GNU Scientific Library – Reference Manual: Providing a function to minimize

Next: Multimin Iteration, Previous: Initializing the Multidimensional Minimizer, Up: Multidimensional Minimization [Index]

### 37.4 Providing a function to minimize

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 and a third routine which calculates both the function value and the gradient together. In order to allow for general parameters the functions are defined by the following data types:

Data Type: gsl_multimin_function_fdf

  This data type defines a general function of n variables with parameters and the corresponding gradient vector of derivatives,

  `double (* f) (const gsl_vector * x, void * params)`

    this function should return the result f(x,params) for argument x and parameters params. If the function cannot be computed, an error value of `GSL_NAN` should be returned.

  `void (* df) (const gsl_vector * x, void * params, gsl_vector * g)`

    this function should store the n-dimensional gradient g_i = d f(x,params) / d x_i in the vector g for argument x and parameters params, returning an appropriate error code if the function cannot be computed.

  `void (* fdf) (const gsl_vector * x, void * params, double * f, gsl_vector * g)`

    This function should set the values of the f and g as above, for arguments x and parameters params. This function provides an optimization of the separate functions for f(x) and g(x)—it is always faster to compute the function and its derivative at the same time.

  `size_t n`

    the dimension of the system, i.e. the number of components of the vectors x.

  `void * params`

    a pointer to the parameters of the function.

Data Type: gsl_multimin_function

  This data type defines a general function of n variables with parameters,

  `double (* f) (const gsl_vector * x, void * params)`

    this function should return the result f(x,params) for argument x and parameters params. If the function cannot be computed, an error value of `GSL_NAN` should be returned.

  `size_t n`

    the dimension of the system, i.e. the number of components of the vectors x.

  `void * params`

    a pointer to the parameters of the function.

The following example function defines a simple two-dimensional paraboloid with five parameters,

    /* Paraboloid centered on (p[0],p[1]), with
       scale factors (p[2],p[3]) and minimum p[4] */

    double
    my_f (const gsl_vector *v, void *params)
    {
      double x, y;
      double *p = (double *)params;

      x = gsl_vector_get(v, 0);
      y = gsl_vector_get(v, 1);

      return p[2] * (x - p[0]) * (x - p[0]) +
               p[3] * (y - p[1]) * (y - p[1]) + p[4];
    }

    /* The gradient of f, df = (df/dx, df/dy). */
    void
    my_df (const gsl_vector *v, void *params,
           gsl_vector *df)
    {
      double x, y;
      double *p = (double *)params;

      x = gsl_vector_get(v, 0);
      y = gsl_vector_get(v, 1);

      gsl_vector_set(df, 0, 2.0 * p[2] * (x - p[0]));
      gsl_vector_set(df, 1, 2.0 * p[3] * (y - p[1]));
    }

    /* Compute both f and df together. */
    void
    my_fdf (const gsl_vector *x, void *params,
            double *f, gsl_vector *df)
    {
      *f = my_f(x, params);
      my_df(x, params, df);
    }

The function can be initialized using the following code,

    gsl_multimin_function_fdf my_func;

    /* Paraboloid center at (1,2), scale factors (10, 20),
       minimum value 30 */
    double p[5] = { 1.0, 2.0, 10.0, 20.0, 30.0 };

    my_func.n = 2;  /* number of function components */
    my_func.f = &my_f;
    my_func.df = &my_df;
    my_func.fdf = &my_fdf;
    my_func.params = (void *)p;

Next: Multimin Iteration, Previous: Initializing the Multidimensional Minimizer, Up: Multidimensional Minimization [Index]