This chapter describes routines for finding minima of arbitrary multidimensional functions. The library provides low level components for a variety of iterative minimizers and convergence tests. These can be combined by the user to achieve the desired solution, while providing full access to the intermediate steps of the algorithms. Each class of methods uses the same framework, so that you can switch between minimizers at runtime without needing to recompile your program. Each instance of a minimizer keeps track of its own state, allowing the minimizers to be used in multi-threaded programs.
Contents:
The problem of multidimensional minimization requires finding a point x such that the scalar function, takes a value which is lower than at any neighboring point. For smooth functions the gradient g = \nabla f vanishes at the minimum. In general there are no bracketing methods available for the minimization of n-dimensional functions. The algorithms proceed from an initial guess using a search algorithm which attempts to move in a downhill direction.
Algorithms making use of the gradient of the function perform a one-dimensional line minimisation along this direction until the lowest point is found to a suitable tolerance. The search direction is then updated with local information from the function and its derivatives, and the whole process repeated until the true n-dimensional minimum is found.
The Nelder-Mead Simplex algorithm applies a different strategy. It maintains n+1 trial parameter vectors as the vertices of a n-dimensional simplex. In each iteration step it tries to improve the worst vertex by a simple geometrical transformation until the size of the simplex falls below a given tolerance.
Both types of algorithms use a standard framework. The user provides a high-level driver for the algorithms, and the library provides the individual functions necessary for each of the steps. There are three main phases of the iteration. The steps are,
Each iteration step consists either of an improvement to the line-minimisation
in the current direction or an update to the search direction itself. The
state for the minimizers is held in a GSL::MultiMin::FdfMinimizer
or
a GSL::MultiMin::FMinimizer
object.
Note that the minimization algorithms can only search for one local minimum at a time. When there are several local minima in the search area, the first minimum to be found will be returned; however it is difficult to predict which of the minima this will be. In most cases, no error will be reported if you try to find a local minimum in an area where there is more than one.
It is also important to note that the minimization algorithms find local minima; there is no way to determine whether a minimum is a global minimum of the function in question.
GSL::MultiMin::FdfMinimizer.alloc(type, n)
GSL::MultiMin::FMinimizer.alloc(type, n)
GSL::MultiMin::FdfMinimizer::CONJUGATE_FR
or "conjugate_fr"
GSL::MultiMin::FdfMinimizer::CONJUGATE_PR
or "conjugate_pr"
GSL::MultiMin::FdfMinimizer::VECTOR_BFGS
or "vector_bfgs"
GSL::MultiMin::FdfMinimizer::VECTOR_BFGS2
or "vector_bfgs2"
(GSL-1.9 or later)GSL::MultiMin::FdfMinimizer::STEEPEST_DESCENT
or "steepest_descent"
GSL::MultiMin::FMinimizer::NMSIMPLEX
or "nmsimplex"
ex:
include GSL::MultiMin m1 = FdfMinimizer.alloc(FdfMinimizer::CONJUGATE_FR, 2) m2 = FdfMinimizer.alloc("steepest_descent", 4) m3 = FMinimizer.alloc(FMinimizer::NMSIMPLEX, 3) m4 = FMinimizer.alloc("nmsimplex", 2)
GSL::MultiMin::FdfMinimizer#set(func, x, step_size, tol)
GSL::MultiMin::Function_fdf
class, see below) starting from
the initial point x (GSL::Vector
). The size of the first trial step is
given by step_size (Vector
). The accuracy of the line minimization is
specified by tol.GSL::MultiMin::FMinimizer#set(func, x, step_size)
GSL::MultiMin::FdfMinimizer#name
GSL::MultiMin::FMinimizer#name
You must provide a parametric function of n
variables for the minimizers to
operate on. You may also need to provide a routine which calculates the gradient of the
function. In order to allow for general parameters the functions are defined by the
classes, GSL::MultiMin::Function_fdf
and GSL::MultiMin::Function
.
GSL::MultiMin:Function_fdf.alloc(proc_f, proc_df, n)
GSL::MultiMin:Function_fdf.alloc(proc_f, proc_df, proc_fdf, n)
GSL::MultiMin:Function_fdf#set_procs(proc_f, proc_df)
GSL::MultiMin:Function_fdf#set_procs(proc_f, proc_df, n)
GSL::MultiMin:Function_fdf#set_procs(proc_f, proc_df, proc_fdf, n)
GSL::MultiMin:Function_fdf#set_params(params)
See example below.
include GSL::MultiMin my_f = Proc.new { |v, params| x = v[0]; y = v[1] p0 = params[0]; p1 = params[1] 10.0*(x - p0)*(x - p0) + 20.0*(y - p1)*(y - p1) + 30.0 } my_df = Proc.new { |v, params, df| x = v[0]; y = v[1] p0 = params[0]; p1 = params[1] df[0] = 20.0*(x-p0) df[1] = 40.0*(y-p1) } my_func = Function_fdf.alloc(my_f, my_df, 2) my_func.set_params([1.0, 2.0]) # parameters
GSL::MultiMin:Function.alloc(proc_f, n)
GSL::MultiMin:Function#set_proc(proc_f)
GSL::MultiMin:Function#set_proc(proc_f, n)
GSL::MultiMin:Function#set_params(params)
See example below.
include GSL::MultiMin np = 2 my_f = Proc.alloc { |v, params| x = v[0]; y = v[1] p0 = params[0]; p1 = params[1] 10.0*(x - p0)*(x - p0) + 20.0*(y - p1)*(y - p1) + 30.0 } my_func = Function.alloc(my_f, np) my_func.set_params([1.0, 2.0]) # parameters
GSL::MultiMin::FdfMinimizer#iterate
GSL::MultiMin::FMinimizer#iterate
GSL::MultiMin::FdfMinimizer#x
GSL::MultiMin::FdfMinimizer#minimum
GSL::MultiMin::FdfMinimizer#gradient
GSL::MultiMin::FMinimizer#x
GSL::MultiMin::FMinimizer#minimum
GSL::MultiMin::FMinimizer#size
GSL::MultiMin::FdfMinimizer#restart
A minimization procedure should stop when one of the following conditions is true:
The handling of these conditions is under user control. The methods below allow the user to test the precision of the current result.
GSL::MultiMin::FdfMinimizer#test_gradient(epsabs)
GSL::MultiMin::FdfMinimizer.test_gradient(g, epsabs)
These method test the norm of the gradient g against the absolute tolerance
epsabs. The gradient of a multidimensional function goes to zero at a minimum.
The tests return GSL::SUCCESS
if the following condition is achieved,
|g| < epsabs
and returns GSL::CONTINUE
otherwise. A suitable choice of epsabs can
be made from the desired accuracy in the function for small variations in x
.
The relationship between these quantities is given by \delta f = g \delta x
.
GSL::MultiMin::FdfMinimizer#test_size(epsabs)
GSL::MultiMin::FdfMinimizer.test_size(size, epsabs)
GSL::SUCCESS
if the size is smaller than tolerance,
otherwise GSL::CONTINUE
is returned.#!/usr/bin/env ruby require("gsl") include GSL::MultiMin my_f = Proc.new { |v, params| x = v[0]; y = v[1] p0 = params[0]; p1 = params[1] 10.0*(x - p0)*(x - p0) + 20.0*(y - p1)*(y - p1) + 30.0 } my_df = Proc.new { |v, params, df| x = v[0]; y = v[1] p0 = params[0]; p1 = params[1] df[0] = 20.0*(x-p0) df[1] = 40.0*(y-p1) } my_func = Function_fdf.alloc(my_f, my_df, 2) my_func.set_params([1.0, 2.0]) # parameters x = Vector.alloc(5.0, 7.0) # starting point minimizer = FdfMinimizer.alloc("conjugate_fr", 2) minimizer.set(my_func, x, 0.01, 1e-4) iter = 0 begin iter += 1 status = minimizer.iterate() status = minimizer.test_gradient(1e-3) if status == GSL::SUCCESS puts("Minimum found at") end x = minimizer.x f = minimizer.f printf("%5d %.5f %.5f %10.5f\n", iter, x[0], x[1], f) end while status == GSL::CONTINUE and iter < 100
#!/usr/bin/env ruby require("gsl") include GSL::MultiMin np = 2 my_f = Proc.new { |v, params| x = v[0]; y = v[1] p0 = params[0]; p1 = params[1] 10.0*(x - p0)*(x - p0) + 20.0*(y - p1)*(y - p1) + 30.0 } my_func = Function.alloc(my_f, np) my_func.set_params([1.0, 2.0]) # parameters x = Vector.alloc([5, 7]) ss = Vector.alloc(np) ss.set_all(1.0) minimizer = FMinimizer.alloc("nmsimplex", np) minimizer.set(my_func, x, ss) iter = 0 begin iter += 1 status = minimizer.iterate() status = minimizer.test_size(1e-2) if status == GSL::SUCCESS puts("converged to minimum at") end x = minimizer.x printf("%5d ", iter); for i in 0...np do printf("%10.3e ", x[i]) end printf("f() = %7.3f size = %.3f\n", minimizer.fval, minimizer.size); end while status == GSL::CONTINUE and iter < 100