1. Apparatus for the delivery of insulin, the apparatus comprising: a) a glucose monitor adapted to measure respective glucose levels of a subject at discrete time intervals and provide respective glucose measurement data indicating each measured glucose level; b) an insulin infusion pump configured to deliver insulin in response to a delivery control signal; c) a memory configured to store a plurality of basal insulin delivery amounts at respective ones of the discrete time intervals; and d) a model predictive controller adapted to, for each of a plurality of the discrete time intervals: i) receive the glucose measurement data for that time interval from the glucose monitor; ii) determine an insulin delivery amount for that time interval using model predictive control based on a selected target glucose concentration range, the received glucose measurement data, the stored basal insulin delivery profile amounts for that time interval and n−1 successive time intervals; and iii) provide to the insulin infusion pump a delivery control signal corresponding to the determined insulin delivery amount, so that a corresponding amount of insulin is delivered by the infusion pump; e) in which the model predictive controller is adapted to determine the insulin delivery amount by mathematical minimization of a cost function that computes a cost metric correlated with physiological-fluid glucose-level excursions from a selected target glucose range for a particular set of n successive candidate insulin delivery amounts beginning from the selected one of the time intervals, and the model predictive controller, in order to carry out the mathematical minimization, is adapted to: i) select n candidate insulin delivery values for a real-type n-dimensional test point; ii) for each dimension i of the n dimensions: A) set a complex-type n-dimensional computation point c equal to the test point; B) set an imaginary part of element i of c equal to a nonzero increment; C) compute a complex-type value of the cost function at c; and D) divide the imaginary part of the complex-type computed function value by the increment to form element i of an approximate Jacobian of the function at the test point; iii) for each pair of dimensions (i,j), each i and j one of the n dimensions: A) set a multicomplex-type n-dimensional computation point b equal to the test point; B) set a first imaginary part of element i of b equal to a nonzero first increment; C) set a second imaginary part of element j of b equal to a nonzero second increment; D) compute a multicomplex-type value of the cost function at b; and E) divide the third imaginary part of the computed multicomplex-type function value by the product of the first and second increments to form element (i,j) of an approximate Hessian of the function at the test point; iv) solve a system of equations defined by the approximate Hessian and the approximate Jacobian to find a delta; v) modify the test point according to the delta to form a next point; vi) determine whether the next point satisfies selected convergence criteria; vii) if not, assign the value of the next point to the test point and repeat the computation of the approximate Jacobian and Hessian of the function, solution of the system of equations, modification of the test point, and determination of whether the next point satisfies the selected convergence criteria; and viii) if so, select the first element of the next point as the candidate insulin delivery amount for the selected one of the time intervals; f) and further in which the model predictive controller is adapted to, in order to compute the cost function for each input set of n successive candidate insulin delivery amounts: i) predict an excursion of the glucose level from a selected target glucose range using at least some of the glucose measurements, a glucose-insulin dynamic model of the subject, and an input set of n candidate insulin delivery amounts; ii) compute a deviation of the candidate insulin delivery amounts from respective selected basal delivery amounts; and iii) form a weighted sum of respective values representing the predicted excursion and the computed deviation using respective selected weights, such that the output of the cost function includes the weighted sum; and g) deliver, with the insulin infusion pump, the determined insulin delivery amount of insulin to the subject, as provided by the delivery control signal from the controller.