COMPASS_SEARCH
The Compass Search Optimization Algorithm
COMPASS_SEARCH
is a Python library which
seeks the minimizer of a scalar function of several variables
using compass search, a direct search algorithm that does not use derivatives.
The algorithm, which goes back to Fermi and Metropolis, is easy to describe.
The algorithm begins with a starting point X, and a step size DELTA.
For each dimension I, the algorithm considers perturbing X(I) by adding
or subtracting DELTA.
If a perturbation is found which decreases the function, this becomes the
new X. Otherwise DELTA is halved.
The iteration halts when DELTA reaches a minimal value.
The algorithm is not guaranteed to find a global minimum. It can, for
instance, easily be attracted to a local minimum. Moreover, the algorithm
can diverge if, for instance, the function decreases as the argument goes
to infinity.
Usage:
[ x, fx, k ] = compass_search ( f, m, x,
delta_tol, delta, k_max )
where

f is the name of the function, of the form "def f ( m, x )",
returning a single real scalar value.

m is the number of variables.

x is an Mvector containing a starting point for the iteration.

delta_tol is the minimum allowed size of DELTA, and must be positive.

delta is the starting stepsize.

k_max is the maximum number of steps allowed. This is the only
way to avoid an infinite loop if the function decreases as it goes to infinity.

x is the program's estimate for the minimizer of the
function;

fx is the function value at x.

k is the number of steps taken.
Licensing:
The computer code and data files described and made available on this web page
are distributed under
the GNU LGPL license.
Languages:
COMPASS_SEARCH is available in
a C version and
a C++ version and
a FORTRAN77 version and
a FORTRAN90 version and
a MATLAB version and
a Python version.
Related Data and Programs:
ASA047,
a Python library which
minimizes a scalar function of several variables using the NelderMead algorithm.
TOMS178,
a Python library which
optimizes a scalar functional of multiple variables
using the HookeJeeves method, by Arthur Kaupe.
This is a version of ACM TOMS algorithm 178.
Author:
John Burkardt
Reference:

Tamara Kolda, Robert Michael Lewis, Virginia Torczon,
Optimization by Direct Search: New Perspectives on Some Classical
and Modern Methods,
SIAM Review,
Volume 45, Number 3, 2003, pages 385482.
Source Code:

beale.py,
defines Beale's function.

bohach1.py,
defines the Bohachevsky function #1.

bohach2.py,
defines the Bohachevsky function #2.

broyden.py,
defines Beale's function.

compass_search.py,
the coordinate search optimization algorithm.

extended_rosenbrock.py,
defines the extended Rosenbrock function.

goldstein_price.py,
defines the GoldsteinPrice polynomial.

himmelblau.py,
defines the Himmelblau function.

local.py,
defines the "local" function.

mckinnon.py,
defines the McKinnon function.

powell.py,
defines the Powell function.

r8vec_print.py,
prints an R8VEC.

rosenbrock.py,
defines the Rosenbrock function.

timestamp.py,
prints the YMDHMS date as a timestamp.
Examples and Tests:
You can go up one level to
the Python source codes.
Last revised on 23 January 2016.