  We present a procedure to numerically compute finite step worst case
performance guarantees on a given algorithm for the unconstrained optimization
of strongly convex functions with Lipschitz continuous gradients. The solution
method provided serves as an alternative approach to that derived by Taylor,
Hendrickx, and Glineur in [Math. Prog. 161 (1-2), 2017]. The difference lies in
the fact that our solution uses conditions for the interpolation of a set of
points and gradient evaluations by the gradient of a function in the class of
interest, whereas their solution uses conditions for the interpolation of a set
of points, gradient evaluations, and function evaluations by a function in the
class of interest. The motivation for this alternative solution is that, in
many cases, neither the algorithm nor the performance metric of interest rely
upon function evaluations. The primary development is a procedure to avoid
suffering from the factorial growth in the number of these conditions with the
size of the set to be interpolated when solving for the worst case performance.
