test_optimization

test_optimization, an Octave code which defines test problems for the scalar function optimization problem.

The scalar function optimization problem is to find a value for the M-dimensional vector X which minimizes the value of the given scalar function F(X).

A special feature of this library is that all the functions can be defined for any dimension 1 <= M.

The functions defined include:

  1. The sphere model;
  2. The axis-parallel hyper-ellipsoid function;
  3. The rotated hyper-ellipsoid function;
  4. Rosenbrock's valley;
  5. Rastrigin's function;
  6. Schwefel's function;
  7. Griewank's function;
  8. The power sum function;
  9. Ackley's function;
  10. Michalewicz's function;
  11. The drop wave function;
  12. The deceptive function;

Licensing:

The computer code and data files described and made available on this web page are distributed under the MIT license

Languages:

test_optimization is available in a C version and a C++ version and a Fortran90 version and a MATLAB version and an Octave version.

Related Data and Programs:

test_optimization_test

asa047, an Octave code which minimizes a scalar function of several variables using the Nelder-Mead algorithm.

compass_search, an Octave code which seeks the minimizer of a scalar function of several variables using compass search, a direct search algorithm that does not use derivatives.

nelder_mead, an Octave code which minimizes a scalar function of several variables using the Nelder-Mead algorithm.

polynomials, an Octave code which defines multivariate polynomials over rectangular domains, for which certain information is to be determined, such as the maximum and minimum values.

praxis, an Octave code which minimizes a scalar function of several variables, without requiring derivative information, by richard brent.

test_opt, an Octave code which defines a number of problems for the scalar optimization problem.

test_opt_con, an Octave code which defines test problems for the minimization of a scalar function of several variables, with the search constrained to lie within a specified hyper-rectangle.

Reference:

  1. Marcin Molga, Czeslaw Smutnicki,
    Test functions for optimization needs.
  2. David Ackley,
    A connectionist machine for genetic hillclimbing,
    Springer, 1987,
    ISBN13: 978-0898382365,
    LC: Q336.A25.
  3. Hugues Bersini, Marco Dorigo, Stefan Langerman, Gregory Seront, Luca Gambardella,
    Results of the first international contest on evolutionary optimisation,
    In Proceedings of 1996 IEEE International Conference on Evolutionary Computation,
    IEEE Press, pages 611-615, 1996.
  4. Laurence Dixon, Gabor Szego,
    The optimization problem: An introduction,
    in Towards Global Optimisation,
    edited by Laurence Dixon, Gabor Szego,
    North-Holland, 1975,
    ISBN: 0444109552,
    LC: QA402.5.T7.
  5. Zbigniew Michalewicz,
    Genetic Algorithms + Data Structures = Evolution Programs,
    Third Edition,
    Springer, 1996,
    ISBN: 3-540-60676-9,
    LC: QA76.618.M53.
  6. Leonard Rastrigin,
    Extremal control systems,
    in Theoretical Foundations of Engineering Cybernetics Series,
    Moscow: Nauka, Russian, 1974.
  7. Howard Rosenbrock,
    An Automatic Method for Finding the Greatest or Least Value of a Function,
    Computer Journal,
    Volume 3, 1960, pages 175-184.
  8. Hans-Paul Schwefel,
    Numerical optimization of computer models,
    Wiley, 1981,
    ISBN13: 978-0471099888,
    LC: QA402.5.S3813.
  9. Bruno Shubert,
    A sequential method seeking the global maximum of a function,
    SIAM Journal on Numerical Analysis,
    Volume 9, pages 379-388, 1972.
  10. Aimo Toern, Antanas Zilinskas,
    Global Optimization,
    Lecture Notes in Computer Science, Number 350,
    Springer, 1989,
    ISBN13: 978-0387508719,
    LC: QA402.T685

Source Code:

Last revised on 07 June 2023.