PRAXIS
Scalar Function Optimization
PRAXIS
is a Python library which
minimizes a scalar function of a vector argument,
without needing derivative information,
by Richard Brent.
PRAXIS seeks an Mdimensional point X which minimizes a
given scalar function F(X). The code is a refinement
of Powell's method of conjugate search directions. The user
does not need to supply the partial derivatives of the function
F(X). In fact, the function F(X) need not be
smoothly differentiable.
Licensing:
The computer code and data files described and made available on this
web page are distributed under
the GNU LGPL license.
Languages:
PRAXIS 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:
COMPASS_SEARCH,
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.
Reference:

Richard Brent,
Algorithms for Minimization without Derivatives,
Dover, 2002,
ISBN: 0486419983,
LC: QA402.5.B74.
Source Code:

beale.py,
the Beale function.

box.py,
the Box function.

chebyquad.py,
the Chebyquad function.

cube.py,
the Cube function.

helix.py,
the Helix function.

hilbert.py,
the Hilbert function.

powell3d.py,
the Powell3d function.

praxis.py,
seeks an Ndimensional minimizer X of a scalar function F(X).

r8_hypot.py,
returns sqrt ( x^2 + y^2 ) as an R8;

r8mat_print.py,
prints an R8MAT;

r8mat_print_some.py,
prints some of an R8MAT;

r8vec_print.py,
prints an R8VEC;

rosenbrock.py,
the Rosenbrock function.

singular.py,
the Singular function.

timestamp.py,
prints the current YMDHMS date as a timestamp;

tridiagonal.py,
the Tridiagonal function.

watson.py,
the Watson function.

wood.py,
the Wood function.
Examples and Tests:
You can go up one level to
the Python source codes.
Last revised on 04 August 2016.