> unit
Vector storage for limited-memory quasi-Newton algorithms
val set_vector_storage : 'a t -> int -> unit
val get_vector_storage : 'a t -> int
Utility functions
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