  Let a control system and a target be given on an open subset of an Euclidean
space. The existence of a Control Lyapunov Function - namely a positive
definite, semiconcave, solution of the Hamilton-Jacobi inequality corresponding
to the control vector field -- guarantees Global Asymptotic Controllability
(GAC). In this case, however, minimization is not an issue. Instead, if a
Lagrangean with non-negative values is considered as well, an optimal control
problem can be defined in relation to the corresponding integral functional.
  In the first part of the present paper we show that the existence of a
Minimum Restraint Function -- a solution of a strict Hamilton-Jacobi inequality
involving the Lagrangian and a non-negative "savings multiplier" -- provides
not only global asymptotic controllability but also savings, namely a
state-dependent upper bound for the infima. This extends a former result, where
the control set was assumed to be compact. Here we allow unbounded controls and
replace inputs' values' compactness with a quite mild hypothesis concerning the
dependence of the data on inputs: such condition is met, for instance, by
control vector fields that are compositions of Lipschitz maps with polynomials
and exponentials of the control variable.
  In the second part of the paper we focus on the case when the dynamics is a
polynomial in the control variable. Through some analysis of convexity
properties of vector-valued polynomials' ranges, we prove some simplified
versions of the main result, in terms of either affine representability or
reduction to weak subsystems for the original dynamics.
