Multidimensional minimization by the BFGS algorithm (GSL) More...
#include <mmin_bfgs2.h>
Public Member Functions | |
virtual int | iterate () |
Perform an iteration. | |
virtual const char * | type () |
Return string denoting type("mmin_bfgs2") | |
virtual int | allocate (size_t n) |
Allocate the memory. | |
virtual int | free () |
Free the allocated memory. | |
int | restart () |
Reset the minimizer to use the current point as a new starting point. | |
virtual int | set (vec_t &x, double u_step_size, double tol_u, func_t &ufunc) |
Set the function and initial guess. | |
virtual int | set_de (vec_t &x, double u_step_size, double tol_u, func_t &ufunc, dfunc_t &udfunc) |
Set the function, the gradient, and the initial guess. | |
virtual int | mmin (size_t nn, vec_t &xx, double &fmin, func_t &ufunc) |
Calculate the minimum min of func w.r.t the array x of size nn . | |
virtual int | mmin_de (size_t nn, vec_t &xx, double &fmin, func_t &ufunc, dfunc_t &udfunc) |
Calculate the minimum min of func w.r.t the array x of size nn . | |
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mmin_base (const mmin_base< func_t, func_t, vec_t > &mb) | |
Copy constructor. | |
int | set_verbose_stream (std::ostream &out, std::istream &in) |
Set streams for verbose I/O. More... | |
virtual int | mmin_de (size_t nvar, vec_t &x, double &fmin, func_t &func, func_t &dfunc) |
Calculate the minimum min of func w.r.t. the array x of size nvar with gradient dfunc . | |
int | print_iter (size_t nv, vec2_t &x, double y, int iter, double value, double limit, std::string comment) |
Print out iteration information. More... | |
const char * | type () |
Return string denoting type ("mmin_base") | |
mmin_base< func_t, func_t, vec_t > & | operator= (const mmin_base< func_t, func_t, vec_t > &mb) |
Copy constructor from operator=. | |
Public Attributes | |
double | step_size |
The size of the first trial step (default 0.01) | |
double | lmin_tol |
The tolerance for the 1-dimensional minimizer. | |
def_auto_grad_t | def_grad |
Default automatic gradient object. | |
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int | verbose |
Output control. | |
int | ntrial |
Maximum number of iterations. | |
double | tol_rel |
Function value tolerance. | |
double | tol_abs |
The independent variable tolerance. | |
int | last_ntrial |
The number of iterations for in the most recent minimization. | |
bool | err_nonconv |
If true, call the error handler if the routine does not "converge". | |
Protected Attributes | |
mmin_linmin_gsl | lm |
The line minimizer. | |
size_t | dim |
Memory size. | |
auto_grad_t * | agrad |
Automatic gradient object. | |
The original variables from the GSL state structure | |
int | iter |
double | step |
double | g0norm |
double | pnorm |
double | delta_f |
double | fp0 |
vec_t | x0 |
vec_t | g0 |
vec_t | p |
vec_t | dx0 |
vec_t | dg0 |
mmin_wrapper_gsl< func_t, vec_t, dfunc_t, auto_grad_t > | wrap |
double | rho |
double | sigma |
double | tau1 |
double | tau2 |
double | tau3 |
int | order |
Store the arguments to set() so we can use them for iterate() | |
vec_t * | st_x |
vec_t | st_dx |
vec_t | st_grad |
double | st_f |
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std::ostream * | outs |
Stream for verbose output. | |
std::istream * | ins |
Stream for verbose input. | |
Private Member Functions | |
mmin_bfgs2 (const mmin_bfgs2< func_t, vec_t, dfunc_t, auto_grad_t, def_auto_grad_t > &) | |
mmin_bfgs2< func_t, vec_t, dfunc_t, auto_grad_t, def_auto_grad_t > & | operator= (const mmin_bfgs2< func_t, vec_t, dfunc_t, auto_grad_t, def_auto_grad_t > &) |
The functions mmin() and mmin_de() min a given function until the gradient is smaller than the value of mmin::tol_rel (which defaults to ).
See an example for the usage of this class in Multidimensional minimizer example .
This class includes the optimizations from the GSL minimizer vector_bfgs2
.
Default template arguments
func_t
- multi_funct11vec_t
- boost::numeric::ublas::vector <double >dfunc_t
- mm_funct11auto_grad_t
- gradient<func_t, boost::numeric::ublas::vector <double > >def_auto_grad_t
- gradient_gsl<func_t, boost::numeric::ublas::vector < double > >ex_mmin
example with the initial guess of Definition at line 387 of file mmin_bfgs2.h.
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