Is there a way to maximize an objective function to find the unknowns in the function?

I need to find the unknowns, n and m by maximizing r using the equation at the bottom. I have a set of data containing the values of ?, d?/dt, and T but I am not sure how to sum up the y values into ?y as they contain n and m. Is there a way for matlab to solve it? Can someone kindly suggest a solution? Thank you!!

[Rena Berman]

(Answers Dev) Restored edit

Answer 1

[Walter Roberson]

function f = obj(nm, a, da, T)
  n = nm(1);
  m = nm(2);
  x = 1./T;
  y = ln(da ./ ((1-a).^n .* a.^m) );
  xy = x .* y;
  sum_x = sum(x);
  sum_y = sum(y);

 and so on.

Then you would be optimizing with

   a = ...  appropriate alpha values
   da = ... appropriate values for dalpha/dt
   T = .... appropriate values for T

   nm0 = randn(1, 2);   %starting point for search

   best_nm = fminsearch( @(nm) obj(nm, a, da, T), nm0 );

However, I suspect you might want to put some constraints on n and m; if so then you would be more likely to use fmincon()