%% model function:
  F = @ (x, p) p(1) * exp (p(2) * x);
  %% independents and dependents:
  x = 1:5;
  y = [1, 2, 4, 7, 14];
  %% initial values:
  init = [.25; .25];
  %% other configuration (default values):
  tolerance = .0001;
  max_iterations = 20;
  weights = ones (1, 5);
  dp = [.001; .001]; % bidirectional numeric gradient stepsize
  dFdp = "dfdp"; % function for gradient (numerical)

  %% linear constraints, A.' * parametervector + B >= 0
  A = [1; -1]; B = 0; % p(1) >= p(2);
  options.inequc = {A, B};

  %% start leasqr, be sure that 'verbose' is not set
  global verbose; verbose = false;
  [f, p, cvg, iter] = ...
      leasqr (x, y, init, F, tolerance, max_iterations, ...
          weights, dp, dFdp, options)