## 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 type:

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.
  `int (* 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.
  `int (* 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.
  `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 paraboloid with two parameters,

    /* Paraboloid centered on (dp[0],dp[1]) */

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

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

      return 10.0 * (x - dp[0]) * (x - dp[0]) +
               20.0 * (y - dp[1]) * (y - dp[1]) + 30.0;
    }

    /* 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 *dp = (double *)params;

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

      gsl_vector_set(df, 0, 20.0 * (x - dp[0]));
      gsl_vector_set(df, 1, 40.0 * (y - dp[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;

    double p[2] = { 1.0, 2.0 }; /* center at (1,2) */

    my_func.f = &my_f;
    my_func.df = &my_df;
    my_func.fdf = &my_fdf;
    my_func.n = 2;
    my_func.params = (void *)p;