I'm using the scipy.optimize "minimize" function to solve a nonlinear optimization problem.
optimizationOutput = minimize(objective, x0, args=(n), method='SLSQP', bounds = bnds, constraints = cons, tol = 1, options={'disp': True ,'eps' : 1e5, "maxiter": 2000})
My input (x0) is an array containing 20 floats.
The first 19 inputs are initially estimated to be 10000, bounded by (0, None).
The last input is initially estimated to be 65, bounded by (60,70).
My problem has to do with the last input. The optimization algorithm seems to exclusively choose it's upper and lower bound, occasionally choosing values like 60.00001460018602. I know that this has to do with the 'eps' parameter of the minimize() function, but am wondering if there is a way to have a separate step size for this last input, as the first 19 inputs rely on the larger step size in order to find a global solution in a timely manner?