  This paper optimizes the step coefficients of first-order methods for smooth
convex minimization in terms of the worst-case convergence bound (i.e.,
efficiency) of the decrease in the gradient norm. This work is based on the
performance estimation problem approach. The worst-case gradient bound of the
resulting method is optimal up to a constant for large-dimensional smooth
convex minimization problems, under the initial bounded condition on the cost
function value. This paper then illustrates that the proposed method has a
computationally efficient form that is similar to the optimized gradient
method.
