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Minimization with Linear Equality Constraints   The objective
function is coded in the function brownfgh.m, where n = 1000,
such that Aeq·x = beq for Aeq that has 100 equations (so Aeq is
a 100-by-1000 matrix).    type brownfgh  Because brownfgh
computes the gradient and Hessian values as well as the
objective function, you need to use optimoptions to indicate
that this information is available in brownfgh, using the
GradObj and Hessian options.   load browneq   ans =   fun =
@brownfgh; load browneq          % Get Aeq and beq, the linear
equalities n = 1000; options =
optimoptions('fmincon','GradObj','on','Hessian','user-
supplied',...  fmincon prints the following exit message:  Local
minimum possible.  fmincon stopped because the final change in
function value relative to   The exitflag value of 3 also
indicates that the algorithm terminated because the change in
the objective function value was less than the tolerance TolFun.
The final function value is